SENTIMENT ANALYSIS ON TWITTER BY USING MAXIMUM ENTROPY AND SUPPORT VECTOR MACHINE METHOD

With the advancement of social media and its growth, there is a lot of data that can be presented for research in social mining. Twitter is a microblogging that can be used. In this event, a lot of companies used the data on Twitter to analyze the satisfaction of their customer about product quality. On the other hand, a lot of users use social media to express their daily emotions. The case can be developed into a research study that can be used both to improve product quality, as well as to analyze the opinion on certain events. The research is often called sentiment analysis or opinion mining. While The previous research does a particularly useful feature for sentiment analysis, but it is still a lack of performance. Furthermore, they used Support Vector Machine as a classification method. On the other hand, most researchers found another classification method, which is considered more efficient such as Maximum Entropy. So, this research used two types of a dataset, the general opinion data, and the airline's opinion data. For feature extraction, we employ four feature extraction, such as pragmatic, lexical-grams, pos-grams, and sentiment lexical. For the classification, we use both of Support Vector Machine and Maximum Entropy to find the best result. In the end, the best result is performed by Maximum Entropy with 85,8% accuracy on general opinion data, and 92,6% accuracy on airlines opinion data

Studi Komparatif Metode Ekstraksi Fitur pada Analisis Sentimen Maskapai Penerbangan Menggunakan Support Vector Machine dan Maximum Entropy

Almost all companies use social media to improve their product services and provide after-sales services that allow their customers to review the quality of their products. By using Twitter social media to be an important source for tracking sentiment analysis. Sentiment analysis is one of the most popular studies today, using sentiment analysis companies can analyze customer satisfaction to improve their services. This study aims to analyze airline sentiments with five different features such as pragmatic, lexical n-gram, POS, sentiment, and LDA using the Support Vector Machine and Maximum Entropy methods. The best results can be obtained using the Maximum Entropy method using all feature extraction with an accuracy of 92.7% and in the Support Vector Machine method, the accuracy obtained is 89.2%.Hampir semua perusahaan menggunakan media sosial untuk meningkatkan layanan produknya, dan memberikan layanan after sales yang memungkinkan pelanggannya dapat me-review kualitas produknya. Dengan menggunakan media sosial twitter untuk menjadi sumber penting untuk melacak analisis sentimen. Analisis sentimen adalah salah satu penelitian paling populer saat ini, dengan menggunakan analisis sentimen perusahaan dapat menganalisis kepuasan pelanggan untuk meningkatkan layanannya. Penelitian ini bertujuan untuk menganalisis sentimen maskapai penerbangan dengan lima fitur berbeda seperti topik pragmatic, lexical n-gram, POS, sentimen, dan LDA menggunakan metode Support Vector Machine dan Maximum Entropy. Hasil terbaik di dapat pada metode Maximum Entropy menggunakan semua ekstraksi fitur dengan akurasi 92,7% dan pada metode Support Vector Machine akurasi yang diperoleh sebesar 89,2%