文章基本信息

标题：Prediction of Stock Market Shift using Sentiment Analysis of Twitter Feeds, Clustering and Ranking
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作者：Tejas Sathe ; Siddhartha Gupta ; Shreya Nair 等
期刊名称：International Journal of Computer Science and Network
印刷版ISSN：2277-5420
出版年度：2015
卷号：4
期号：2
页码：382-386
出版社：IJCSN publisher
摘要：The stock market is fluctuating constantly. The rise and fall in stock prices are seemingly random. However, this is not so. Even a minute happening in the company can have a huge effect on the stock price. As each investor buys and sells the stock, the price rises and falls depending on the sale and purchase, the demand and supply. Whether or not an investor buys a particular company's stock is based on his knowledge and impression of the company. The latter is what we will employ to decide whether or not to buy a certain company's stock at the current price. There are 6 accepted discrete moods. Millions of people tweet every second. A fairly accurate prediction and analysis of the tweet's underlying mood can be made using sentiment analysis. Each word has a certain grammatical signature that tells us which mood it belongs to. Depending on what the users are feeling about a company as they tweet about it this engine will decide whether or not one should buy stocks of that company. This paper describes how to map this mood with market sentiment and in turn with prediction of rise/fall of stock prices
关键词：Twitter;Sentiment Analysis;Successive Deviations;Stock Market;Mood