Such use of sarcasm also relies on the context which involves the presumption of commonsense and background knowledge of an event. Sarcasm Detection –Machine learning perspective • Variable Selection- This is a high dimensional data problem. The present study sought to investigate social-cognitive functions in a large, consecutive cohort of premanifest and manifest HD gene expansion carriers using tests assessing sarcasm detection, … This huge publicly available data can be used for research and a variety of applications. This makes the sarcasm detection task interesting and challenging at the same time. Sarcasm is an interesting problem for sentiment analysis because surface This research proposes sarcasm detection using deep learning in code-switch tweets, specifically the mash-up of English with Indian native language, Hindi. 2017). In a such way the main objective of irony and sarcasm detection is to discover features that allow us to discriminate ironic (or sarcastic) texts from nonironic (or nonsarcastic) texts.

The objective of our work is detection of satire in images taken from the popular photo-sharing platform-Flickr. The proposed model is a hybrid of bidirectional long short-term memory with a softmax attention layer and convolution neural network for real-time sarcasm detection. Data has around 8000 different characters/words which are variables but we have only 2694 records/ observations.

It even hard for humans to sometime detect sarcasm as it is subjective to the con-text, people, language, culture, situation, etc. OBJECTIVE Emotion recognition has been widely studied in Huntington disease (HD), but only a few studies have investigated more complex social cognition and, when so, exclusively in manifest HD. Computational detection of sarcasm has seen attention from the sentiment analysis community in the past few years. When such lexical cues are present in sentences, sarcasm detection can achieve high accuracy. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Objective of the Project Our primary goal is to analyze weather a tweet is sarcastic or not. Beginning with an approach that used speech-based features, automatic sarcasm detection has witnessed great interest from the sentiment analysis community. The second dictionary contains almost all words from sarcastic comments and frequent words from non-sarcastic comments.. A same sentence could be sarcastic or may not be sarcas- Automatic sarcasm detection is the task of predicting sarcasm in text. However, sarcasm is also expressed implicitly, i.e., without the use of any explicit lexical cues. Sarcasm is a speech act where speaker conveys the meaning implicitly.

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