The Model of Sentiment Analysis for Classifying the Online Shopping Reviews
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Abstract
Nowadays, users can make a decision to order online goods and services from searching information related to goods and services. These may be based on opinions and reviews from previous purchasers as a guideline for purchasing decisions. Moreover, the current opinions information and reviews are enormous and increased all the time. consumers have to spend time for information analytics. Therefore, the model of sentiment analysis regarding goods and services reviews is needed. This research aims to build a model of Sentiment analysis with 3-level of emotion. they are positive neutral and negative, regarding previous user’s reviews and opinions towards online products and services. The techniques used in this research are Machine Learning including Word Segmentation and Bag of Words which compared four categories of sentiment analysis methods: LSTM, SGD, Logistic Regression and Support Vector Machines. There are 5 steps for model building as following: 1) Data Preparation Phase 2) Word Tokenization Phase 3) Training & Streaming Phase 4) Classification phase and 5) Model Evaluation Phase. The consumers’ opinions were gathered the datasets from open data with number of 12,900 comments. The model can help consumers to make a decision for purchasing of goods and services, and help entrepreneurs gain the information. This is to improve products and services in the future. This proposed method can classify the opinions into 3 scales which are positive, neutral and negative opinions. In summary, the proposed sentiment analysis model can perform LSTM accuracy is at 81.27%, Logistic Regression accuracy is at 69%, SGD accuracy is at 66% and Support Vector Machines accuracy is at 65%. However, the LSTM shows better performance on the classification compared to other techniques with deep learning approach. It also found that the F1-score can be implemented for Thai text appropriately.
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