Algorithmic Trading and Sentiment Analysis in Indian Stock Market
The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Sentiment analysis, an important part of text mining, attempts to learn about the author’s opinions on a text through its content and structure. S...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2024/11/itmconf_icaetm2024_01011.pdf |
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| Summary: | The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Sentiment analysis, an important part of text mining, attempts to learn about the author’s opinions on a text through its content and structure. Such information is particularly valuable for determining the overall opinion of a large number of people. Examples of its usefulness are predicting box office sales or stock prices. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. This study, will predict a sentiment value for stock-related tweets on Twitter, and demonstrate a correlation between this sentiment and the movement of a company’s stock price in a real-time streaming environment. This study data ranges from the period 2018 to 2024. The study reveals that the percentage of error which is less than 5% on almost all companies except one. Where it tells that if the percentage of Error is less than 5 then the accuracy is high and the predicted prices are more accurate. |
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| ISSN: | 2271-2097 |