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stock-volatility-google-trends

Deep Learning Stock Volatility with Google Domestic Trends: https://arxiv.org/pdf/1512.04916.pdf

stock_market_lexicon

Stock Market Lexicon

stormtweetssentimentanalysis

Computes sentiment analysis of tweets of US States in real-time using Storm.

tabula

Tabula is a tool for liberating data tables trapped inside PDF files

tabula-java

Extract tables from PDF files

tabula-py

Simple wrapper of tabula-java: extract table from PDF into pandas DataFrame

tensorflow

Computation using data flow graphs for scalable machine learning

tensorflow_seq2seq_chatbot

textblob

Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

tonghe-qingyuan

tripadvisor-review-analysis

Big Data Coding Challenge

tushare

TuShare is a utility for crawling historical data of China stocks

tutorial

tweepy

Twitter for Python!

twitter-sentiment

Sentiment Analysis of Twitter Feeds

twitterhybridclassifier

Source code for the Twitter Hybrid Sentiment Classifier used in Semeval 2014 competition. (Sentiment Analysis system)

ubuntu-ranking-dataset-creator

A script that creates train, valid and test datasets for the ranking task from Ubuntu corpus dialogs.

ucl-latex-thesis-templates

UCL LaTeX thesis templates.

unofficial-google-trends-api

Python-based API that uses the http site to download Google Trends data

usent

Subjectivity and sentiment classification using polarity lexicons

vadersentiment

VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.

vmls.jl

web-crawler-data-extraction

A web crawler made in python using Scrapy framework which crawls www.bloomberg.com and scrapes all the articles of the website

whymathematica

:computer: Mathematica 简易教程/一个乱七八糟的放 notebook 的地方

word2vec-sentiments

Tutorial for Sentiment Analysis using Doc2Vec in gensim (or "getting 87% accuracy in sentiment analysis in under 100 lines of code")