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http://hdl.handle.net/2080/4135
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DC Field | Value | Language |
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dc.contributor.author | Sharma, Karan | - |
dc.contributor.author | Hota, Lopamudra | - |
dc.contributor.author | Tikkiwa, Vinay Anand | - |
dc.contributor.author | Kumar, Arun | - |
dc.date.accessioned | 2023-12-18T04:43:22Z | - |
dc.date.available | 2023-12-18T04:43:22Z | - |
dc.date.issued | 2023-11 | - |
dc.identifier.citation | International Conference on Machine Learning and Data Engineering (ICMLDE), UPES, Dehradun, India, 23-24 November 2023 | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/4135 | - |
dc.description | Copyright belongs to proceeding publisher | en_US |
dc.description.abstract | In recent years, social media platforms such as Twitter have become popular among fans to discuss and share their opinions about the matches. This research aims to contribute to the growing body of knowledge on utilizing the sentiments expressed through messages on social media platforms to predict outcomes of matches in the sport of cricket. This research work provides insights into the effectiveness of sentiment analysis techniques in forecasting match outcomes, which can be beneficial to cricket fans, and cricket analysts. In this work, sentiment analysis is carried out based on models developed using various machine-learning techniques. The work also explores the impact of different features, such as the number of tweets collected, the timing of tweets, and the sentiment analysis techniques on the accuracy of the prediction. The results of this study have implications for cricket fans, sports analysts, and the broader field of predictive analytics. | en_US |
dc.subject | Cricket | en_US |
dc.subject | en_US | |
dc.subject | Sentiment Analysis | en_US |
dc.subject | Prediction | en_US |
dc.subject | Social Media Analytics | en_US |
dc.title | Exploring Twitter Sentiments for Predicting Match Outcomes in The Game of Cricket | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2023_ICMLDE_KSharma_Exploring.pdf | 626.98 kB | Adobe PDF | View/Open Request a copy |
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