Academic Recommendation
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2016, Vol 3, Issue 9
Abstract
The proposed work is intended to develop a semi-automatic technique for classifying the sentiments based text. Basically in this presented work for text classification the decision trees are implemented which are the supervised learning algorithms. But the additional effort for tagging of data is necessary therefore that is known as the semi supervised model. The technique is applied on the online student’s learning and discussion to find the students experience and improve the experience in further learning processes. Therefore the entire system development is performed on two major modules first for generating the student’s communication data and then uses it with the supervised model for text classification according to the user emotions. In this technique the web data is first pre-processed for improving the quality of learning data. After that the data is used with the NLP parser for finding the communicated text features. The computed text features are than used with two different supervised learning models namely C4.5 and the ID3. These models are basically a kind of decision trees, during training of these algorithms the algorithm generates the tree. These generated decision are termed here as the trained model. The trained classifier is further used for real time classification of communicated text for the binary classification. The implementation of the entire text mining concept is performed using the JAVA technology and the classifiers performance is compared using the accuracy, error rate, memory consumption and time consumption. According to the computed classifiers performance the proposed technique namely c4.5 based classifier perform more accurate classification as compared to the traditional ID3
Authors and Affiliations
Shikha Agarwal
Keywords
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