Social Media Sentiment Analysis with Multi-Token Concatenated Embedding and Semantixpert Probabilistic Classifier for Fake News Detection
Keywords:
Byte Pair Encoding, Sentiment analysis, polarity prediction, fake news detection, social media.Abstract
In the field of false news identification, sentiment analysis is an effective strategy for enhancing detection accuracy. Sentiment analysis is critical for understanding user emotions and intents, but traditional methods frequently neglect the complex interplay between text and emojis, particularly in recognizing sarcasm. To address this limitation, a novel approach, the "Multi-Token Concatenated Embedding and SemantiXpert Probabilistic Classifier" is proposed which is aimed at improving sarcasm detection, polarity prediction accuracy, and overall sentiment prediction accuracy. The existing sentiment analysis approaches fail to capture the indirect interaction between text and emojis, resulting in the loss of crucial sentiment cues and misunderstanding of sarcasm in news content. Hence, Multi-Token Concatenated Embedding approach is introduced, which includes Multi-Run Byte Pair Encoding (MR-BPE) to capture sub-word patterns and the Concat-ViLT model to encapsulate the textual representation of emojis, thus enhancing the model's ability to understand the multimodal context of text-emoji pairs. Moreover, existing semantic classification methods are based on lexical patterns and mean-zero feature assumptions, which leads to misclassifications and lower accuracy in recognizing the genuine emotional tone of mixed sentiment comments. For semantic classification, a SemantiXpert Probabilistic Classifier is proposed which includes a Contrastive Semantic Clause Filter Network to understand user intentions and domain context and a Polarized Probabilistic Classifier is utilized to enhance the polarity prediction thereby reducing misclassifications and improving accuracy in sentiment prediction based fake news detection. As a result, the proposed model outperforms existing methods, achieving higher accuracy, sensitivity, and AUC values.
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