Sentiment Digitization Modeling for Recommendation System

Abstract

As the importance of providing personalized services increases, various studies on personalized recommendation systems are actively being conducted. Among the many methods used for recommendation systems, the most widely used is collaborative filtering. However, this method has lower accuracy because recommendations are limited to using quantitative information, such as user ratings or amount of use. To address this issue, many studies have been conducted to improve the accuracy of the recommendation system by using other types of information, in addition to quantitative information. Although conducting sentiment analysis using reviews is popular, previous studies show the limitation that results of sentiment analysis cannot be directly reflected in recommendation systems. Therefore, this study aims to quantify the sentiments presented in the reviews and reflect the results to the ratings; that is, this study proposes a new algorithm that quantifies the sentiments of user-written reviews and converts them into quantitative information, which can be directly reflected in recommendation systems. To achieve this, the user reviews, which are qualitative information, must first be quantified. Thus, in this study, sentiment scores are calculated through sentiment analysis by using a text mining technique. The data used herein are from movie reviews. A domain-specific sentiment dictionary was constructed, and then based on the dictionary, sentiment scores of the reviews were calculated. The collaborative filtering of this study, which reflected the sentiment scores of user reviews, was verified to demonstrate its higher accuracy than the collaborative filtering using the traditional method, which reflects only user rating data. To overcome the limitations of the previous studies that examined the sentiments of users based only on user rating data, the method proposed in this study successfully enhanced the accuracy of the recommendation system by precisely reflecting user opinions through quantified user reviews. Based on the findings of this study, the recommendation system accuracy is expected to improve further if additional analysis can be performed.

Contents and sentiment analysis of newspaper articles and comments on telemedicine in Korea: Before and after of COVID-19 outbreak

“…Sentiment analysis techniques built for English pose practical challenges for Korean because Korean is an agglutinative language 24 , 25 where part-of-speech is often not distinguished by the unit of space, and parts of speech may be different even within a bunch of words. In addition, Korean is a language with a drastic change in vocabulary.…”

Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis

“…The work [28] addresses the limitations of traditional CF techniques by introducing a sentiment digitization modeling framework. The authors emphasize the importance of considering user sentiment in recommendation systems, as it can significantly impact the relevance and personalization of recommendations.…”

Improving collaborative filtering using lexicon-based sentiment analysis

“…The method proposed in [26] presented a recommendation algorithm that enhances collaborative filtration efficiency by quantifying sentiments based on a dictionary. They mixed sentiments with the rating data in order to produce new rating data.…”