Automated Assessment of Review Quality Using Latent Semantic Analysis

Quality of a review can be identified by reviewing a review. Quantifiable factors that help identify the quality of a review include quality and tone of review comments, and the number of tokens each contains. We use machine-learning techniques such as latent semantic analysis (LSA) and cosine similarity to classify comments based on their quality and tone. Our paper details experiments that were conducted on student review and metareview data by using different data pre-processing steps. We compare these pre-processing steps and show that when applied to student review data, they help improve data quality by providing better text classification. Our technique helps predict metareview scores for student reviews.