Topic Classification Method for Analyzing Effect of eWOM on Consumer Game Sales
This work addresses marketing research for consumer game sales by analyzing eWOM, but it is incremental as it applies existing methods to new data with modest improvements.
The study tackled the problem of analyzing user needs for consumer game software by proposing a topic extraction method using entropy-based feature selection and SVM classification on tweet data, achieving a 0.63 F-measure.
Electronic word-of-mouth (eWOM) has become an important resource for the analysis of marketing research. In this study, in order to analyze user needs for consumer game software, we focus on tweet data. And we proposed topic extraction method using entropy-based feature selection based feature expansion. We also applied it to the classification of the data extracted from tweet data by using SVM. As a result, we achieved a 0.63 F-measure.