TweetFinSent: A Dataset of Stock Sentiments on Twitter

Yulong Pei, A. Mbakwe, Akshat Gupta, Salwa Alamir, Hanxuan Lin, Xiaomo Liu, Sameena Shah
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引用次数: 3

Abstract

Stock sentiment has strong correlations with the stock market but traditional sentiment analysis task classifies sentiment according to having feelings and emotions of good or bad. This definition of sentiment is not an accurate indicator of public opinion about specific stocks. To bridge this gap, we introduce a new task of stock sentiment analysis and present a new dataset for this task named TweetFinSent. In TweetFinSent, tweets are annotated based on if one gained or expected to gain positive or negative return from a stock. Experiments on TweetFinSent with several sentiment analysis models from lexicon-based to transformer-based have been conducted. Experimental results show that TweetFinSent dataset constitutes a challenging problem and there is ample room for improvement on the stock sentiment analysis task. TweetFinSent is available at https://github.com/jpmcair/tweetfinsent.
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TweetFinSent: Twitter上的股票情绪数据集
股票情绪与股票市场有很强的相关性,但传统的情绪分析任务将情绪分类为有感觉和情绪好坏。这种情绪的定义并不能准确反映公众对特定股票的看法。为了弥补这一差距,我们引入了一个新的股票情绪分析任务,并为该任务提供了一个名为TweetFinSent的新数据集。在TweetFinSent中,tweet根据是否获得或预期从股票获得正或负回报进行注释。在TweetFinSent上进行了基于词典和基于变压器的情感分析模型的实验。实验结果表明,TweetFinSent数据集构成了一个具有挑战性的问题,并且在股票情绪分析任务上有足够的改进空间。TweetFinSent可在https://github.com/jpmcair/tweetfinsent上获得。
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