Longgang Zhao¹, Seok-Won Lee^2,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1855-1877, 2024, DOI:10.32604/cmc.2024.056215 - 15 October 2024

Abstract Although sentiment analysis is pivotal to understanding user preferences, existing models face significant challenges in handling context-dependent sentiments, sarcasm, and nuanced emotions. This study addresses these challenges by integrating ontology-based methods with deep learning models, thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback. The framework comprises explicit topic recognition, followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis. In the context of sentiment analysis, we develop an expanded sentiment lexicon based on domain-specific corpora by leveraging techniques such as word-frequency analysis and word embedding. More >

Mubashar Hussain¹, Toqir A. Rana^2,3, Aksam Iftikhar⁴, M. Usman Ashraf^5,*, Muhammad Waseem Iqbal⁶, Ahmed Alshaflut⁷, Abdullah Alourani⁸

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4641-4656, 2022, DOI:10.32604/cmc.2022.024759 - 28 July 2022

Abstract In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user’s behavior. However, in general, the… More >

Jang Hyun Kim^1,2,3, Jisung Jang^1,3, Yonghwan Kim⁴, Dongyan Nan^1,2,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3815-3826, 2022, DOI:10.32604/cmc.2022.029507 - 16 June 2022

Abstract This study explored user satisfaction with mobile payments by applying a novel structural topic model. Specifically, we collected 17,927 online reviews of a specific mobile payment (i.e., PayPal). Then, we employed a structural topic model to investigate the relationship between the attributes extracted from online reviews and user satisfaction with mobile payment. Consequently, we discovered that “lack of reliability” and “poor customer service” tend to appear in negative reviews. Whereas, the terms “convenience,” “user-friendly interface,” “simple process,” and “secure system” tend to appear in positive reviews. On the basis of information system success theory, we… More >

Seon Hong Lee¹, Haein Lee¹, Jang Hyun Kim^2,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4983-4997, 2022, DOI:10.32604/cmc.2022.027943 - 21 April 2022

Abstract Metaverse is one of the main technologies in the daily lives of several people, such as education, tour systems, and mobile application services. Particularly, the number of users of mobile metaverse applications is increasing owing to the merit of accessibility everywhere. To provide an improved service, it is important to analyze online reviews that contain user satisfaction. Several previous studies have utilized traditional methods, such as the structural equation model (SEM) and technology acceptance method (TAM) for exploring user satisfaction, using limited survey data. These methods may not be appropriate for analyzing the users of… More >

Jie Liu^a,b,c,d, Xiaodong Fu^d, Jin Liu^a,b,c, Yunchuan Sun^a,e

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 73-80, 2018, DOI:10.1080/10798587.2016.1267244

Abstract Reviews are contents written by users to express opinions on products or services. The information contained in reviews is valuable to users who are going to make decisions on products or services. However, there are numbers of reviews for popular products, and the quality of reviews is not always good. It’s necessary to pick out reviews, which are in high quality from numbers of reviews to assist user in making decision. In this paper, we collected 21,501 reviews flagged as good from 499,253 products on JD.com. We observed the level of users is an important More >