E-Commerce Customer Satisfaction Analysis On Microblogs

Abstract

Technology influences various things in people's lives, ranging from socialization to shopping behaviour, and hence marketplace inevitably appears as a part of e-commerce concepts. Opinions related to shopping activities are thus interesting for further research. This paper investigates the appropriate methods for analyzing e-commerce customer satisfaction. The research was conducted by compiling aspects of customer satisfaction and then by determining the appropriate lexicon method for sentiment analysis. A dataset containing 88,816 tweets was drawn from Twitter microblogs with a predefined set of keywords related to 5 e-commerce organizers. To analyze the data in a fine-grained manner, we also propose six aspects of customer satisfaction obtained from literature studies. Then we use the WordCloud visualization technique and topic modelling to get 73 keywords to categorize tweets into aspects. Our experiments carried out two scenarios: Scenario 1 compares several opinionated dictionaries, while Scenario 2 compares different approaches for computing sentiment scores. The application produces an appropriate method using Scenario 1 with an accuracy of 0.54. Scenario 2 produces the highest accuracy is 0.46. The application of the lexicon-based (or dictionary-based) method to sentiment analysis results that throughout e-commerce in every aspect, it has dominant positive sentiment, and the most dominant aspect throughout and in every e-commerce is the "product" aspect.