股票新闻文章的情绪分析

V. Kalyanaraman, Sarah Kazi, Rohan Tondulkar, Sangeeta Oswal
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引用次数: 14

摘要

在本文中,我们利用新闻文章的情绪分析来观察其对股票价格的影响。我们使用必应API收集数据集,该API为我们提供了有关特定公司的新闻文章的链接。由于没有专门针对股票文章的预先存在的情感词典,我们创建了一个专门用于分析股票文章的情感词典。将两种不同的机器学习算法应用于数据集,并比较了两种算法的准确性。为了测试我们的结果,我们为数据集中的每篇文章附加了一个整体情绪,并将其与算法预测的情绪进行比较。我们还将预测结果与市场上股票价格的实际变化进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Analysis on News Articles for Stocks
In this paper we have used sentiment analysis on news articles to see its effect on stock prices. We collected our dataset using Bing API which gave us links to news articles about a specific company. As no pre-existing sentiment dictionary specifically for stock articles exited, we created a specialized sentiment dictionary only meant to analyze stock articles. Two different machine learning algorithms were applied to the dataset and the accuracy of the two was compared. In order to test our results we attached an overall sentiment to each article in our data set which was compared to the predicted sentiment by the algorithm. We also compared the predicted results with the actual change in the stock prices on the market.
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