Proceedings of the First International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India

Research Article

Comprehensive Analysis of Classifier to Identify Sentiment in Football Specific Tweets

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  • @INPROCEEDINGS{10.4108/eai.16-5-2020.2304099,
        author={Venkatesh  Venkatesh and C V Keerthi and Y  Nagaraj and S  Swetha},
        title={Comprehensive Analysis of Classifier to Identify Sentiment in Football Specific Tweets},
        proceedings={Proceedings of the First  International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICASISET 2020, 16-17 May 2020, Chennai, India},
        publisher={EAI},
        proceedings_a={ICASISET},
        year={2021},
        month={1},
        keywords={stance detection classifier twitter feature extraction lexicon},
        doi={10.4108/eai.16-5-2020.2304099}
    }
    
  • Venkatesh Venkatesh
    C V Keerthi
    Y Nagaraj
    S Swetha
    Year: 2021
    Comprehensive Analysis of Classifier to Identify Sentiment in Football Specific Tweets
    ICASISET
    EAI
    DOI: 10.4108/eai.16-5-2020.2304099
Venkatesh Venkatesh1,*, C V Keerthi1, Y Nagaraj1, S Swetha1
  • 1: Department of Computer Science and Engineering, University Visvesvaraya College of Engi-neering, Bangalore University, K R Circle, Bengaluru-56001
*Contact email: testtt@test.test

Abstract

The football fans' feelings get unfold during the different phases of the football match and they express their emotions, stance on Twitter. This re-search work focuses on identifying the stance expressed by the fans on Twitter. The changes in fans' opinions are reflected in a series of tweets written by fans. In this paper, various classification algorithms are used to analyze and catego-rize sentiments present in tweets posted during the 2018 FIFA World Cup. In this work, a football-specific dataset is created and labeled manually. From the dataset, a lexicon related to football-specific sentiment is created. We use do-main-specific lexicons, the TF-IDF feature selection method, Count Vectorizer, and various sentiment classifiers to identify the sentiment expressed by football fans on Twitter. In this paper, the performance of different classifier algorithms is analyzed while determining the hidden sentiment. Our experiment results demonstrate that the Random Forest algorithm exhibits consistent and robust performance compared to other classifiers