Comparison of SVM and LIWC for Sentiment Analysis of SARA

Abstract

SARA is a sensitive issue based on sentiments about self-identity regarding ancestry, religion, nationality or ethnicity. The impact of the issue of SARA is conflict between groups that leads to hatred and division. SARA issues are widely spread through social media, especially Twitter. To overcome the problem of SARA, it is necessary to develop an effective method to filter negative SARA. This study aims to analyze Indonesian-language tweets and determine whether the tweet contains positive or negative SARA or does not contain SARA (neutral). Machine learning (i.e., SVM) and lexicon-based method (i.e., LIWC) were compared based on 450 tweet data to determine the best approach for each sentiment (positive, negative, and neutral). The best evaluation results are shown in the negative SARA classification using SVM with λ = 3 and γ = 0.1, where Precision = 0.9, Recall = 0.6, and F1-Score = 0.72. The best results from the positive SARA classification were shown in the LIWC method, where Precision = 0.6, Recall = 0.8, and F1-Score = 0.69. The best evaluation results for neutral classification are shown in SVM with λ = 3 and γ = 0.1, with Precision = 0.52, Recall = 0.87, and F1-Score = 0.65.

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