Statistics-Based Outlier Detection and Correction Method for Amazon Customer Reviews.

ABSTRACT: People nowadays use the internet to project their assessments, impressions, ideas, and observations about various subjects or products on numerous social networking sites. These sites serve as a great source to gather data for data analytics, sentiment analysis, natural language processing, etc. Conventionally, the true sentiment of a customer review matches its corresponding star rating. There are exceptions when the star rating of a review is opposite to its true nature. These are labeled as the outliers in a dataset in this work. The state-of-the-art methods for anomaly detection involve manual searching, predefined rules, or traditional machine learning techniques to detect such instances. This paper conducts a sentiment analysis and outlier detection case study for Amazon customer rev

SUBMITTER: Chatterjee I 

PROVIDER: S-EPMC8700267 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature