Sarcastic Sentiment Detection Based on Types of Sarcasm Occurring in Twitter Data

Sarcastic Sentiment Detection Based on Types of Sarcasm Occurring in Twitter Data

Santosh Kumar Bharti (Department of Computer Science, National Institute of Technology Rourkela, Rourkela, India), Ramkrushna Pradhan (Department of Computer Science, National Institute of Technology Rourkela, Rourkela, India), Korra Sathya Babu (Department of Computer Science, National Institute of Technology Rourkela, Rourkela, India) and Sanjay Kumar Jena (Department of Computer Science, National Institute of Technology Rourkela, Rourkela, India)
Copyright: © 2017 |Pages: 20
DOI: 10.4018/IJSWIS.2017100105
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Abstract

In Natural Language Processing (NLP), sarcasm analysis in the text is considered as the most challenging task. It has been broadly researched in recent years. The property of sarcasm that makes it harder to detect is the gap between the literal and its intended meaning. It is a particular kind of sentiment which is capable of flipping the entire sense of a text. Sarcasm is often expressed verbally through the use of high pitch with heavy tonal stress. The other clues of sarcasm are the usage of various gestures such as gently sloping of eyes, hands movements, shaking heads, etc. However, the appearances of these clues for sarcasm are absent in textual data which makes the detection of sarcasm dependent upon several other factors. In this article, six algorithms were proposed to analyze the sarcasm in tweets of Twitter. These algorithms are based on the possible occurrences of sarcasm in tweets. Finally, the experimental results of the proposed algorithms were compared with some of the existing state-of-the-art.
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Introduction

Sentiment analysis is a procedure to extract the attitude of a speaker or a writer concerning some target (Pang, 2002, pp. 79-86). Social media and social networking have fueled the online space, including Facebook, Amazon, Twitter, etc. as ratings, reviews, comments, etc. are everywhere. Social media content is growing rapidly with every passing day. With the large volumes of information generating daily, identifying clear and most consumer reliable information about their preferences has become very tough task nowadays. To get the correct and trusted information, individuals are showing interest towards analysis of social networking content. For every business, online reviews have become the deciding factor which can break or make a product in the marketplace. Sentiment analyzer is used as a tool to understand how consumers are reacting to an event or a new product through auditing social networking posts and comments.

Sarcasm is derived from the French word “Sarcasmor” that means “tear flesh” or “grind the teeth”. In simple words, sarcasm is a way to speak bitterly. Macmillan Dictionary defines, “sarcasm is an 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” (Macmillan, 2007). For example: “it feels great being bored”. In this example, the literal meaning of the sentence is different than what the speaker intends to say using sarcasm. While writing sarcasm in text, people often use only positive words to convey a negative opinion instead of real negative. However, the average human reader face problem in sarcasm detection for social media content such as tweets, reviews, blogs, online forums, etc.

In this paper, the tweets of Twitter are used as the dataset for sarcasm detection. Twitter is a microblogging social networking site where a user can read and post the messages. The Twitter allow posting a message of a limited length of 140 characters. Due to the limitations, the users often use symbolic and figurative texts to express their feelings such as @username, smilies, emoji, exclamation mark and interjection words. Recognition of these symbolic and figurative texts in tweets are the most arduous task in NLP. In the realm of Twitter, the author has observed several types of sarcastic tweets as shown in Table 1 that occurred frequently.

Table 1.
Various types of sarcastic tweets
T1Sarcasm as a contradiction between positive sentiment and negative situation.
T2Sarcasm as a contradiction between negative sentiment and positive situation.
T3Tweets that starts with interjection word.
T4Sarcasm as a contradiction between likes and dislikes.
T5Sarcasm as a contradiction between tweet and the universal facts.
T6Sarcasm as a contradiction between tweet and its temporal facts.
T7Positive tweet that contains a word and its antonym pair.

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