Parsing Based Sarcasm Detection from Literal Language in Tweets

Objective: To investigate the impact of sarcasm in analyzing the sentiments from tweets. Design: 1. The Tweets related to five different domains are collected from the Twitter by creating Twitter developer account. 2. The Tweets are preprocessed in order to extract the features (Term Frequency, Entropy, Gain Ratio) from the Tweets. 3. Proposed an Iterative algorithm in updating the dictionary with Negative Phrases and sentiment words. 4. Assigned a polarity to each tweet using the Dictionary based approach. 5. Tweets with Zero scores are detected as Sarcasm tweets. 6. Analysis on Variance (AOV) Test is performed on the scores obtained. 7. Perform prediction on the scores using Machine Learning Algorithms. 8. Estimated the Mean Square Prediction Error (MSPE) using Cross Validation. Outcome: The impact of sarcasm on sentiment analysis is measured in terms of Precision, Recall and F-score. Results: 1. Scores of Tweets based on sentiment words and Negative Phrases. 2. Summary of Analysis on variance on Scores obtained. 3. Performance of Machine Learning Algorithms in Detecting the sarcasm from tweets. Conclusion: This paper has been presented to put forth the hypothesis that changes sentiment polarity (positive to negative) of sentences can be used as a feature for detecting sarcasm within product review length bodies of text. With the level of accuracy achieved, this scoring technique can be implemented as software for social networks. Also, these efforts may be a useful tool for learning about patterns in sarcasm or making better dictionaries of offensive words. Keywords: Sarcasm detection, machine learning algorithms, support vector machine, Twitter, analytics, RTextTools.