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Published in:

Volume 7 Issue 11
November-2020
eISSN: 2349-5162

Unique Identifier

JETIR2011140

Page Number

64-70

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Title

Sentiment Analysis of Tweets using SVM and Maximum Entropy

ISSN

2349-5162

Cite This Article

"Sentiment Analysis of Tweets using SVM and Maximum Entropy ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.64-70, November-2020, Available :http://www.jetir.org/papers/JETIR2011140.pdf

Abstract

: Sentiment analysis is the computational learning of masses' opinions. Sentiment analysis will classify the text in a sentence or document to find out the opinions expressed in the sentence or document, which can be positive or negative. This scheme discusses the sentiment of someone on Twitter towards public figures. The data used is in the form of tweet data with the keywords "#NEP" or "National Education Policy". Tweets obtained are then processed by doing text preprocessing and then classified using the support vector machine algorithm with maximum entropy. The performance of this algorithm uses and obtained a precision of 73.33%, the accuracy of 77.47%, recall of 87.76%, and f-measure of 79.66% respectively.

Key Words

Sentiment Analysis, Opinion Mining, Data Mining, Machine Learning, Support Vector Machin, Maximum Entropy

Cite This Article

"Sentiment Analysis of Tweets using SVM and Maximum Entropy ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp64-70, November-2020, Available at : http://www.jetir.org/papers/JETIR2011140.pdf

Publication Details

Published Paper ID: JETIR2011140
Registration ID: 302191
Published In: Volume 7 | Issue 11 | Year November-2020
DOI (Digital Object Identifier):
Page No: 64-70
ISSN Number: 2349-5162

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Cite This Article

"Sentiment Analysis of Tweets using SVM and Maximum Entropy ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp64-70, November-2020, Available at : http://www.jetir.org/papers/JETIR2011140.pdf




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