With the advent of fast-paced information dissemination and retrieval, it has become inherently important to resort to automated means of predicting stock market prices. In this paper, we propose Taureau, a framework that leverages Twitter sentiment analysis for predicting stock market movement. The aim of our research is to determine whether Twitter, which is assumed to be representative of the general public, can give insight into the public perception of a particular company and has any correlation to that company's stock price movement. We intend to utilize this correlation to predict stock price movement. We first utilize Tweepy and getOldTweets to obtain historical tweets indicating public opinions for a set of top companies during periods of major events. We filter and label the tweets using standard programming libraries. We then vectorize and generate word embedding from the obtained tweets. Afterward, we leverage TextBlob, a state-of-the-art sentiment analytics engine, to assess and quantify the users' moods based on the tweets. Next, we correlate the temporal dimensions of the obtained sentiment scores with monthly stock price movement data. Finally, we design and evaluate a predictive model to forecast stock price movement from lagged sentiment scores. We evaluate our framework using actual stock price movement data to assess its ability to predict movement direction.

翻译：随着快速信息传播与检索时代的到来，借助自动化手段预测股票市场价格变得愈发重要。本文提出公牛框架，该框架利用推特情感分析预测股票市场走势。本研究旨在探究推特（被视为公众舆论的代表）能否反映公众对特定公司的看法，以及其与该公司股票价格走势是否存在相关性。我们计划利用这种相关性来预测股票价格变动。首先，使用Tweepy和getOldTweets获取一组顶级公司在重大事件期间的历史推文，以反映公众意见。通过标准编程库对推文进行过滤和标注，随后对获取的推文进行向量化并生成词嵌入。进而利用最先进的情感分析引擎TextBlob基于推文评估和量化用户情绪。接着，将所得情感得分的时间维度与月度股票价格走势数据进行关联。最后，设计并评估基于滞后情感得分预测股票价格走势的预测模型。我们利用实际股票价格走势数据评估该框架预测走势方向的能力。