Seminar "Modeling and Predicting Helpfulness of Online reviews"

Thursday, February 2^nd, 2023 at 18.00 there will be a seminar on the topic "Modeling and Predicting Helpfulness of Online reviews".

Abstract: Consumers prefer reading online reviews before making their buying decisions. However, it is challenging to discern the best online reviews due to the large volume of online reviews for some products. In this regard, the helpfulness characteristic of online reviews is effective in dealing with information overload and supports consumers in their decision-making process. In this talk, I will share my contributions in developing predictive models for helpfulness of product reviews. I addressed helpfulness in two prospective: Binary classification and Regression model. Several newly-proposed review content, reviewer, and product features are investigated. Specifically, state-of-the-art semantic, word contextual embedding and language models are explored. Likewise, popular machine learning, ensemble and deep learning models are also utilized to build the effective frameworks. Accordingly, our work could be of value to the research community concerned by identifying what makes a review helpful or not helpful by uncovering the importance of new indicators that sheds light on the empirical relationship between these variables and review helpfulness. Additionally, our work has important implications for marketing professionals and retailer platforms that can utilize our results to optimize their customer feedback systems, enhance reviewer guidelines, and include more useful product reviews.