PUBLIC’S SENTIMENT ANALYSIS ON SHOPEE-FOOD SERVICE USING LEXICON-BASED AND SUPPORT VECTOR MACHINE

Shafira Shalehanny; Agung Triayudi; Endah Tri Esti Handayani

doi:10.34288/jri.v4i1.129

Authors

Shafira Shalehanny Universitas Nasional
Agung Triayudi Universitas Nasional
Endah Tri Esti Handayani Universitas Nasional

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v4i1.129

Keywords:

opinion, Twitter, sentiment analysis, lexicon-based, support vector machine

Abstract

Technology field following how era keep evolving. Social media already on everyone’s daily life and being a place for writing their opinion, either review or response for product and service that already being used. Twitter are one of popular social media on Indonesia, according to Statista data it reach 17.55 million users. For online business sector, knowing sentiment score are really important to stepping up their business. The use of machine learning, NLP (Natural Processing Language), and text mining for knowing the real meaning of opinion words given by customer called sentiment analysis. Two methods are using for data testing, the first is Lexicon Based and the second is Support Vector Machine (SVM). Data source that used for sentiment analyst are from keyword ‘ShopeeFood’ and ‘syopifud’. The result of analysis giving accuracy score 87%, precision score 81%, recall score 75%, and f1-score 78%.

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