SENTIMENT ANALYSIS OF ONLINE LOANS ON TWITTER USING LEXICON BASED METHODS AND SUPPORT VECTOR MACHINE (SVM)

Cita Suci Saputri; Arie Qur'ania; Irma Anggraeni

doi:10.33751/komputasi.v21i2.10125

SENTIMENT ANALYSIS OF ONLINE LOANS ON TWITTER USING LEXICON BASED METHODS AND SUPPORT VECTOR MACHINE (SVM)

Cita Suci Saputri, Arie Qur'ania, Irma Anggraeni

Abstract

Technological developments are increasingly rapid and moving towards digital, which in the end technology can also help people who are experiencing economic problems, namely with online loan services. Even though there are many conveniences provided by online loan services, of course not all people give positive comments because there are quite a few negative comments about this service.One of the social media that is widely used by the public to provide comments about online loans is Twitter. Sentiment analysis is a data processing process to obtain information about whether an opinion sentence tends to be positive, negative or even neutral. This research contains sentiment analysis towards Online Loans on Twitter using the Lexicon Based and Support Vector Machine methods. From the results of this research, the accuracy for SVM was 82.36%. From these results it can be concluded that the use of the Lexicon Based and Support Vector Machine methods is considered quite good and effective for classifying sentiment

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DOI: 10.33751/komputasi.v21i2.10125