Aplikasi Analisa Sentimen Bilingual dan Emoji pada Komentar Media Sosial Instagram Menggunakan Metode Support Vector Machine

Satria Adi Nugraha(1*), Henry Novianus Palit(2), Hans Juwiantho(3),


(1) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(2) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(3) Program Studi Teknik Informatika, Universitas Kristen Petra Surabaya
(*) Corresponding Author

Abstract


Indonesia is ranked 4th as the most Instagram user in the world. This makes business people triggered to promote their products and services to content creators to make reviews and upload them on Instagram. Business people need to evaluate uploads to assess whether the promotions carried out get a positive or negative response from netizens. Evaluation can be done by checking the comments column. Instagram comments not only contain comments in Indonesian but in English along with emojis. However, checking manually will certainly take a lot of time. Therefore, it is necessary to build an application system that can detect bilingual sentiments and emojis in Instagram comments. This system was built using the Support Vector Machine method to classify language, Indonesian sentiment, and English sentiment and then evaluated using the accuracy value. The data used is a sample of uploaded comments in the form of posts, reels, and IGTV. The combination of preprocessing cleansing, normalization, stopwords removal, and stemming as well as parameter tuning using GridSearchCV was also tested to find the best model. The model is divided into language classification models with Indonesia, Inggris, and Campuran labels, Indonesian sentiment classifications, and English sentiment classifications with positive, neutral, and negative labels. The best accuracy obtained by the model for language classification, Indonesian sentiment, and English sentiment is 88.77%, 73.10%, and 71.56%, respectively. In addition, emojis need to be analyzed because the model that analyzes emojis has 3.875% better accuracy than the model that ignores emoji.

Keywords


sentiment analysis; Support Vector Machine; Instagram comments; bilingual sentiment analysis

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