Source: https://www.ijcseonline.org/full_paper_view.php?paper_id=3455
Timestamp: 2019-04-22 22:13:32+00:00

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Copyright © R. Merlin Packiam, V. Sinthu Janita Prakash	. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
IEEE Style Citation: R. Merlin Packiam, V. Sinthu Janita Prakash, “Relevance Based Feature Selection Algorithm For Efficient Preprocessing of Textual Data Using HMM”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.1, pp.15-21, 2019.
MLA Style Citation: R. Merlin Packiam, V. Sinthu Janita Prakash "Relevance Based Feature Selection Algorithm For Efficient Preprocessing of Textual Data Using HMM." International Journal of Computer Sciences and Engineering 7.1 (2019): 15-21.
APA Style Citation: R. Merlin Packiam, V. Sinthu Janita Prakash, (2019). Relevance Based Feature Selection Algorithm For Efficient Preprocessing of Textual Data Using HMM. International Journal of Computer Sciences and Engineering, 7(1), 15-21.
With a rapid growth of the world of Internet, the social media is eventually growing and is playing a very major role in most of our lives. There are various social networking sites such as Twitter, Google+, Face book which provide a platform for the people to present themselves. Twitter is an efficient micro-blogging tool which has become very popular throughout the world. Nowadays, there is an ongoing trend of posting every thought and emotion of one’s life on these social networking sites. Due to this, emotion analysis has gained popularity in analyzing the thoughts, opinions, feelings, sentiments, etc., of various people. But handling such a huge amount of unstructured data is a tedious task to take up. Feature selection is the process of reducing the number of collected features to a relevant subset of features and is often used to combat the curse of dimensionality. This paper proposes a Relevance Feature Selection for efficient analytics on twitter data. After selecting the features from the tweets, Support Vector Machine (SVM) based classification is applied to analyze the data using Hidden Morkov Model(HMM). The performance of the proposed method has been evaluated through experiments. The entire research was evaluated through publicly available twitter data set with various metrics such as precision, recall, F-measure and Accuracy. By comparing the obtained results with the existing research results, the performance of the proposed work provides better result.
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