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Analisis Sentimen Film pada Twitter Berbahasa Indonesia Menggunakan Ensemble Features dan Naive Bayes Rosy Indah Permatasari; Mochammad Ali Fauzi; Putra Pandu Adikara; Eka Dewi Lukmana Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 11 (2018): November 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Sentiment analysis or opinion mining is one of the latest research topics in the field of information processing. It aims to know whether the polarity of a text-shaped data (document, sentence, paragraph) will lead to positive, negative, or neutral trait. This research used document text about Indonesian movie review which was obtained from Twitter. The method used in this research was Naive Bayes using Ensemble Features as a renewal feature beside Bag of Words Features. There are several types of Ensemble Features which are Twitter specific features, textual features, part of speech features, and lexicon based features. 500 data were used in this research, which were later divided into two types of data with the comparison of 70% for training data and 30% for testing data. The result of system accuracy obtained from sentiment analysis with Naive Bayes and Ensemble Features methods is 61.33%, 0.6369 precision, 0.5467 recall, and 0.5814 f-measure. The result of system accuracy using Ensemble Features and Bag of Words Features is 89.33%, 0.9041 precision, 0.88 recall, and 0.8922 f-measure.
Analisis Sentimen Opini Film Menggunakan Metode Naive Bayes dengan Ensemble Feature dan Seleksi Fitur Pearson Correlation Coefficient Fachrul Rozy Saputra Rangkuti; Mochammad Ali Fauzi; Yuita Arum Sari; Eka Dewi Lukmana Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Microblogging has become the media information that is very popular among internet users. Therefore, the microblogging became a source of rich data for opinions and reviews especially on movie reviews. We proposed, sentiment analysis on movie review using ensemble features and Bag of Words and selection Features Pearson's Correlation to reduce the dimension of the feature and get the optimal feature combinations. Use the feature selection is done to improve the performance of the classification, reducing the dimension of the feature and get the optimal feature combinations. The process of classification using several models of Naive Bayes i.e. Bernoulli Naive Bayes for binary data , Gaussian Naive Bayes for continuous data and Multinomial Naive Bayes for numeric data. The results of this study indicate that by using the non-standard word on tweet evaluation results obtained accuracy 82%, precision 86%, recall 79.62% and f-measure 82.69% using Feature Selection 20%. Then after using manual standardization of word the evaluation results on the accuracy increased by 8% and then the accuracy becomes 90%, precision 92%, recall 88.46% and f-measure 90.19% using 85% feature selection. Based on these results it can be concluded that by using the standardization of word can improve the performance of classification and feature selection Pearson's provide optimal feature combinations and reducing the total number of dimensions feature.
Co-Authors Fachrul Rozy Saputra Rangkuti M. Ali Fauzi Putra Pandu Adikara Rosy Indah Permatasari Yuita Arum Sari