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
Symbolic representation of text documents
Proceedings of the Third Annual ACM Bangalore Conference
Automatically computed document dependent weighting factor facility for Naïve Bayes classification
Expert Systems with Applications: An International Journal
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Expert Systems with Applications: An International Journal
Cluster based symbolic representation and feature selection for text classification
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
A clustering study of a 7000 EU document inventory using MDS and SOM
Expert Systems with Applications: An International Journal
A symbolic approach for text classification based on dissimilarity measure
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
Dissimilarity based feature selection for text classification: a cluster based approach
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Text classification using symbolic similarity measure
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
An empirical study on various text classifiers
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Expert Systems with Applications: An International Journal
An efficient classification approach for large-scale mobile ubiquitous computing
Information Sciences: an International Journal
Nonparallel hyperplane support vector machine for binary classification problems
Information Sciences: an International Journal
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This work implements an enhanced hybrid classification method through the utilization of the naïve Bayes classifier and the Support Vector Machine (SVM). In this project, the Bayes formula was used to vectorize (as opposed to classify) a document according to a probability distribution reflecting the probable categories that the document may belong to. The Bayes formula gives a range of probabilities to which the document can be assigned according to a pre determined set of topics such as those found in the "20 newsgroups" dataset for instance. Using this probability distribution as the vectors to represent the document, the SVM can then be used to classify the documents on a multi - dimensional level. The effects of an inadvertent dimensionality reduction caused by classifying using only the highest probability using the naïve Bayes classifier can be overcome using the SVM by employing all the probability values associated with every category for each document. This method can be used for any dataset and shows a significant reduction in training time as compared to the LSquare method and significant improvement in classification accuracy when compared to pure naïve Bayes systems and also the TF-IDF/SVM hybrids.