The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Support Vector Machine Ensemble with Bagging
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th 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
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
An empirical study of sentiment analysis for chinese documents
Expert Systems with Applications: An International Journal
Lexical Affect Sensing: Are Affect Dictionaries Necessary to Analyze Affect?
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Expert Systems with Applications: An International Journal
Comparative experiments on sentiment classification for online product reviews
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Get out the vote: determining support or opposition from congressional floor-debate transcripts
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Seeing several stars: a rating inference task for a document containing several evaluation criteria
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A comparison of sentiment analysis techniques: polarizing movie blogs
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
A study of information retrieval weighting schemes for sentiment analysis
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Ensemble of feature sets and classification algorithms for sentiment classification
Information Sciences: an International Journal
Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews
Expert Systems with Applications: An International Journal
Selective SVMs ensemble driven by immune clonal algorithm
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Survey on mining subjective data on the web
Data Mining and Knowledge Discovery
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
A comparative study of feature selection and machine learning techniques for sentiment analysis
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
| Hi-index | 0.00 |
In recent years, several approaches have been proposed for sentiment based classification of online text. Out of the different contemporary approaches, supervised machine learning techniques like Naive Bayes (NB) and Support Vector Machines (SVM) are found to be very effective, as reported in literature. However, some studies have reported that the conditional independence assumption of NB makes feature selection a crucial problem. Moreover, SVM also suffers from other issues like selection of kernel functions, skewed vector spaces and heterogeneity in the training examples. In this paper, we propose a hybrid method by integrating "weak" support vector machine classifiers using boosting techniques. The proposed model exploits classification performance of Boosting while using SVM as the base classifier, applied for sentiment based classification of online reviews. The results on movies and hotel review corpora of 2000 reviews have shown that the proposed approach has succeeded in improving the performance of SVM. The resultant ensemble classifier has performed better than the single base SVM classifier, and the results confirm that ensemble SVM with boosting, significantly outperforms single SVM in terms of accuracy.