TEXT MINING AND SENTIMENT ANALYSIS ON REVIEWS OF PROTON CARS IN MALAYSIA - Yap, Bee Wah; Abdullah, Nazira; Abdul-Rahman, Shuzlina; Tan, Michael Loong Peng (Malaysian Journal of Science, 2018)
Social networks play an important role in commercial products, and thus knowing the emotions and opinions of users is very useful in improving services, sales, business and marketing strategies. This paper illustrates text mining approach and sentiment analysis to gain valuable insights into consumer perceptions towards Proton cars in Malaysia. In the first case study, a total of 5000 comments posted on Paultan’s Automotive News Facebook page were retrieved and analyzed using R, a statistical open source programming software. The final dataset consists of 277 posts with 2964 comments. Data cleaning was carried out to remove non-English word. A word cloud was then generated and the frequently mentioned words are “China”, “like”, “price” and “good” and ‘SUV’. In the second case study, we perform sentiment analysis on a total of 5330 comments using SAS Enterprise Miner 14.1. Out of the 60 documents, 39 (65%) posts were positive comments from the public while 21 (35%) posts were negative comments. The frequently mentioned words in the positive documents are “look good”, “buy”, “better” and “nice”. Chi-Square and Information Gain were compared in selecting the meaningful terms of features. The selected features were used to evaluate the performance of Support Vector Machine (SVM) in classifying the posts as positive or negative. Five-fold cross validation results showed that SVM using linear kernel function has the highest accuracy (73.3%), sensitivity (76.7%) and F-measure (0.805).