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数据结构 ? 6.95M
Data Structure ?
* 以上分析是由系统提取分析形成的结果,具体实际数据为准。
README.md
Context
I have been interested in using public sentiment and journalism to gather sentiment profiles on publicly traded companies. I first developed a Python package (https://github.com/dwallach1/Stocker) that scrapes the web for articles written about companies, and then noticed the abundance of overlap with Twitter. I then developed a NodeJS project that I have been running on my RaspberryPi to monitor Twitter for all tweets coming from those mentioned in the *content* section. If one of them tweeted about a company in the stocks_cleaned.csv file, then it would write the tweet to the database. Currently, the file is only from earlier today, but after about a month or two, I plan to update the tweets.csv file (hopefully closer to 50,000 entries.
I am not quite sure how this dataset will be relevant, but I hope to use these tweets and try to generate some sense of public sentiment score.
Content
This dataset has all the publicly traded companies (tickers and company names) that were used as input to fill the tweets.csv. The influencers whose tweets were monitored were:
['MarketWatch', 'business', 'YahooFinance', 'TechCrunch', 'WSJ', 'Forbes', 'FT', 'TheEconomist', 'nytimes', 'Reuters', 'GerberKawasaki', 'jimcramer', 'TheStreet', 'TheStalwart', 'TruthGundlach', 'Carl_C_Icahn', 'ReformedBroker', 'benbernanke', 'bespokeinvest', 'BespokeCrypto', 'stlouisfed', 'federalreserve', 'GoldmanSachs', 'ianbremmer', 'MorganStanley', 'AswathDamodaran', 'mcuban', 'muddywatersre', 'StockTwits', 'SeanaNSmith'
Acknowledgements
The data used here is gathered from a project I developed : https://github.com/dwallach1/StockerBot
Inspiration
I hope to develop a financial sentiment text classifier that would be able to track Twitter's (and the entire public's) feelings about any publicly traded company (and cryptocurrency).
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