Sarcastic Sentiment Detection Based on Types of ...

International Journal on Semantic Web and Information Systems Volume 13 • Issue 4 • October-December 2017

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 Sanjay Kumar Jena, Department of Computer Science, National Institute of Technology Rourkela, Rourkela, India

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. Keywords NLP, Opinion Mining, Parsing, POS Tagging, Sarcasm, Sentiment Analysis, Tweets, Twitter Data

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 DOI: 10.4018/IJSWIS.2017100105  Copyright © 2017, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 

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International Journal on Semantic Web and Information Systems Volume 13 • Issue 4 • October-December 2017

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. The author has already discussed sarcasm type T1, T2 and T3 in their previous article (Bharti, 2015). The remaining types of sarcastic tweets are as follows: •

•

•

•

Type T4 depicts the users’ likes and dislikes behavior while posting the tweets on Twitter. To learn the behavioral habit of a particular Twitter user, one can analyze and observe his likes and dislikes habit. Using these likes and dislikes lists of a particular user, one can analyze the sarcastic tweets from the particular user’s account. For example, if any Sachin Tendulkar fan who likes to post the tweet about Sachin and his Twitter account consist a tweet like “I love to see Sachin’s failure in batting” which contradict his like’s habit so, one can be easily identify that given tweet is sarcastic; Type T5 says about tweets contradicting universal facts. For example, if any user has posted a tweet “sun is revolving around the earth” and the fact is earth revolves around the sun. In such cases, user is intentionally negating the universal facts in their tweet, and there is a high probability that the tweet is sarcastic; Type T6 says about tweets that contradict temporal facts, which may change over a period. For example, “India had an amazing win in cricket world cup 2015 final”, while the fact is Australia won the cricket world cup in 2015 and India won the cricket world cup in 2011. If this type of contradiction occurs, then the tweet will be sarcastic; Type T7 discuss about the positive tweets that contains an antonym pair of either adjective, verb, noun or adverb. For example, “when I am online, she is offline, and when she is online, I am offline, and I love this game so much” #sarcasm. In this example, online is an adjective and offline is the antonym of online. If any tweet contains an antonym pair of either adjective or adverb or verb and overall tweet sentiment is positive, then the tweet has high chance to be classified as sarcastic.

Table 1. Various types of sarcastic tweets T1

Sarcasm as a contradiction between positive sentiment and negative situation.

T2

Sarcasm as a contradiction between negative sentiment and positive situation.

T3

Tweets that starts with interjection word.

T4

Sarcasm as a contradiction between likes and dislikes.

T5

Sarcasm as a contradiction between tweet and the universal facts.

T6

Sarcasm as a contradiction between tweet and its temporal facts.

T7

Positive tweet that contains a word and its antonym pair.

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