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Add a post about visualization word cloud
This commit is contained in:
Pengzhan Hao
@@ -0,0 +1,135 @@
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---
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layout: post
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title: "Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries"
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date: 2020-09-15 22:00:14 -0400
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categories: visualization
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---
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<img src="/static/2020-09/2020-06-28.png" height="350">
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## Background
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Recently, I set up a web-based RSS client for retrieving and organizing everyday news. I used [TinyTinyRSS](https://tt-rss.org/), or as ttrss, a popular RSS client which friendly to docker. Thanks to developer [HenryQW](https://ttrss.henry.wang/#about), a well-written Nginx-based docker configuration is already available in docker hub. With more feeds were added, I found some feeds does not need to be checked everyday. Thus I was thinking to create a script to automatically list all keywords appears in a last period and generate a heat map kind figure of it.
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Before you go further, I'll tell you all my settings to give readers a general overview.
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My first step is to read all text-based information from TTRSS's PostgreSQL database. With information, I used a Chinese-NLP library, [jieba](https://github.com/fxsjy/jieba), to extract all keyword with their occurrences frequency. By using [WordCloud](https://github.com/amueller/word_cloud), a python library, word cloud figure is generated and present. More details will be discussed in later sections.
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## Get RSS feeds' text
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My first thought is generating a keyword heat map for economy news of a last week. Since this blog post are more skewed to Chinese tokenization and draw the word cloud figure. I'll leave my code here just in case. The SQL connector I used is [psycopg2](https://pypi.org/project/psycopg2/), an easy-use PostgreSQL library.
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```python
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def __init__(self):
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self.dbe = psycopg2.connect(
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host=DB_HOST, port=DB_PORT, database=DB_NAME, user=DB_USER, password=DB_PASS)
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def get_1w_of_feed_byid(self, id=1) -> list:
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cur = self.dbe.cursor()
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cur.execute('SELECT content FROM public.ttrss_entries \
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where date_updated > now() - interval \'1 week\' AND id in ( \
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select int_id from DB_TABLE_NAME \
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where feed_id=' + str(id) + ' \
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) \
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ORDER BY id ASC '
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)
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rows = cur.fetchall()
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return rows
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```
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Most arguments are intuitive and easy to understand. The only exception is argument of function *get_1w_of_feed_byid*. This **id** is the feed index of my subscriptions.
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## Tokenize with frequency
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Two popular tokenization library were used, and I chose [jieba](https://github.com/fxsjy/jieba) after a few comparison. Before cutting the sentence, we first need to remove all punctuation marks.
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```python
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def remove_biaodian(text: str) -> str:
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punct = set(u''':!),.:;?]}¢'"、。〉》」』】〕〗〞︰︱︳﹐､﹒
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﹔﹕﹖﹗﹚﹜﹞！），．：；？｜｝︴︶︸︺︼︾﹀﹂﹄﹏､～￠
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々‖•·ˇˉ―--′’”([{£¥'"‵〈《「『【〔〖（［｛￡￥〝︵︷︹︻
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︽︿﹁﹃﹙﹛﹝（｛“‘-—_…''')
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ret = ""
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for x in text:
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if x in punct:
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ret += ''
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else:
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ret += x
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return ret
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```
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After we have an all characters string, we can call jieba. By using the function *jieba.posseg.cut* with or without paddle, we can have a word list and their "part of speech". As you can see in the following code, I also did two more works.
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First, in the if statement, I only kept all nouns with some categories. Category abbreviation such as "nr" and "ns" represent different "part of speech", I attached with categories I used in the following table. For more details you can find in this [link](https://github.com/fxsjy/jieba).
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The second work is only keeping words with length longer than 2 characters. In Chinese, there's no space between words such as Latin writing systems. Since then, some single-character-words such as conjunction words are easy to be misrecognized as specialty-noun. And this misrecognition will cause more single-character being regarded as specialty-noun. I am not able to improve NLP method, so I used a easy way to fix this by removing any words less than 2 characters.
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```python
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import jieba.posseg as pseg
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def get_noun_jieba(self, content: str) -> list:
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content = remove_biaodian(content)
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words = pseg.cut(content) # Invoking jieba.posseg.cut function
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ret = []
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for word, flag in words:
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# print(word, flag)
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if flag in ['nr', 'ns', 'nt', 'nw', 'nz', 'PER', 'ORG', 'x']: # LOC
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ret.append(word)
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return [remove_biaodian(i) for i in ret if i.strip() != "" and len(remove_biaodian(i.strip())) >= 2]
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```
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* Word category names and abbreviations
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| Abbreviation | Category name/ Part of speech |
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| ------------ | ----------------------------- |
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| nr | People name noun |
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| ns | Location name noun |
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| nt | Organization name noun |
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| nw | Arts work noun |
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| nz | Other noun |
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| PER | People name noun |
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| ORG | Location name noun |
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| x | Non-morpheme word |
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With all words extracted, we can easily calculate their frequencies. After this, we can using the following line of code to print a sorted result to verify correctness.
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```python
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noun = seg.get_noun_jieba(test_content)
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# ... Calculate frequency of above word list ...
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print(sorted(a_dict.items(), key=lambda x: x[1]))
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```
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## Draw word cloud
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With a keyword and frequency dictionary(data structure), we can just call built-in functions from wordcloud library to generate the figure.
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First we need to initialize an instance of wordcloud class. As you can see in my code, I set it with 6 parameters. Width and Height of the canvas, maximum amount of words used to generate the figure, the font of words, background color and margin between any two words.
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After having the instance, we call function *generate_from_frequencies* and pass keyword dictionary to it. The return value of this function is an bitmap image, which we can use [matplotlib](https://matplotlib.org/) to plot it to your screen.
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I tested my plot on ubuntu-subsystem on Windows 10, unfortunately matplotlib under subsystem depends on x11 window manager and its not default available on windows. We need to install an x11 manager to support. [Xming](https://sourceforge.net/projects/xming/) is the one I used.
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```python
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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font_path = "./font/haipai.ttf"
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output_path = "./font/out.png"
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def show_figure_with_frequency(keywords: dict):
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wc = WordCloud(width=828, height=1792, max_words=200, font_path=font_path,
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background_color="white", margin=1).generate_from_frequencies(keywords)
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plt.imshow(wc)
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plt.axis('off')
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plt.show()
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```
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If everything work fine, a word cloud figure will show up in a new window. My version looks like this.
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<img src="/static/2020-09/2020-06-28.png" height="150">
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This generated word cloud figure reflects the most popular economy news' keyword in the week started 06-28-2020. Two largest words in the figure are "新冠" and "新冠病毒", both means "Covid-19" (This figure was in the week of the second covid spur in Beijing, China). The size of the image fits my phone screen and I can use an app to automatic sync it to my phone's wallpaper. However, in this image, too many location nouns are presented. This will be something I can make progress on in the future.
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@@ -104,6 +104,8 @@
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<div class="col-box-title">Newest Posts</div>
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<ul class="post-list">
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<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
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<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
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<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
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@@ -202,6 +202,8 @@ Niagara Falls, NY, USA, 2017.</p>
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<div class="col-box-title">Newest Posts</div>
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<ul class="post-list">
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|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
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||||
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||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
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||||
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<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
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||||
|
||||
@@ -146,6 +146,8 @@ You also need to save charles Root Certificate, it also contains in the same men
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<div class="col-box-title">Newest Posts</div>
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<ul class="post-list">
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|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
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||||
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<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
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@@ -0,0 +1,306 @@
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<!DOCTYPE html>
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<html>
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<head>
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
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<title>Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries « Stop Talking, Start Doing - 停止空想，开始行动</title>
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<link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Tangerine">
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<link rel="alternate" type="application/rss+xml" title="Stop Talking, Start Doing - 停止空想，开始行动" href="https://codersherlock.github.com//feed.xml" />
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<body>
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<header class="header">
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<div class="wrapper">
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<a class="site-title" href="/">Stop Talking, Start Doing - 停止空想，开始行动</a>
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<nav class="site-nav">
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<a class="page-link" href="/about/">About Me</a>
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<a class="page-link" href="/category/">Category</a>
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<div class="post">
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<header class="post-header">
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<h1 class="post-title">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</h1>
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<p class="post-meta">Sep 15, 2020</p>
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</header>
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<article class="post-content">
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<p><img src="/static/2020-09/2020-06-28.png" height="350" /></p>
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<h2 id="background">Background</h2>
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<p>Recently, I set up a web-based RSS client for retrieving and organizing everyday news. I used <a href="https://tt-rss.org/">TinyTinyRSS</a>, or as ttrss, a popular RSS client which friendly to docker. Thanks to developer <a href="https://ttrss.henry.wang/#about">HenryQW</a>, a well-written Nginx-based docker configuration is already available in docker hub. With more feeds were added, I found some feeds does not need to be checked everyday. Thus I was thinking to create a script to automatically list all keywords appears in a last period and generate a heat map kind figure of it.</p>
|
||||
|
||||
<p>Before you go further, I’ll tell you all my settings to give readers a general overview.</p>
|
||||
|
||||
<p>My first step is to read all text-based information from TTRSS’s PostgreSQL database. With information, I used a Chinese-NLP library, <a href="https://github.com/fxsjy/jieba">jieba</a>, to extract all keyword with their occurrences frequency. By using <a href="https://github.com/amueller/word_cloud">WordCloud</a>, a python library, word cloud figure is generated and present. More details will be discussed in later sections.</p>
|
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|
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<h2 id="get-rss-feeds-text">Get RSS feeds’ text</h2>
|
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<p>My first thought is generating a keyword heat map for economy news of a last week. Since this blog post are more skewed to Chinese tokenization and draw the word cloud figure. I’ll leave my code here just in case. The SQL connector I used is <a href="https://pypi.org/project/psycopg2/">psycopg2</a>, an easy-use PostgreSQL library.</p>
|
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|
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<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="bp">self</span><span class="p">.</span><span class="n">dbe</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="p">.</span><span class="n">connect</span><span class="p">(</span>
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<span class="n">host</span><span class="o">=</span><span class="n">DB_HOST</span><span class="p">,</span> <span class="n">port</span><span class="o">=</span><span class="n">DB_PORT</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="n">DB_NAME</span><span class="p">,</span> <span class="n">user</span><span class="o">=</span><span class="n">DB_USER</span><span class="p">,</span> <span class="n">password</span><span class="o">=</span><span class="n">DB_PASS</span><span class="p">)</span>
|
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|
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<span class="k">def</span> <span class="nf">get_1w_of_feed_byid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
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<span class="n">cur</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">dbe</span><span class="p">.</span><span class="n">cursor</span><span class="p">()</span>
|
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<span class="n">cur</span><span class="p">.</span><span class="n">execute</span><span class="p">(</span><span class="s">'SELECT content FROM public.ttrss_entries </span><span class="se">\
|
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</span><span class="s"> where date_updated > now() - interval </span><span class="se">\'</span><span class="s">1 week</span><span class="se">\'</span><span class="s"> AND id in ( </span><span class="se">\
|
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</span><span class="s"> select int_id from DB_TABLE_NAME </span><span class="se">\
|
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</span><span class="s"> where feed_id='</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">)</span> <span class="o">+</span> <span class="s">' </span><span class="se">\
|
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</span><span class="s"> ) </span><span class="se">\
|
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</span><span class="s"> ORDER BY id ASC '</span>
|
||||
<span class="p">)</span>
|
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<span class="n">rows</span> <span class="o">=</span> <span class="n">cur</span><span class="p">.</span><span class="n">fetchall</span><span class="p">()</span>
|
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<span class="k">return</span> <span class="n">rows</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<p>Most arguments are intuitive and easy to understand. The only exception is argument of function <em>get_1w_of_feed_byid</em>. This <strong>id</strong> is the feed index of my subscriptions.</p>
|
||||
|
||||
<h2 id="tokenize-with-frequency">Tokenize with frequency</h2>
|
||||
|
||||
<p>Two popular tokenization library were used, and I chose <a href="https://github.com/fxsjy/jieba">jieba</a> after a few comparison. Before cutting the sentence, we first need to remove all punctuation marks.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">remove_biaodian</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">str</span><span class="p">:</span>
|
||||
<span class="n">punct</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="s">u''':!),.:;?]}¢'"、。〉》」』】〕〗〞︰︱︳﹐､﹒
|
||||
﹔﹕﹖﹗﹚﹜﹞！），．：；？｜｝︴︶︸︺︼︾﹀﹂﹄﹏､～￠
|
||||
々‖•·ˇˉ―--′’”([{£¥'"‵〈《「『【〔〖（［｛￡￥〝︵︷︹︻
|
||||
︽︿﹁﹃﹙﹛﹝（｛“‘-—_…'''</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="s">""</span>
|
||||
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">text</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">punct</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">+=</span> <span class="s">''</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">+=</span> <span class="n">x</span>
|
||||
<span class="k">return</span> <span class="n">ret</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<p>After we have an all characters string, we can call jieba. By using the function <em>jieba.posseg.cut</em> with or without paddle, we can have a word list and their “part of speech”. As you can see in the following code, I also did two more works.</p>
|
||||
|
||||
<p>First, in the if statement, I only kept all nouns with some categories. Category abbreviation such as “nr” and “ns” represent different “part of speech”, I attached with categories I used in the following table. For more details you can find in this <a href="https://github.com/fxsjy/jieba">link</a>.</p>
|
||||
|
||||
<p>The second work is only keeping words with length longer than 2 characters. In Chinese, there’s no space between words such as Latin writing systems. Since then, some single-character-words such as conjunction words are easy to be misrecognized as specialty-noun. And this misrecognition will cause more single-character being regarded as specialty-noun. I am not able to improve NLP method, so I used a easy way to fix this by removing any words less than 2 characters.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">jieba.posseg</span> <span class="k">as</span> <span class="n">pseg</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_noun_jieba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">content</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="n">content</span> <span class="o">=</span> <span class="n">remove_biaodian</span><span class="p">(</span><span class="n">content</span><span class="p">)</span>
|
||||
<span class="n">words</span> <span class="o">=</span> <span class="n">pseg</span><span class="p">.</span><span class="n">cut</span><span class="p">(</span><span class="n">content</span><span class="p">)</span> <span class="c1"># Invoking jieba.posseg.cut function
|
||||
</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">word</span><span class="p">,</span> <span class="n">flag</span> <span class="ow">in</span> <span class="n">words</span><span class="p">:</span>
|
||||
<span class="c1"># print(word, flag)
|
||||
</span> <span class="k">if</span> <span class="n">flag</span> <span class="ow">in</span> <span class="p">[</span><span class="s">'nr'</span><span class="p">,</span> <span class="s">'ns'</span><span class="p">,</span> <span class="s">'nt'</span><span class="p">,</span> <span class="s">'nw'</span><span class="p">,</span> <span class="s">'nz'</span><span class="p">,</span> <span class="s">'PER'</span><span class="p">,</span> <span class="s">'ORG'</span><span class="p">,</span> <span class="s">'x'</span><span class="p">]:</span> <span class="c1"># LOC
|
||||
</span> <span class="n">ret</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">remove_biaodian</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ret</span> <span class="k">if</span> <span class="n">i</span><span class="p">.</span><span class="n">strip</span><span class="p">()</span> <span class="o">!=</span> <span class="s">""</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">remove_biaodian</span><span class="p">(</span><span class="n">i</span><span class="p">.</span><span class="n">strip</span><span class="p">()))</span> <span class="o">>=</span> <span class="mi">2</span><span class="p">]</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<ul>
|
||||
<li>Word category names and abbreviations</li>
|
||||
</ul>
|
||||
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Abbreviation</th>
|
||||
<th>Category name/ Part of speech</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>nr</td>
|
||||
<td>People name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>ns</td>
|
||||
<td>Location name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>nt</td>
|
||||
<td>Organization name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>nw</td>
|
||||
<td>Arts work noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>nz</td>
|
||||
<td>Other noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>PER</td>
|
||||
<td>People name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>ORG</td>
|
||||
<td>Location name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>x</td>
|
||||
<td>Non-morpheme word</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
<p>With all words extracted, we can easily calculate their frequencies. After this, we can using the following line of code to print a sorted result to verify correctness.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">noun</span> <span class="o">=</span> <span class="n">seg</span><span class="p">.</span><span class="n">get_noun_jieba</span><span class="p">(</span><span class="n">test_content</span><span class="p">)</span>
|
||||
<span class="c1"># ... Calculate frequency of above word list ...
|
||||
</span><span class="k">print</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">a_dict</span><span class="p">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<h2 id="draw-word-cloud">Draw word cloud</h2>
|
||||
|
||||
<p>With a keyword and frequency dictionary(data structure), we can just call built-in functions from wordcloud library to generate the figure.</p>
|
||||
|
||||
<p>First we need to initialize an instance of wordcloud class. As you can see in my code, I set it with 6 parameters. Width and Height of the canvas, maximum amount of words used to generate the figure, the font of words, background color and margin between any two words.</p>
|
||||
|
||||
<p>After having the instance, we call function <em>generate_from_frequencies</em> and pass keyword dictionary to it. The return value of this function is an bitmap image, which we can use <a href="https://matplotlib.org/">matplotlib</a> to plot it to your screen.</p>
|
||||
|
||||
<p>I tested my plot on ubuntu-subsystem on Windows 10, unfortunately matplotlib under subsystem depends on x11 window manager and its not default available on windows. We need to install an x11 manager to support. <a href="https://sourceforge.net/projects/xming/">Xming</a> is the one I used.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">wordcloud</span> <span class="kn">import</span> <span class="n">WordCloud</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
|
||||
|
||||
<span class="n">font_path</span> <span class="o">=</span> <span class="s">"./font/haipai.ttf"</span>
|
||||
<span class="n">output_path</span> <span class="o">=</span> <span class="s">"./font/out.png"</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">show_figure_with_frequency</span><span class="p">(</span><span class="n">keywords</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
<span class="n">wc</span> <span class="o">=</span> <span class="n">WordCloud</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">828</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">1792</span><span class="p">,</span> <span class="n">max_words</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">font_path</span><span class="o">=</span><span class="n">font_path</span><span class="p">,</span>
|
||||
<span class="n">background_color</span><span class="o">=</span><span class="s">"white"</span><span class="p">,</span> <span class="n">margin</span><span class="o">=</span><span class="mi">1</span><span class="p">).</span><span class="n">generate_from_frequencies</span><span class="p">(</span><span class="n">keywords</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="p">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">wc</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="p">.</span><span class="n">axis</span><span class="p">(</span><span class="s">'off'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<p>If everything work fine, a word cloud figure will show up in a new window. My version looks like this.</p>
|
||||
|
||||
<p><img src="/static/2020-09/2020-06-28.png" height="150" /></p>
|
||||
|
||||
<p>This generated word cloud figure reflects the most popular economy news’ keyword in the week started 06-28-2020. Two largest words in the figure are “新冠” and “新冠病毒”, both means “Covid-19” (This figure was in the week of the second covid spur in Beijing, China). The size of the image fits my phone screen and I can use an app to automatic sync it to my phone’s wallpaper. However, in this image, too many location nouns are presented. This will be something I can make progress on in the future.</p>
|
||||
|
||||
|
||||
</article>
|
||||
|
||||
|
||||
|
||||
<div class="post-comments">
|
||||
<div id="disqus_thread"></div>
|
||||
<script type="text/javascript">
|
||||
var disqus_shortname = 'codersherlockblog'; // required: replace example with your forum shortname
|
||||
(function() {
|
||||
var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
|
||||
dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';
|
||||
(document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);
|
||||
})();
|
||||
</script>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
<div class="col-second">
|
||||
<div class="col-box col-box-author">
|
||||
<img class="avatar" src="/static/avatar.jpg" alt="Pengzhan Hao">
|
||||
<div class="col-box-title name">Pengzhan Hao</div>
|
||||
<p></p>
|
||||
<p class="contact">
|
||||
|
||||
<a href="https://github.com/codersherlock">GitHub</a>
|
||||
|
||||
|
||||
|
||||
<a href="mailto:haopengzhan@gmail.com">Email</a>
|
||||
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div class="col-box">
|
||||
<div class="col-box-title">Newest Posts</div>
|
||||
<ul class="post-list">
|
||||
|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/charles-is-not-a-good-tool">Using charles proxy to monitor mobile SSL traffics</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/hello">Stop Talking is the worst title of one blog</a></li>
|
||||
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
<div class="col-box post-toc hide">
|
||||
<div class="col-box-title">TOC</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<footer class="footer">
|
||||
<div class="wrapper">
|
||||
© 2016 Pengzhan Hao
|
||||
</div>
|
||||
</footer>
|
||||
|
||||
<script src="/js/easybook.js"></script>
|
||||
|
||||
</body>
|
||||
|
||||
</html>
|
||||
@@ -118,6 +118,8 @@
|
||||
<div class="col-box-title">Newest Posts</div>
|
||||
<ul class="post-list">
|
||||
|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
|
||||
|
||||
@@ -135,6 +135,8 @@
|
||||
<div class="col-box-title">Newest Posts</div>
|
||||
<ul class="post-list">
|
||||
|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
|
||||
|
||||
@@ -227,6 +227,8 @@ su
|
||||
<div class="col-box-title">Newest Posts</div>
|
||||
<ul class="post-list">
|
||||
|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
|
||||
|
||||
@@ -100,6 +100,14 @@
|
||||
</ul>
|
||||
|
||||
|
||||
<h2 class="category" id="visualization">VISUALIZATION</h2>
|
||||
<ul>
|
||||
|
||||
<li><span>Sep 15</span> » <a href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
</ul>
|
||||
|
||||
|
||||
<h2 class="category" id="xv6">XV6</h2>
|
||||
<ul>
|
||||
|
||||
@@ -133,6 +141,8 @@
|
||||
<div class="col-box-title">Newest Posts</div>
|
||||
<ul class="post-list">
|
||||
|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
|
||||
|
||||
+170
-2
@@ -6,10 +6,178 @@
|
||||
</description>
|
||||
<link>https://codersherlock.github.com//</link>
|
||||
<atom:link href="https://codersherlock.github.com//feed.xml" rel="self" type="application/rss+xml"/>
|
||||
<pubDate>Tue, 15 Sep 2020 19:43:12 -0400</pubDate>
|
||||
<lastBuildDate>Tue, 15 Sep 2020 19:43:12 -0400</lastBuildDate>
|
||||
<pubDate>Tue, 15 Sep 2020 22:22:06 -0400</pubDate>
|
||||
<lastBuildDate>Tue, 15 Sep 2020 22:22:06 -0400</lastBuildDate>
|
||||
<generator>Jekyll v4.1.1</generator>
|
||||
|
||||
<item>
|
||||
<title>Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</title>
|
||||
<description><p><img src="/static/2020-09/2020-06-28.png" height="350" /></p>
|
||||
|
||||
<h2 id="background">Background</h2>
|
||||
|
||||
<p>Recently, I set up a web-based RSS client for retrieving and organizing everyday news. I used <a href="https://tt-rss.org/">TinyTinyRSS</a>, or as ttrss, a popular RSS client which friendly to docker. Thanks to developer <a href="https://ttrss.henry.wang/#about">HenryQW</a>, a well-written Nginx-based docker configuration is already available in docker hub. With more feeds were added, I found some feeds does not need to be checked everyday. Thus I was thinking to create a script to automatically list all keywords appears in a last period and generate a heat map kind figure of it.</p>
|
||||
|
||||
<p>Before you go further, I’ll tell you all my settings to give readers a general overview.</p>
|
||||
|
||||
<p>My first step is to read all text-based information from TTRSS’s PostgreSQL database. With information, I used a Chinese-NLP library, <a href="https://github.com/fxsjy/jieba">jieba</a>, to extract all keyword with their occurrences frequency. By using <a href="https://github.com/amueller/word_cloud">WordCloud</a>, a python library, word cloud figure is generated and present. More details will be discussed in later sections.</p>
|
||||
|
||||
<h2 id="get-rss-feeds-text">Get RSS feeds’ text</h2>
|
||||
|
||||
<p>My first thought is generating a keyword heat map for economy news of a last week. Since this blog post are more skewed to Chinese tokenization and draw the word cloud figure. I’ll leave my code here just in case. The SQL connector I used is <a href="https://pypi.org/project/psycopg2/">psycopg2</a>, an easy-use PostgreSQL library.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="p">.</span><span class="n">dbe</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="p">.</span><span class="n">connect</span><span class="p">(</span>
|
||||
<span class="n">host</span><span class="o">=</span><span class="n">DB_HOST</span><span class="p">,</span> <span class="n">port</span><span class="o">=</span><span class="n">DB_PORT</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="n">DB_NAME</span><span class="p">,</span> <span class="n">user</span><span class="o">=</span><span class="n">DB_USER</span><span class="p">,</span> <span class="n">password</span><span class="o">=</span><span class="n">DB_PASS</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_1w_of_feed_byid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="n">cur</span> <span class="o">=</span> <span class="bp">self</span><span class="p">.</span><span class="n">dbe</span><span class="p">.</span><span class="n">cursor</span><span class="p">()</span>
|
||||
<span class="n">cur</span><span class="p">.</span><span class="n">execute</span><span class="p">(</span><span class="s">'SELECT content FROM public.ttrss_entries </span><span class="se">\
|
||||
</span><span class="s"> where date_updated &gt; now() - interval </span><span class="se">\'</span><span class="s">1 week</span><span class="se">\'</span><span class="s"> AND id in ( </span><span class="se">\
|
||||
</span><span class="s"> select int_id from DB_TABLE_NAME </span><span class="se">\
|
||||
</span><span class="s"> where feed_id='</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">id</span><span class="p">)</span> <span class="o">+</span> <span class="s">' </span><span class="se">\
|
||||
</span><span class="s"> ) </span><span class="se">\
|
||||
</span><span class="s"> ORDER BY id ASC '</span>
|
||||
<span class="p">)</span>
|
||||
<span class="n">rows</span> <span class="o">=</span> <span class="n">cur</span><span class="p">.</span><span class="n">fetchall</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">rows</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<p>Most arguments are intuitive and easy to understand. The only exception is argument of function <em>get_1w_of_feed_byid</em>. This <strong>id</strong> is the feed index of my subscriptions.</p>
|
||||
|
||||
<h2 id="tokenize-with-frequency">Tokenize with frequency</h2>
|
||||
|
||||
<p>Two popular tokenization library were used, and I chose <a href="https://github.com/fxsjy/jieba">jieba</a> after a few comparison. Before cutting the sentence, we first need to remove all punctuation marks.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="k">def</span> <span class="nf">remove_biaodian</span><span class="p">(</span><span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
|
||||
<span class="n">punct</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="s">u''':!),.:;?]}¢'"、。〉》」』】〕〗〞︰︱︳﹐､﹒
|
||||
﹔﹕﹖﹗﹚﹜﹞！），．：；？｜｝︴︶︸︺︼︾﹀﹂﹄﹏､～￠
|
||||
々‖•·ˇˉ―--′’”([{£¥'"‵〈《「『【〔〖（［｛￡￥〝︵︷︹︻
|
||||
︽︿﹁﹃﹙﹛﹝（｛“‘-—_…'''</span><span class="p">)</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="s">""</span>
|
||||
<span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">text</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">punct</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">+=</span> <span class="s">''</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">ret</span> <span class="o">+=</span> <span class="n">x</span>
|
||||
<span class="k">return</span> <span class="n">ret</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<p>After we have an all characters string, we can call jieba. By using the function <em>jieba.posseg.cut</em> with or without paddle, we can have a word list and their “part of speech”. As you can see in the following code, I also did two more works.</p>
|
||||
|
||||
<p>First, in the if statement, I only kept all nouns with some categories. Category abbreviation such as “nr” and “ns” represent different “part of speech”, I attached with categories I used in the following table. For more details you can find in this <a href="https://github.com/fxsjy/jieba">link</a>.</p>
|
||||
|
||||
<p>The second work is only keeping words with length longer than 2 characters. In Chinese, there’s no space between words such as Latin writing systems. Since then, some single-character-words such as conjunction words are easy to be misrecognized as specialty-noun. And this misrecognition will cause more single-character being regarded as specialty-noun. I am not able to improve NLP method, so I used a easy way to fix this by removing any words less than 2 characters.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">import</span> <span class="nn">jieba.posseg</span> <span class="k">as</span> <span class="n">pseg</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_noun_jieba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">content</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
|
||||
<span class="n">content</span> <span class="o">=</span> <span class="n">remove_biaodian</span><span class="p">(</span><span class="n">content</span><span class="p">)</span>
|
||||
<span class="n">words</span> <span class="o">=</span> <span class="n">pseg</span><span class="p">.</span><span class="n">cut</span><span class="p">(</span><span class="n">content</span><span class="p">)</span> <span class="c1"># Invoking jieba.posseg.cut function
|
||||
</span>
|
||||
<span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">word</span><span class="p">,</span> <span class="n">flag</span> <span class="ow">in</span> <span class="n">words</span><span class="p">:</span>
|
||||
<span class="c1"># print(word, flag)
|
||||
</span> <span class="k">if</span> <span class="n">flag</span> <span class="ow">in</span> <span class="p">[</span><span class="s">'nr'</span><span class="p">,</span> <span class="s">'ns'</span><span class="p">,</span> <span class="s">'nt'</span><span class="p">,</span> <span class="s">'nw'</span><span class="p">,</span> <span class="s">'nz'</span><span class="p">,</span> <span class="s">'PER'</span><span class="p">,</span> <span class="s">'ORG'</span><span class="p">,</span> <span class="s">'x'</span><span class="p">]:</span> <span class="c1"># LOC
|
||||
</span> <span class="n">ret</span><span class="p">.</span><span class="n">append</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="n">remove_biaodian</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ret</span> <span class="k">if</span> <span class="n">i</span><span class="p">.</span><span class="n">strip</span><span class="p">()</span> <span class="o">!=</span> <span class="s">""</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">remove_biaodian</span><span class="p">(</span><span class="n">i</span><span class="p">.</span><span class="n">strip</span><span class="p">()))</span> <span class="o">&gt;=</span> <span class="mi">2</span><span class="p">]</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<ul>
|
||||
<li>Word category names and abbreviations</li>
|
||||
</ul>
|
||||
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Abbreviation</th>
|
||||
<th>Category name/ Part of speech</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td>nr</td>
|
||||
<td>People name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>ns</td>
|
||||
<td>Location name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>nt</td>
|
||||
<td>Organization name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>nw</td>
|
||||
<td>Arts work noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>nz</td>
|
||||
<td>Other noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>PER</td>
|
||||
<td>People name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>ORG</td>
|
||||
<td>Location name noun</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>x</td>
|
||||
<td>Non-morpheme word</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
<p>With all words extracted, we can easily calculate their frequencies. After this, we can using the following line of code to print a sorted result to verify correctness.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="n">noun</span> <span class="o">=</span> <span class="n">seg</span><span class="p">.</span><span class="n">get_noun_jieba</span><span class="p">(</span><span class="n">test_content</span><span class="p">)</span>
|
||||
<span class="c1"># ... Calculate frequency of above word list ...
|
||||
</span><span class="k">print</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">a_dict</span><span class="p">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<h2 id="draw-word-cloud">Draw word cloud</h2>
|
||||
|
||||
<p>With a keyword and frequency dictionary(data structure), we can just call built-in functions from wordcloud library to generate the figure.</p>
|
||||
|
||||
<p>First we need to initialize an instance of wordcloud class. As you can see in my code, I set it with 6 parameters. Width and Height of the canvas, maximum amount of words used to generate the figure, the font of words, background color and margin between any two words.</p>
|
||||
|
||||
<p>After having the instance, we call function <em>generate_from_frequencies</em> and pass keyword dictionary to it. The return value of this function is an bitmap image, which we can use <a href="https://matplotlib.org/">matplotlib</a> to plot it to your screen.</p>
|
||||
|
||||
<p>I tested my plot on ubuntu-subsystem on Windows 10, unfortunately matplotlib under subsystem depends on x11 window manager and its not default available on windows. We need to install an x11 manager to support. <a href="https://sourceforge.net/projects/xming/">Xming</a> is the one I used.</p>
|
||||
|
||||
<div class="language-python highlighter-rouge"><div class="highlight"><pre class="highlight"><code><span class="kn">from</span> <span class="nn">wordcloud</span> <span class="kn">import</span> <span class="n">WordCloud</span>
|
||||
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="n">plt</span>
|
||||
|
||||
<span class="n">font_path</span> <span class="o">=</span> <span class="s">"./font/haipai.ttf"</span>
|
||||
<span class="n">output_path</span> <span class="o">=</span> <span class="s">"./font/out.png"</span>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">show_figure_with_frequency</span><span class="p">(</span><span class="n">keywords</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
|
||||
<span class="n">wc</span> <span class="o">=</span> <span class="n">WordCloud</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">828</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="mi">1792</span><span class="p">,</span> <span class="n">max_words</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">font_path</span><span class="o">=</span><span class="n">font_path</span><span class="p">,</span>
|
||||
<span class="n">background_color</span><span class="o">=</span><span class="s">"white"</span><span class="p">,</span> <span class="n">margin</span><span class="o">=</span><span class="mi">1</span><span class="p">).</span><span class="n">generate_from_frequencies</span><span class="p">(</span><span class="n">keywords</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="p">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">wc</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="p">.</span><span class="n">axis</span><span class="p">(</span><span class="s">'off'</span><span class="p">)</span>
|
||||
<span class="n">plt</span><span class="p">.</span><span class="n">show</span><span class="p">()</span>
|
||||
</code></pre></div></div>
|
||||
|
||||
<p>If everything work fine, a word cloud figure will show up in a new window. My version looks like this.</p>
|
||||
|
||||
<p><img src="/static/2020-09/2020-06-28.png" height="150" /></p>
|
||||
|
||||
<p>This generated word cloud figure reflects the most popular economy news’ keyword in the week started 06-28-2020. Two largest words in the figure are “新冠” and “新冠病毒”, both means “Covid-19” (This figure was in the week of the second covid spur in Beijing, China). The size of the image fits my phone screen and I can use an app to automatic sync it to my phone’s wallpaper. However, in this image, too many location nouns are presented. This will be something I can make progress on in the future.</p>
|
||||
|
||||
</description>
|
||||
<pubDate>Tue, 15 Sep 2020 22:00:14 -0400</pubDate>
|
||||
<link>https://codersherlock.github.com//archivers/generate-word-cloud-with-chinese-fenci</link>
|
||||
<guid isPermaLink="true">https://codersherlock.github.com//archivers/generate-word-cloud-with-chinese-fenci</guid>
|
||||
|
||||
|
||||
<category>visualization</category>
|
||||
|
||||
</item>
|
||||
|
||||
<item>
|
||||
<title>Xv6 introduction</title>
|
||||
<description><p>I hate xv6, a stupid, useless education-oriented system. In this article, I will generally talk about how to implement system call to this operating system.</p>
|
||||
|
||||
@@ -73,6 +73,22 @@
|
||||
|
||||
<ul class="post-list">
|
||||
|
||||
<li>
|
||||
<h2>
|
||||
<a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a>
|
||||
</h2>
|
||||
|
||||
<div class="post-meta">Sep 15, 2020</div>
|
||||
|
||||
<div class="post-excerpt">
|
||||
<p><img src="/static/2020-09/2020-06-28.png" height="350" /></p>
|
||||
|
||||
<p>
|
||||
<a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Read More »</a>
|
||||
</p>
|
||||
</div>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<h2>
|
||||
<a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a>
|
||||
@@ -174,6 +190,8 @@ My current solution is using AP to forward all SSL traffic to a proxy, <a href="
|
||||
<div class="col-box-title">Newest Posts</div>
|
||||
<ul class="post-list">
|
||||
|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
|
||||
|
||||
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|
After Width: | Height: | Size: 428 KiB |
@@ -208,6 +208,8 @@
|
||||
<div class="col-box-title">Newest Posts</div>
|
||||
<ul class="post-list">
|
||||
|
||||
<li><a class="post-link" href="/archivers/generate-word-cloud-with-chinese-fenci">Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/intro-xv6">Xv6 introduction</a></li>
|
||||
|
||||
<li><a class="post-link" href="/archivers/some-of-my-previews-exper-work">Some of my previews experiment works: 2016</a></li>
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 428 KiB |
Reference in New Issue
Block a user