• Journal of Information Technology Services
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• v.21 no.2
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• pp.109-126
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• 2022

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

• International Journal of Contents
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• v.15 no.1
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• pp.1-9
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• 2019

Since the VR market is expected to have a high growth, this study aimed to investigate the human factor-related determinants of user satisfaction with mobile VR headsets. A pre-study of customer reviews was conducted with the help of semantic network analysis to identify the core keywords for understanding negative and positive predictors of mobile VR headset experiences. Through laboratory testing with three different commercial models, the main study measured and identified the predictors of user satisfaction. From the results, five factors were extracted as valid predictor variables and used for regression analysis. These factors were immersion, VR sickness, usability, wear-ability and menu navigation interface. All the five predictors were proved to be significant determinants of the perceived user satisfaction with mobile VR headsets. Usability was the strongest predictor, followed by VR sickness and wear-ability. Practical and theoretical implications of the results were discussed.

• ETRI Journal
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• v.43 no.1
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• The Journal of Information Systems
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• v.27 no.1
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• pp.89-110
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• 2018

Purpose There is much information in customer reviews, but finding key information in many texts is not easy. Business decision makers need a model to solve this problem. In this study we propose a multi-topic sentiment analysis approach using Latent Dirichlet Allocation (LDA) for user-generated contents (UGC). Design/methodology/approach In this paper, we collected a total of 104,039 hotel reviews in seven of the world's top tourist destinations from TripAdvisor (www.tripadvisor.com) and extracted 30 topics related to the hotel from all customer reviews using the LDA model. Six major dimensions (value, cleanliness, rooms, service, location, and sleep quality) were selected from the 30 extracted topics. To analyze data, we employed R language. Findings This study contributes to propose a lexicon-based sentiment analysis approach for the keywords-embedded sentences related to the six dimensions within a review. The performance of the proposed model was evaluated by comparing the sentiment analysis results of each topic with the real attribute ratings provided by the platform. The results show its outperformance, with a high ratio of accuracy and recall. Through our proposed model, it is expected to analyze the customers' sentiments over different topics for those reviews with an absence of the detailed attribute ratings.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.914-915
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• 2017

Importance and usage of the recommender system increases with the increase of information. The accuracy of the system recommendation primarily depends on the data. There is a problem in recommender systems, known as cold start problem. The lack of data about new products and users causes the cold start problem, and the system will not be able to give correct recommendation. This paper deals with cold start problem by comparing product specification and the review of the resembled products. The user, who likes the resembled product of the new one has more probability of taking interest in the new product as well. However, if a user disagreed with resembled product due to some reasons which the user mentioned in the reviews. The new product overcomes that issue, so the user will greatly accept the new product. Therefore, the system needs to recommend new product to those users as well, in this way the cold start problem will get resolved.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.163-175
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• 2024

This study conducts topic modeling analysis on four electric scooter sharing platforms: Alpaca, SingSing, Kickgoing, and Beam. Using user review data, the study aims to identify key topics and issues associated with each platform, as well as uncover common themes across platforms. The analysis reveals that users primarily express concerns and preferences related to application usability, service mobility, and parking/accessibility. Additionally, each platform exhibits unique characteristics and challenges. Alpaca users generally appreciate convenience and enjoyment but express concerns about safety and service areas. SingSing faces issues with application functionality, while Kickgoing users encounter connectivity problems and device usability issues. Beam receives overall positive feedback, but users express dissatisfaction with application usability and parking. Based on these findings, scooter sharing service providers should focus on enhancing application features, stability, and expanding service coverage to meet user expectations and improve customer satisfaction. Furthermore, highlighting platform-specific strengths and providing tailored services can enhance competitiveness and foster continuous service growth and development.

• Smart Media Journal
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• v.12 no.6
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• pp.27-34
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• 2023

As Netflix usage has increased due to the COVID-19 pandemic, users' experiences with the service have also increased. Therefore, this study aims to conduct topic modeling analysis based on Netflix review data to explore the changes in Netflix user experience and service before and after the COVID-19 pandemic. We collected Netflix app review data from the Google Play Store using the Google Play Scraper library, and used topic modeling to examine keyword differences between app reviews before and after the pandemic. The analysis revealed four main topics: Netflix app features, Netflix content, Netflix service usage, and Netflix overall reviews. After the pandemic, when user experience increased, users tended to use more diverse and detailed keywords in their reviews. By using Netflix review data to analyze users' opinions, this study shows the changes in user experience of Netflix services before and after the pandemic, which can be used as a guide to strengthen competitiveness in the competitive OTT market.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.111-122
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• 2021

This study analyzed user reviews of Dongbaekjeon and Incheoneum app, which are representative local currencies in Korea, to identify the positive/negative factors of local currency users, and established a marketing strategy based on this. App user reviews were classified into positive and negative based on the star rating, and word cloud, topic modeling, and social network analysis were performed, respectively. As a result, in the negative reviews of Dongbaekjeon and Incheoneum, dissatisfaction with app use and card issuance appeared in common. In positive reviews, keywords such as 'local economy' and 'small business owners' along with satisfaction with 'cashback' appeared. It means that local currency users perceived that their consumption support local economy, and they felt satisfaction in using local currency. Based on the satisfaction/dissatisfaction factors identified as a result of the analysis of this study, we identified what needs to be improved and to be strengthened, and appropriate marketing strategies were established. The text mining method used in this study and research results can provide meaningful information about local currencies to public officials and marketers in charge of local currencies.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.113-122
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• 2024

This comprehensive study delves into the analysis of user review data across various metaverse platforms, employing advanced text mining techniques such as TF-IDF and Word2Vec to gain insights into user perceptions. The primary objective is to uncover the factors that contribute to user satisfaction and dissatisfaction, thereby providing a nuanced understanding of user experiences in the metaverse. Through TF-IDF analysis, the research identifies key words and phrases frequently mentioned in user reviews, highlighting aspects that resonate positively with users, such as the ability to engage in creative activities and social interactions within these virtual environments. Word2Vec analysis further enriches this understanding by revealing the contextual relationships between words, offering a deeper insight into user sentiments and the specific features that enhance their engagement with the platforms. A significant finding of this study is the identification of common grievances among users, particularly related to the processes of refunds and login, which point to broader issues within payment systems and user interface designs across platforms. These insights are critical for developers and operators of metaverse platforms, suggesting a focused approach towards enhancing user experiences by amplifying positive aspects. The research underscores the importance of continuous improvement in user interface design and the transparency of payment systems to foster a loyal user base. By providing a comprehensive analysis of user reviews, this study offers valuable guidance for the strategic development and optimization of metaverse platforms, ensuring they remain responsive to user needs and continue to evolve as vibrant, engaging virtual environments.