Design of an Efficient Model for Sentiment Analysis on Twitter Using Between Fine-Tuning, Multimodal Integration, and Attention Mechanisms

For sentiment analysis of Twitter data, a robust methodology that can effectively catch the nuances of informal language and multimodal information is essential. Previous works, though well intended, generally fail to capture the characteristics of Twitter discourse and integrate various modalities. This paper introduces a new framework for sentiment analysis using BERTweet fine-tuning, multimodal integration, and attention mechanisms for the task of sentiment analysis. BERTweet-FT would fine-tune the pre-trained BERTweet model for Twitter data, so it can capture the sentiment-related features within a tweet. Further, MM-BERTweet can leverage the textual content with additional modalities like images and user metadata, leading to rich contextual representations, which in turn provide accurate sentiment predictions. Furthermore, AGBERTweet can even add enhancements to sentiment analysis by using attention mechanisms to pick out salient words or phrases crucial for sentiment classification. Proposed Framework: Our proposed framework offers several advantages over the existing state-of-the-art baseline approaches: it improves the accuracy of sentiment classification by 5% to 12% over different ablation components. By using Twitter-specific language comprehension, multimodal information, and attention mechanisms, Our method improves Twitter data sample sentiment analysis performance. Effect of the Work: This work will have an impact on a variety of applications, where precise sentiment analysis is essential for decision-making processes, such as social media marketing strategies, public opinion analysis, and brand sentiment monitoring. By improving sentiment analysis methods specifically designed for Twitter data, this work attempts to address important issues and creates new opportunities for future study in this field.