Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 03
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
IEICE - Transactions on Information and Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on 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
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
An empirical study of sentiment analysis for chinese documents
Expert Systems with Applications: An International Journal
An integration strategy for mining product features and opinions
Proceedings of the 17th ACM conference on Information and knowledge management
AMAZING: A sentiment mining and retrieval system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
IEICE - Transactions on Information and Systems
A Holistic Approach to Product Review Summarization
STFSSD '09 Proceedings of the 2009 Software Technologies for Future Dependable Distributed Systems
Accessing Positive and Negative Online Opinions
UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services
UPAR7: a knowledge-based system for headline sentiment tagging
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
An opinion analysis system using domain-specific lexical knowledge
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Document sentiment classification by exploring description model of topical terms
Computer Speech and Language
Using pointwise mutual information to identify implicit features in customer reviews
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
A comparative study of feature selection and machine learning techniques for sentiment analysis
Proceedings of the 2012 ACM Research in Applied Computation Symposium
An artificial neural network based approach for sentiment analysis of opinionated text
Proceedings of the 2012 ACM Research in Applied Computation Symposium
A document-level sentiment analysis approach using artificial neural network and sentiment lexicons
ACM SIGAPP Applied Computing Review
Ontology-based sentiment analysis of twitter posts
Expert Systems with Applications: An International Journal
A boosted SVM based sentiment analysis approach for online opinionated text
Proceedings of the 2013 Research in Adaptive and Convergent Systems
A boosted SVM based ensemble classifier for sentiment analysis of online reviews
ACM SIGAPP Applied Computing Review
Hi-index | 12.05 |
The existing senti-lexicon does not sufficiently accommodate the sentiment word that is used in the restaurant review. Therefore, this thesis proposes a new senti-lexicon for the sentiment analysis of restaurant reviews. When classifying a review document as a positive sentiment and as a negative sentiment using the supervised learning algorithm, there is a tendency for the positive classification accuracy to appear up to approximately 10% higher than the negative classification accuracy. This creates a problem of decreasing the average accuracy when the accuracies of the two classes are expressed as an average value. In order to mitigate such problem, an improved Naive Bayes algorithm is proposed. The result of the experiment showed that when this algorithm was used and a unigrams+bigrams was used as the feature, the gap between the positive accuracy and the negative accuracy was narrowed to 3.6% compared to when the original Naive Bayes was used, and that the 28.5% gap was able to be narrowed compared to when SVM was used. Additionally, the use of this algorithm based on the senti-lexicon showed an accuracy that improved by a maximum of 10.2% in recall and a maximum of 26.2% in precision compared to when SVM was used, and by a maximum of 5.6% in recall and a maximum of 1.9% in precision compared to when Naive Bayes was used.