• C.H. Bhargava Phanindra
• Nirnai Rai
• Palash Chaturvedi
• S.R. Rajeswari

Soccer fans generate a large range of tweets to replicate their views and emotions on what's happening throughout numerous soccer matches round the globe. The tweets replicate the reactions of the fans throughout the commencement of the sport. Grouping the information of the sentiment expressed throughout can facilitate to grant complete image that expresses fan interaction throughout a specific soccer event. LDA is a generative applied mathematics model that permits sets of observations to be explained by unobserved teams that designate why some elements of the information square measure similar. LDA was accustomed to confirm what quite topics typically trend on twitter. Conducting in depth experiments on dataset to match the performance of various learning algorithms in distinguishing the sentiment expressed in soccer connected tweets. Topic modeling has been accustomed to confirm the subject of the tweets regarding soccer news.

Latent Dirchilet Allocation; topic modeling; twitter data; soccer

"Computational linguistics and data analysis of the football specific tweets using LDA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.401-403, June 2019, Available :http://www.jetir.org/papers/JETIR1907N94.pdf

"Computational linguistics and data analysis of the football specific tweets using LDA ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp401-403, June 2019, Available at : http://www.jetir.org/papers/JETIR1907N94.pdf