@inproceedings{8b37090e723846d9bf2b103fa378972b,
title = "Sentiment Analysis of Stock Market Investors and Its Correlation with Stock Price Using Maximum Entropy",
abstract = "To study the correlation between the sentiment of the stock market investors and the stock price, this paper uses Python web crawler to crawl for 20 representative stocks on the Shanghai Stock Exchange (SSE) with two-year{\textquoteright}s data and the comment text of related stocks in Guba (a stock forum at Sina.com). Through the extraction and preprocessing on the crawled data, a classifier model using maximum entropy is built to classify stock-related comments into three sentiment labels for analysis. Eventually the sentiment index of investor is established to compare with the fluctuation of stock price. The comparison shows that there is a correlation between the sentiment index of investor and the volatility of stock price, which is beneficial to help investor monitor public opinion and adopts diverse strategies in the stock market.",
keywords = "Crawler, Machine learning, Maximum entropy, Sentiment analysis, Stock",
author = "Liang Xue and Han Wang and Fengling Wang and Huawen Ma",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 20th IEEE/ACIS International Summer Semi-Virtual Conference on Computer and Information Science, ICIS 2021 ; Conference date: 23-06-2021 Through 25-06-2021",
year = "2021",
doi = "10.1007/978-3-030-79474-3\_3",
language = "英语",
isbn = "9783030794736",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "29--44",
editor = "Roger Lee",
booktitle = "Computer and Information Science, 2021",
address = "德国",
}