Rating and Comments Mining Using TF-IDF and SO-PMI for Improved Priority Ratings

• Jinah Kim,
• Nammee Moon,

Vol. 13, No. 11, November 30, 2019
10.3837/tiis.2019.11.003, Download Paper (Free):

• Data Mining
• Text Mining
• Comments Mining
• TF-IDF
• SO-PMI

Abstract

Data mining technology is frequently used in identifying the intention of users over a variety of information contexts. Since relevant terms are mainly hidden in text data, it is necessary to extract them. Quantification is required in order to interpret user preference in association with other structured data. This paper proposes rating and comments mining to identify user priority and obtain improved ratings. Structured data (location and rating) and unstructured data (comments) are collected and priority is derived by analyzing statistics and employing TF-IDF. In addition, the improved ratings are generated by applying priority categories based on materialized ratings through Sentiment-Oriented Point-wise Mutual Information (SO-PMI)-based emotion analysis. In this paper, an experiment was carried out by collecting ratings and comments on 쐏lace and by applying them. We confirmed that the proposed mining method is 1.2 times better than the conventional methods that do not reflect priorities and that the performance is improved to almost 2 times when the number to be predicted is small.

Statistics

Cite this article