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World Journal of Engineering
Research and Technology

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.218

ICV : 79.45

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  • Article Invited for Publication

    Article are invited for publication in WJERT Coming Issue

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    Its Our pleasure to inform you that, WJERT 1 January 2019 Issue has been Published, Kindly check it on

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    Its our Pleasure to Inform you that WJERT Impact Factor has been increased from 4.236 to 5.218 due to high quality Publication at International Level

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    WJERT Rank with Index Copernicus Value 79.45 due to high reputation at International Level


    JANUARY 2019 Issue has been successfully launched on 1 January 2019




S. Maheswari* and Dr. R. Anbuselvi


Sentiment Analysis is a technique to identify people?s opinion, attitude, sentiment, and emotion towards any specific target such as individuals, events, topics, product, organizations, services etc. Sarcasm is a special kind of sentiment that comprise of words which mean the opposite of what you really want to say. Sarcasm is a sort of sentiment where public expresses their negative emotions using positive word within the text. It is very hard for humans to acknowledge. In this way we show the interest in sarcasm detection of social media text, particularly in tweets. In this paper we study new method pattern based approach for sarcasm detection, and also used behavioral modeling approach for effective sarcasm detection by analyzing the content of tweets however by conjoint exploiting the activity traits of users derived from their past activities. By using the various classifiers such as Random Forest, Support Vector Machine (SVM), k Nearest Neighbors (k- NN) and Maximum Entropy, we check the accuracy and performance.

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