LAMBDA: Lexicon and Aspect-Based Multimodal Data Analysis of Tweet

Sentiment analysis (SA) is widely employed across various domains, including government policy directives, corporate customer and staff satisfaction monitoring, political analysis, and public tension monitoring in security structures.However, new difficulties for sentiment analysis algorithms have emerged with the advent of highly unstructured manifestations of emotion in online social media platforms.To address this, we propose an enhanced approach that combines lexicon-based analysis and aspect-based sentiment analysis for tweets.In this study pre-processing step allows us to handle the lack of syntactic and grammatical structure commonly found in social media text.Moreover, acknowledging the multimodal characteristics of social media data, encompassing audio, visuals, and videos, our methodology expands to encompass sentiment analysis of text derived from these diverse modalities.Our approach classifies the content as positive, negative, or neutral, utilizing both the lexicon-based approach and the aspect-based sentiment analysis.By combining these techniques, we aim to capture both general sentiment tendencies and aspect-specific sentiments within the text data.We evaluate the efficacy of our proposed approach using the STS-Gold datasets.Text data achieved the highest sentiment accuracy at 92.83%, followed by audio, image, and video data with accuracies of 91.11%, 87.8%, and 86.67%, respectively by using LAMBDA approach.The results highlight the effectiveness of our approach compared to other state-of-the-art research studies.