Research Article
Comparison Study Of Term Weighting Optimally With SVM In Sentiment Analysis
@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
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.