Sentiment topic sarcasm mixture model to distinguish sarcasm prevalent topics based on the sentiment bearing words in the tweets

Sentiment analysis as we all know is a developed field in which new features keeps on adding, but most of the time, on the internet people use sarcasm to convey their message which is very difficult to understand both by people and machines. Sarcastic statements are very complex as most of the time they sound in positive context if interpreted literally but actually the speaker mean the opposite of what they speak. Sarcasm detection is a subtask of opinion mining. The main intention behind sarcasm detection is to identify the user opinions or emotions expressed by the user in the written text. It plays a critical role in sentiment analysis by correctly identifying sarcastic or non sarcastic sentences. The sarcastic sentence has mixed polarity of both positive and negative words. Understanding sarcasm is quite a difficult and a challenging task even for humans as well as for machines. Various approaches for sarcasm detection are purely based on machine learning classifiers where training the classifier is based on simple lexical or dictionary based features. The objective of the work is to develop an unsupervised probabilistic relational model to identify sarcasm prevalent topics based on the sentiment distribution of the words in the tweets. The model estimates sentiment based topic level distribution. The model evaluation shows the sentiment associated words that do appear in the short text given the sentiment related label. The model outperforms the other baseline state of art Model for sarcasm detection as shown in the experimental result and it is very much suited for the prediction of sarcasm of a short tweet.