Sentimental analysis using fuzzy and naive bayes

Sentimental Analysis is the best way to judge people's opinion regarding a particular post. In this paper we present analysis for sentiment behavior of Twitter data. The proposed work utilizes the naive Bayes and fuzzy Classifier to classify Tweets into positive, negative or neural behavior of...

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Bibliographic Details
Published in:	2017 International Conference on Computing Methodologies and Communication (ICCMC) pp. 945 - 950
Main Authors:	Mehra, Ruchi, Bedi, Mandeep Kaur, Singh, Gagandeep, Arora, Raman, Bala, Tannu, Saxena, Sunny
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-07-2017
Subjects:	Artificial Intelligence Behaviour Analysis classification Conferences Data mining deep learning Facebook machine learning Natural language processing neural networks opinion mining Sentiment analysis Task analysis Twitter
Online Access:	Get full text
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Summary:	Sentimental Analysis is the best way to judge people's opinion regarding a particular post. In this paper we present analysis for sentiment behavior of Twitter data. The proposed work utilizes the naive Bayes and fuzzy Classifier to classify Tweets into positive, negative or neural behavior of a particular person. We present experimental evaluation of our dataset and classification results which proved that combined proposed method is more efficient in terms of Accuracy, Precision and Recall.
DOI:	10.1109/ICCMC.2017.8282607