ACADSTAFF UGM

CREATION
Title : Sarcasm Detection For Sentiment Analysis in Indonesian Tweets
Author :

YESSI YUNITASARI (1) Aina Musdholifah, S.Kom., M.Kom. Ph.D (2) Anny Kartika Sari, S.Si., M.Sc., Ph.D. (3)

Date : 0 2019
Keyword : Naïve bayes, sarcasm,tweet,sentiment analysis,random forest Naïve bayes, sarcasm,tweet,sentiment analysis,random forest
Abstract : Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is83.2%, and the recall is91.3%.
Group of Knowledge : Ilmu Komputer
Original Language : English
Level : Internasional
Status :
Published
Document
No Title Document Type Action
1 2019_Sarcasm Detection For Sentiment Analysis in Indonesian Tweets.pdf
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2 Sarcasm Detection For Sentiment Analysis in Indonesian Tweets.pdf
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