Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Using information scent to model user information needs and actions and the Web
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Dealing with mobility: understanding access anytime, anywhere
ACM Transactions on Computer-Human Interaction (TOCHI)
Sentiment analysis: capturing favorability using natural language processing
Proceedings of the 2nd international conference on Knowledge capture
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Opinion observer: analyzing and comparing opinions on the Web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
The sentimental factor: improving review classification via human-provided information
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis
Computational Linguistics
Feature and Opinion Mining for Customer Review Summarization
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Voice of the customers: mining online customer reviews for product feature-based ranking
WOSN'10 Proceedings of the 3rd conference on Online social networks
A comparative study of TF*IDF, LSI and multi-words for text classification
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new fuzzy dempster MCDM method and its application in supplier selection
Expert Systems with Applications: An International Journal
Fuzzy VIKOR with an application to water resources planning
Expert Systems with Applications: An International Journal
Semi-supervised latent variable models for sentence-level sentiment analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Lexicon-based Comments-oriented News Sentiment Analyzer system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Information Management: The Journal for Information Professionals
Hi-index | 12.05 |
With the rapid growth and dissemination of mobile services, enhancement of customer satisfaction has emerged as a core issue. Customer reviews are recognized as fruitful information sources for monitoring and enhancing customer satisfaction levels, particularly as they convey the real voices of actual customers expressing relatively unambiguous opinions. As a methodological means of customer review analysis, sentiment analysis has come to the fore. Although several sentiment analysis approaches have proposed extraction of the emotional information from customer reviews, however, a lacuna remains as to how to effectively analyze customer reviews for the purpose of monitoring customer satisfaction with mobile services. In response, the present study developed a new framework for measurement of customer satisfaction for mobile services by combining VIKOR (in Serbian: ViseKriterijumsa Optimizacija I Kompromisno Resenje) and sentiment analysis. With VIKOR, which is a compromise ranking method of the multicriteria decision making (MCDM) approach, customer satisfaction for mobile services can be accurately measured by a sentiment-analysis scheme that simultaneously considers maximum group utility and individual regret. The suggested framework consists mainly of two stages: data collection and preprocessing, and measurement of customer satisfaction. In the first, data collection and preprocessing stage, text mining is utilized to compile customer-review-based dictionaries of attributes and sentiment words. Then, using sentiment analysis, sentiment scores for attributes are calculated for each mobile service. In the second stage, levels of customer satisfaction are measured using VIKOR. For the purpose of illustration, an empirical case study was conducted on customer reviews of mobile application services. We believe that the proposed customer-review-based approach not only saves time and effort in measuring customer satisfaction, but also captures the real voices of customers.