A dynamic language model for speech recognition
HLT '91 Proceedings of the workshop on Speech and Natural Language
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
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
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
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
The utility of linguistic rules in opinion mining
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A holistic lexicon-based approach to opinion mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Expert Systems with Applications: An International Journal
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Scaling high-order character language models to gigabytes
Software '05 Proceedings of the Workshop on Software
Document-level sentiment classification: An empirical comparison between SVM and ANN
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
More people are buying products online and expressing their opinions on these products through online reviews. Sentiment analysis can be used to extract valuable information from reviews, and the results can benefit both consumers and manufacturers. This research shows a study which compares two well known machine learning algorithms namely, dynamic language model and naïve Bayes classifier. Experiments have been carried out to determine the consistency of results when the datasets are of different sizes and also the effect of a balanced or unbalanced dataset. The experimental results indicate that both the algorithms over a realistic unbalanced dataset can achieve better results than the balanced datasets commonly used in research.