Computerised Sentiment Analysis On Socialnetworks. Two Case Studies: Fifa World Cup 2018and Cristiano Ronaldo Joining Juventus

Sentiment analysis on social networks plays a prominent role inmany applications. The key techniques here are how to identify opinions, toclassify sentiment polarity, and to infer emotions. In this study, we proposeda sentence-level sentiment analysis based on a microblogging platform likeTwitter. Comprehensive evaluation results on real-world scenarios such as theFIFA World Cup 2018, and the Cristiano Ronaldo’s transfer to Juventus inthe summer of 2018 demonstrate a correlation between the polarity (nega-tive or positive) and fan’s sentiments. In addition, the evaluation of severalmachine learning techniques; applied to identify the polarity and related emo-tions, revealed that the SVM outperforms other models such as Naive Bayes,ANN, kNN, and Logistic Regression. Additional studies should be addressedto evaluate the proposed system on different sport events, and scenarios.