Sentiment and Emotion Analyses for Malaysian Mobile Digital Payment Applications

1. Sentiment and emotion analyses provide a quick and easy way to infer users' perceptions regarding products, services, topics and events, and thus rendering it useful to businesses and government bodies for effective decision making. In this paper, we describe the outcomes of sentiment and emotion analyses performed on a mobile payment app, Boost, which is available in the Google Play Store. A total of 2463 text reviews were gathered, however, after pre-processing, 1054 of these reviews were annotated and used for sentiment and emotion analyses. Four supervised learning algorithms, namely, Support Vector Machine, Naïve Bayes, Decision Tree and Random Forest were compared using Python. Accuracy and F1 scores indicate Random Forest to have outperformed all the other algorithms for both sentiment and emotion analyses. A vast majority of the reviews were found to contain anger for the negative sentiments, whereas joy was observed for the positive reviews.

[1]  Carlos Alberto Ochoa Ortíz Zezzatti,et al.  A Sentiment Analysis Model to Analyze Students Reviews of Teacher Performance Using Support Vector Machines , 2017, DCAI.

[2]  Nergis Pervan,et al.  SENTIMENT ANALYSIS USING A RANDOM FOREST CLASSIFIER ON TURKISH WEB COMMENTS , 2017 .

[3]  Yang Liu,et al.  Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms , 2017, Expert Syst. Appl..

[4]  Archana Singh,et al.  Comparative analysis of sentiment orientation using SVM and Naive Bayes techniques , 2016, 2016 2nd International Conference on Next Generation Computing Technologies (NGCT).

[5]  Ioannis Hatzilygeroudis,et al.  Recognizing emotions in text using ensemble of classifiers , 2016, Eng. Appl. Artif. Intell..

[6]  David M. Fresco,et al.  Emotions and Life: Perspectives from Psychology, Biology, and Evolution , 2005 .

[7]  Shujuan Wang,et al.  Naive Bayes classifiers for music emotion classification based on lyrics , 2017, 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).

[8]  Zhen Zuo.  Sentiment Analysis of Steam Review Datasets using Naive Bayes and Decision Tree Classifier , 2018 .

[9]  LiuYang,et al.  Multi-class sentiment classification , 2017 .

[10]  Amlan Chakrabarti,et al.  Demonetization and its aftermath: an analysis based on twitter sentiments , 2018, Sādhanā.

[11]  R. Plutchik.  Emotions and Life: Perspectives from Psychology, Biology, and Evolution , 2002 .

[12]  Melody Moh,et al.  On Multi-tier Sentiment Analysis Using Supervised Machine Learning , 2015, 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT).