Proceedings of The 2nd International Conference On Advance And Scientific Innovation, ICASI 2019, 18 July, Banda Aceh, Indonesia

Research Article

Comparison Study Of Term Weighting Optimally With SVM In Sentiment Analysis

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  • @INPROCEEDINGS{10.4108/eai.18-7-2019.2288508,
        author={Amril Mutoi Siregar and Sutan  Faisal and Tukino  Tukino and Adam  Puspabhuana and Manase Sahat H Simarangkir},
        title={Comparison Study Of Term Weighting Optimally With SVM In Sentiment Analysis},
        proceedings={Proceedings of The 2nd International Conference On Advance And Scientific Innovation, ICASI 2019, 18 July, Banda Aceh,  Indonesia},
        publisher={EAI},
        proceedings_a={ICASI},
        year={2019},
        month={11},
        keywords={tf -- idf tf binary term of occurrence sentimentanalysis svm twitter},
        doi={10.4108/eai.18-7-2019.2288508}
    }
    
  • Amril Mutoi Siregar
    Sutan Faisal
    Tukino Tukino
    Adam Puspabhuana
    Manase Sahat H Simarangkir
    Year: 2019
    Comparison Study Of Term Weighting Optimally With SVM In Sentiment Analysis
    ICASI
    EAI
    DOI: 10.4108/eai.18-7-2019.2288508
Amril Mutoi Siregar1,*, Sutan Faisal1, Tukino Tukino1, Adam Puspabhuana2, Manase Sahat H Simarangkir3
  • 1: Deparment of Technology and Computer Science, Buana PerjuanganUniversity,Jalan HS. Ronggo WaluyoTelukjambe Timur, Karawang, Indonesia
  • 2: Department of Information System Diploma, STMIK of Kharisma, JalanPangkalPerjuangan KM 1, Karawang, Indonesia
  • 3: 3Department of ComputerEngineering, Meta Industry ¬¬Polytechnic, JalanInti I Blok C1 No. 7LippoCikarangCibatuCikarang, Bekasi, Indonesia
*Contact email: amrilmutoi@ubpkarawang.ac.id

Abstract

The rapid of internet and social media users have changed the way people interact in their daily activities. For example, banking and retail began to use various social media, especially online media such as tweeter. The problem that arises is how to get information from thousands and even million data generated through social media, to be a decision as in predicting consumer satisfaction of the service or product.Another problem is the social media users in communicating using slang or local language. In sentiment analysis to predict the sentiment is not easy because it must be able to identify the words. In sentiment analysis, to overcome these problems the method used is text mining so as to process opinions from social media. The proposed approach is to analyze optimal term weighting between TF-IDF, frequency term (TF) and Binary Term Occurrence (BTO), using SVM algorithm. Target feature extraction for selection of datasets by predicting positive and negative sentiments. The result of weighting of terms approaching sentiment is using TF-IDFwith SVM.