A Framework to Determine Sarcastic Sentiments in Opinion Polls

Abstract

An examination of the expression of opinions and attitudes by social media and internet users toward a specific topic is what sentiment analysis is about. In diverse fields such as commerce, politics and education, it is predominantly utilized to aid decision making. Sarcasm delivers an implicit information that is opposite to what one positively declares or writes. It is often viewed as a witty language that expresses scorn, insult and reprimand in a hilarious manner. Sarcastic sentiments by people become a problem if they cannot be detected in a sentiment classification since that can affect judgment and decision making. Previous works have indicated that the detection of sarcasm during sentiment analysis can be a very tedious task and may consume a lot of time. To classify a sentiment into sarcastic or non-sarcastic, and determine the level of predicted accuracy and loss. By so doing, one can be able to investigate the significant impact and effect of sarcastic sentiment in an opinion poll through a theoretical and empirical experiment. The study reviews different approaches for detecting sarcasm in sentiment analysis and how they perform. Lastly, the study introduces a framework that will assist in the identification and classification of sarcastic sentiments in an opinion poll by classifying sentiments as sarcastic or non-sarcastic. A framework is introduced that comprises of three operators namely Cluster+Expert Judgement, Train & Validate and Classify & predict; that facilitates sentiment classification. Based on our empirical findings on state-of-the-act deep learning techniques such as BLSTM and sAtt-BLSTM, we employed a simple, efficient and straight forward approach by employing purely LSTM to produce the framework that helps in the determination of sarcastic sentiments. The driverless car dataset composed of the emergence of driverless cars being powered by Google is considered for the empirical analysis. We applied the X-means clustering algorithm with expert judgment to ensure the efficient labeling of the chosen unsupervised dataset into sarcastic or non-sarcastic classes. We also employed a broad-spectrum twitter dataset consisting of both sarcastic and non-sarcastic tweets. The performance of our model has been evaluated using performance measures such as recall, precision, accuracy and F1-score emerging from a generated confusion matrix. Our test result for the sarcastic statement using a sarcastic tweet has a prediction value of 0.998875 while that of the non-sarcastic tweet has a prediction value of 0.000055. The results provide an accurate, efficient, and reliable prediction based on the generated confusion matrix and loss figures derived. The result, therefore, indicates that though LSTM is relatively cheap as compared to BLSTM, it resulted in improved classification performance. Based on our empirical investigations through a thorough review, a framework has been introduced to classify sentiments as sarcastic or non-sarcastic. X-means clustering algorithm with an expert judgment approach has been applied to label extracted dataset without complete labels. A deep learning technique such as LSTM has been adopted because it is efficient but cheaper as compared to most state-of-the-art deep learning techniques. Keywords: Sentiment Analysis, Sarcasm, Classification Techniques, Long Short Term Memory, Opinion Poll