Sarcasm Detection on Tweets: Ensemble Approach
Rupali Amit Bagate, Ramadass Suguna · INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING · 2022
In the era of web 2.0 social media data is getting generated at huge amount of value to different organizations. Twitter is one of the platforms where different communities express their opinion on social media platform. These different opinion leads to tremendous amount information to work on such as opinion mining, sentiment analysis, sarcasm detection on various social media platform. Sentiment analysis is a natural language processing where author find contextual meaning of text to identify the sentiment of people on digital platform. Sentiment analysis is study of people’s behaviour. Now a day’s people use Sarcasm in many forms to convey their feelings on many social media platforms. Therefore, Sarcasm detection which is sub branch of sentiment analysis becomes a challenging job. Our research is broadly focusing on sarcasm detection of tweets using different machine learning model and neural network model. To enhance the result of classification author have used ensemble model such as staking. Proposed model is inputted with training dataset of tweeter which approximately divided into 70% into training and 30 % into testing dataset. Ensemble model is trained on cleaned dataset and our model is generating an acceptable accuracy which can classify the sarcastic tweets accurately.