Conditioning Customers' Product Reviews for Accurate Classification Performance

Abstract

In recent years, people use Internet as a platform to express their own ideas and opinions about various subjects or products. The data from these sites serve as sources for sentiment analysis. On e-commerce websites, the costumer product review conventionally expresses sentiment that corresponds with the given star rating; however, this is not always true; there are reviews that express sentiments opposite to the given star rating, which can be labeled as outliers. This paper builds on previous work that finds outliers in product review datasets, scraped from Amazon.com, using a statistics-based outlier detection and correction method (SODCM). This work focuses on 3-star reviews specifically and studies the correct polarity assignment of 3-star reviews. It investigates the behavior of SODCM when 3-star reviews are classified as negative and positive respectively.