Leveraging Lexicon-Based and Sentiment Analysis Techniques for Online Reputation Generation

Abstract

Advanced reputation generation systems analyze user-generated content such as opinions and reviews expressed in natural language in order to produce a reliable and trusted reputation value. According to recent and relevant literature, while many important attributes are exploited such as semantic and sentiment orientation of the reviews, time, and opinions’ relevancy, other relevant attributes such as users’ credibility, as well as the sentiment intensity of the reviews, are not considered. In this paper, we propose a system that computes a single numerical reputation value between 0 and 10 from Twitter microblogging platform by incorporating the sentiment orientation of the tweets, the sentiment intensity of the positive tweets, as well as the users' and tweets' credibility score. To assess the effectiveness of our system we have compared its computed reputation value with the ground truth one ranging from 0 to 10. This later is the weighted average votes of thousands of users taken from IMDb, Amazon, TripAdvisor, and Yelp concerning respectively four products and services (movie: 7.9, phone product: 7.3, hotel: 9, restaurant: 7.1). The experimental results conducted on four real-world Twitter datasets related to the aforementioned products show that our system provides a reputation value (movie: 7.66, phone product: 7.18, hotel: 8.71, restaurant: 7.08) that is near to the ground truth one. Consequently, it can be applied in practice and used by consumers and businesses to generate a reliable and trusted reputation value from tweets in order to support them during their decision-making process in Ecommerce platforms (buying, renting, booking, etc.).