yanhuihang Goto Github PK

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....

one-file-projects

oxford-3000-test

A Vue.js webapp to test your English vocabularies

prettytable

Display tabular data in a visually appealing ASCII table format

sa

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.

uploads

webpy.github.com

Github pages for webpy.org

weibored.js

Delete all weibo, if you get bored.

yanhuihang

yanhuihang.github.io

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