Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature

Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature

twitter, sentiment analysis, sarcasm, social media, textual feature, emoji feature

Twitter is a social media platform used to express sentiments about events, topics, individuals, and
groups. Sentiments in Tweets can be classified as positive or negative expressions. However, the
sentiment is an expression that is the opposite of what is meant to be, and this is called sarcasm. The
existence of sarcasm in a Tweet is chalenging to be detected automatically by a system, even by humans. In this research, we propose a weighting scheme based on the inconsistency between the sentiment of Indonesian tweets and the usage of emoji. The weighting scheme for detecting sarcasm can be used to find out a sentiment about an event, topic, individual, group, or product's review. The proposed method calculates the distance between the textual feature polarity score obtained from the Convolutional Neural Network and the emoji polarity score in a Tweet. This method is used to find the boundary value between Tweets that contain sarcasm or not. The model's experimental results developed obtained an f1-score of 87.5%, precision 90.5%, and recall 84.8%

Bagus Satria Wiguna, Cinthia Vairra Hudiyanti, Alqis Rausanfita, Agus Zainal Arifin, Rizka W. Sholikah

http://dx.doi.org/10.21609/jiki.v14i1.812

Faculty of Computer Science Universitas Indonesia

2021-02-28

Sri Wahyuni

e-ISSN : 2502-9274 printed ISSN : 2088-7051

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Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)