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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| Format: | Article |
| Language: | English |
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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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| author | Patil Smita Satish Kubsad Pramod Kulkarni Savitha |
| author_facet | Patil Smita Satish Kubsad Pramod Kulkarni Savitha |
| author_sort | Patil Smita Satish |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc0c886d5fae41d0bcb3ac03cfd97a86 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-dc0c886d5fae41d0bcb3ac03cfd97a862024-12-13T10:03:56ZengEDP SciencesITM Web of Conferences2271-20972024-01-01680101110.1051/itmconf/20246801011itmconf_icaetm2024_01011Algorithmic Trading and Sentiment Analysis in Indian Stock MarketPatil Smita Satish0Kubsad Pramod1Kulkarni Savitha2KLS Institute of Management Education and ResearchM S Ramaiah University of Applied SciencesKLS Institute of Management Education and ResearchThe 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.https://www.itm-conferences.org/articles/itmconf/pdf/2024/11/itmconf_icaetm2024_01011.pdf |
| spellingShingle | Patil Smita Satish Kubsad Pramod Kulkarni Savitha Algorithmic Trading and Sentiment Analysis in Indian Stock Market ITM Web of Conferences |
| title | Algorithmic Trading and Sentiment Analysis in Indian Stock Market |
| title_full | Algorithmic Trading and Sentiment Analysis in Indian Stock Market |
| title_fullStr | Algorithmic Trading and Sentiment Analysis in Indian Stock Market |
| title_full_unstemmed | Algorithmic Trading and Sentiment Analysis in Indian Stock Market |
| title_short | Algorithmic Trading and Sentiment Analysis in Indian Stock Market |
| title_sort | algorithmic trading and sentiment analysis in indian stock market |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2024/11/itmconf_icaetm2024_01011.pdf |
| work_keys_str_mv | AT patilsmitasatish algorithmictradingandsentimentanalysisinindianstockmarket AT kubsadpramod algorithmictradingandsentimentanalysisinindianstockmarket AT kulkarnisavitha algorithmictradingandsentimentanalysisinindianstockmarket |