Abstract
Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.
Highlights
The advancement in computer technology and the World-Wide Web (WWW) has brought about growth in social media platforms such as Facebook, Instagram, Myspace, and Twitter to get connected with friends and families [1]
The results show that the random forest classifier attained the highest performance precision, with 83.5% over all the classifiers
The results show that it outperformed other classifiers in terms of f-measure, recall, and accuracy
Summary
The advancement in computer technology and the World-Wide Web (WWW) has brought about growth in social media platforms such as Facebook, Instagram, Myspace, and Twitter to get connected with friends and families [1]. The process of identifying people’s opinions (sentiments) about products, politics, services, or individuals brings a lot of benefits to the organizations [2,3]. It has become an important step in analyzing people’s sentiment [4,5]. Sarcasm is extremely contextual and topic reliant, and as a result, some contextual clues and shifts in polarity sentiment can assist in sarcasm identification in a text by determining the obscurity of the meaning and improving the overall sentiment classification of a large volume of user’s textual data obtained from social media. The predictive performance of the sentiment classification will rely on context vector and learning algorithms to guarantee the reliability of the overall classification
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