Sarcastic Sentiment Detection Based on Types of Sarcasm Occurring in 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 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.