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 simil...

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Bibliographic Details
Published in2011 IEEE 11th International Conference on Advanced Learning Technologies pp. 136 - 138
Main Authors Ramachandran, L., Gehringer, E. F.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2011
Subjects
Accuracy
automated metareviewing
Humans
latent semantic analysis
Matrix decomposition
quality of reviews
Semantics
Syntactics
text mining
Thumb
Training
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ISBN9781612842097
1612842097
ISSN2161-3761
DOI10.1109/ICALT.2011.46

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Summary: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.
ISBN:9781612842097
1612842097
ISSN:2161-3761
DOI:10.1109/ICALT.2011.46