An Effective Social Network Sentiment Mining Model for Healthcare Product Sales Analysis

Abstract

Social network websites have become important marketing platforms for studying business models. When impressive advertisements are posted on the platforms, social network users like to comment or post their experiences as part of feedback about the products. Those interesting opinions will exert influences on other users' buying decisions but some may remain commented. This invokes people's interests to dig out interesting relationship between sentiment of social network users and the volume of product sales. Then, they can apply the discovered patterns of sentiment and sales to predict users' buying behaviors. This study proposes a framework based on data mining method to find interesting patterns of sentiment and sales. The proposed model starts by defining sentiment topics with their corresponding terms and then follows by a fuzzy model to infer the sentiment scores for user opinions. Each transaction in the database is transformed to attach with public sentiment scores, influential users' sentiment scores and volume of product sales. To better obtain the relationship among public sentiment, users' sentiment and volume of product sales, a mining method of inter-transaction association rules is considered to extract the interesting patterns of sentiment and sales. Two case studies are given to verify the effectiveness of the proposed method.