Abstract: Social Media is a popular medium of communication amongst youngsters to remain connected with their friends. Facebook is one of the most preferred Social Media Sites which store the gigantic amount of data which can be explored for Sentiment Analysis. In this study, we have applied hybrid analysis approach which combines the best features of a lexical analysis and SVM machine learning classification algorithm on Facebook Posts. The analysis is further improved by incorporating language discourse features to detect intensity of sentiment and the prominent emotions expressed through these posts.
Objectives: With the growth of Social Media and the increasing use of English-Hindi (Hinglish) in linguistically diverse countries such as India, it is becoming increasingly important to analyse Hinglish-language content on Social Media platforms like Facebook. Prior sentiment and emotion analyses have only focused on single-label classification, ignoring the possibility of coexisting emotions within one instance. By analysing code-mixed Facebook status updates, the study aims to investigate multiple emotions. Method: 15,995 English-Hindi mixed Facebook status updates are annotated with emotions like joy, sadness, anger, fear, trust, disgust, surprise, anticipation, and love. Different pre-processing techniques are used to normalize the noisy data to produce more accurate results. We apply five different multi-level classification algorithms with word-level and character n-gram approaches to test the best classification results. Findings: The results of the experiment indicate that a status update can evoke multiple emotions rather than just one. Precision, recall, F1 score, and accuracy using both Micro and Macro averaging are used to evaluate the performance of different classifiers. As compared to other classification algorithms, the Classifier Chains algorithm with its 2-6-gram approach has the highest accuracy of 86% with a precision of 0.98. As compared to other classifiers, the Classifier Chains algorithm offered better results due to its ability to consider the correlations between class labels. Applications: The article focuses on the multi-label emotion classification task, which examines whether a Facebook status update shows none, one, or more of the nine emotions as outlined by Plutchik's wheel of emotions. Considering the emotion of a text can support decision-making processes in various ways.
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