Garg, Sumit Kumar and Meher, Ronak Kumar (2015) Naive Bayes Model with Improved Negation Handling and N-Gram Method for Sentiment Classification. BTech thesis.
Sentiment classification is turning into one of the most fundamental research areas for prediction and classification. In Sentiment mining, we basically try to analyse the results and predict outcomes that are based on customer feedback or opinions. Some work has been done to increase the accuracy of the Naive Bayes classifier. In this project we have examined different methods of improvising the accuracy and space of a Naive Bayes classifier for sentiment classification. We have used a modified negation handling method using POS tagging to decrease the number of feature in the feature set and also discovered that combining these with n-gram method results in improvement in the accuracy. So, a more accurate sentiment classifier with less space complexity can be built from Naive Bayes Classifier.
|Item Type:||Thesis (BTech)|
|Uncontrolled Keywords:||Sentiment Analysis, Naive Bayes Classifier, n-Gram, Negation Handling, POS Tagging|
|Subjects:||Engineering and Technology > Computer and Information Science > Data Mining|
|Divisions:||Engineering and Technology > Department of Computer Science|
|Deposited By:||Mr. Sanat Kumar Behera|
|Deposited On:||16 Sep 2016 16:51|
|Last Modified:||16 Sep 2016 16:51|
|Supervisor(s):||Babu, K S|
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