A Study on the Recommendation of Contents using Speech Emotion Information and Emotion Collaborative Filtering

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.

show abstract

“…Ridge regression is a linear regression that has L 2 -constraints. The ridge estimates are obtained using Equation (4).…”

Section: Step 5-2 Review Data Preprocessingmentioning

confidence: 99%

“…However, questions have been raised whether the user rating information properly reflects the user preferences. This uncertainty has led to various studies that developed collaborative filtering models that better reflect user preferences [4].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Digitization Modeling for Recommendation System

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Speech converts text-based information into sound that contains both emotions and lexical meaning. Factors such as the use of onomatopoeia, vocalization speed, and the length of pauses between phonations are useful clues for detecting a subject’s emotions [ 6 ]. Speech is the most efficient and natural type of human–machine interfacing and research on extracting the emotions contained in speech is very active.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering

Kim

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.

show abstract

Emotion and Collaborative Filtering-Based Recommendation System

Kim

2020

The 9th International Conference on Smart Media and Applications

View full text Add to dashboard Cite

A Study on the Recommendation of Contents using Speech Emotion Information and Emotion Collaborative Filtering

Cited by 5 publications

References 8 publications

Sentiment Digitization Modeling for Recommendation System

Sentiment Digitization Modeling for Recommendation System

Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering

Emotion and Collaborative Filtering-Based Recommendation System

Contact Info

Product

Resources

About