YAN Hui Hang's repositories
AnnualCodeReport
模仿网易云音乐的年度代码报告
berkeleyparser
Automatically exported from code.google.com/p/berkeleyparser
bilibili-helper-o
哔哩哔哩 (bilibili.com) 辅助工具，可以替换播放器、推送通知并进行一些快捷操作
chinese-independent-developer
👩🏿💻👨🏾💻👩🏼💻👨🏽💻👩🏻💻**独立开发者项目列表 -- 分享大家都在做什么
Classical-Chinese
古文现代文翻译平行语料库
English-Vocabulary-Word-List
Common English Vocabulary Word List
english-words
:memo: A text file containing 479k English words for all your dictionary/word-based projects e.g: auto-completion / autosuggestion
ezcs
My On-class Works on the CS Course, GZEZ
filetype.py
Small, dependency-free, fast Python package to infer binary file types checking the magic numbers signature
Gdufszhushou
广外校园助手
gender
Predict Gender from Names Using Historical Data
grappa
Behavior-oriented, expressive, human-friendly Python assertion library for the 21st century
learnxinyminutes-docs
Code documentation written as code! How novel and totally my idea!
luxun
鲁迅全集整理：小说，杂文，书信，评论等等....
oxford-3000-test
A Vue.js webapp to test your English vocabularies
prettytable
Display tabular data in a visually appealing ASCII table format
Sentiment-Analysis-on-Barak-Obama-Tweets
To answer the question "Which category of emotion is most frequent for Barak Obama?" I've done sentiment Analysis on Barak Obama's Tweet.I've categorised Tweets into 3 catergory as Positive, Negative and Neutral. To categorized the tweets I followed below steps.<BR> 1. Tweet is a Positive Tweet if number of Positive words in a Tweet is greater than number of Negative words.<BR> 2. Tweet is a Negative tweet if Negative words are greater than Positive words. <BR> 3. If number of Positive and negative words are equal in a tweet then its a Neutral Tweet.<BR> To do this I build a vocabulary of Positive and negative word list from Datasets provided below. 1. Positive Words Dataset: https://gist.github.com/mkulakowski2/4289437 2. Negative Words Dataset: https://gist.github.com/mkulakowski2/4289441count 3. Twitter Sentiment Analysis Dataset: http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/ Results: Positive Tweets are highest with count of 24331. To identify the Hypothesis: "Most of Barak Obama's tweets will be regarding healthcare". I scraped the health related words from "http://www.english-for-students.com/Health-Vocabulary.html" and created healthWordList. And considered a tweet as a healtcare related tweet if atleast one word in a tweet is also is healthWordList. Results: The Hypothesis is false as only 6768 Tweets out of 27346 overall tweets are related to Health care. After completing this I checked the accuracy of categorised tweets using Naive Bayes Algorithm. I trained the model with Sentiment Analysis Dataset with different tweets and checked the accuracy of my categorization of tweets.
three.js
JavaScript 3D library.
webpy.github.com
Github pages for webpy.org
Weibored.js
Delete all weibo, if you get bored.
Youtube-Auto-Subtitle-Download
:coffee: Download Youtube Subtitle (Still work in 2020!) (Work best on Chrome + Tampermonkey) **Looking for maintainer** 2020-10-7 更新：支持中英双语字幕下载，请在页面底部提供的另一个链接进行安装