International Journal of Computer Applications (0975 – 8887) Volume 166 – No.9, May 2017 39 Review Paper: Sarcasm Detection and Observing User Behavioral Pooja Deshmukh Student of ME (CSE) Department of Computer Science and Engineering Deogiri Institute of Engineering and Management Studies, Aurangabad Sarika Solanke Assistant Professor Department of Computer Science and Engineering Deogiri Institute of Engineering and Management Studies, Aurangabad ABSTRACT Sarcasm is a sort of sentiment where public expresses their negative emotions using positive word within the text. It is very tough 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 modelling 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. General Terms Sarcasm Detection, Patter Based Approach, User Behavioral modeling. Keywords Sarcasm, Sentiment, SVM, KNN 1. INTRODUCTION Social net-working websites have become a popular platform for users to express their feelings and opinions on various topics, such as events, or products. Social media channels have become a popular platform to discuss ideas and to interact with people worldwide area. Twitter is also important social media network for people to express their feelings, opinions, and thoughts. Users post more than 340 million tweets and 1.6 billion search queries every day [1] [2]. Twitter is a social media platform where users post their views of everyday life. Many organizations and companies have been interested in these data for the purpose of studying the opinion of people regards the political events, popular products or Movies. When a particular product is launched, people start tweeting, writing reviews, posting comments, etc. on social media such as twitter. People turn to social media network to read the comments, and reviews from other users about a product before they decide whether to purchase or not. If the user review is good for the particular products then the users are buy the product otherwise not. Organizations are also depends on these sites to know the response of users for their products and use the user feedback to improve their products [3]. Sentiment analysis is the opinion of the user for the particular things.Sentiment analysis is the extraction of feeling from any communication (verbal/non verbal).Two ways to express sentiment analysis. 1) Explicit sentiments: Direct expression of the opinion about the subject shows the presence of explicit sentiment. 2) Implicit sentiments: Whenever any sentence implies an opinion then such sentence shows the Presence of implicit sentiment (Indirect expression). Sentiment analysis and opinion mining depends on emotional words in a text to check its polarity (i.e., whether it deals positively or negatively with its theme) [4].Sarcasm is a type of sentiment where people express their negative feelings using positive word in the text. The example of this is “I love the pain of breakup”. The love is the positive words but it express the negative feeling, such as breakup in this example.It is usually used to transfer implicit information within the message a person transmits. It is hard even for humans to recognize. Used Pattern based approach for detecting sarcasm on twitter.The definition of sarcasm is the activity of saying or writing the opposite of what you mean, or of speaking in a way intended to make someone else feel stupid or show them that you are angry. 2. LITERATURE REVIEW In [3], authors show the interest in sarcasm delectation in the tweeter. For capturing real time tweets they use the Hadoop base framework, and processes that tweets they used the different six algorithms such as parsing based lexicon generation algorithm (PBLGA), tweets contradicting with universal facts (TCUF), interjection word start (IWS), positive sentiment with antonym pair (PSWAP), Tweets contradicting with time-dependent facts (TCTDF), Likes dislikes contradiction (LDC), these algorithm are used identifies sarcastic sentiment effectively. This method is more suitable for real time streaming tweets. In [4], authors use the computational system it is use for harnesses context incongruity as a basis for sarcasm detection. Sarcasm classifier uses four types of features: lexical, pragmatic, explicit incongruity, and implicit incongruity features. They evaluate system on two text forms: tweets and discussion forum posts. For improvement of performance of tweet uses the rule base algorithm, and to improve the performance for discussion forum posts, uses the novel approach to use elicitor posts for sarcasm detection. This system also introduces error analysis, the system future work (a) role of numbers for sarcasm, and (b) situations with subjective sentiment. In [5], authors used the machine learning approach to sarcasm detection on Twitter in two languages English and Czech. First work is sarcasm detection on Czech language. They used the two classifier Maximum Entropy (MaxEnt) and Support Vector Machine (SVM) with different combinations of features on both the Czech and English datasets. Also use the different preprocessing technique such as Tokenizing, POS- tagging, No stemming and Removing stop words, its use for finding the issue of Czech language.