A Fair and Comprehensive Comparison of Multimodal Tweet Sentiment Analysis Methods
This work provides a standardized comparison framework for researchers in multimodal sentiment analysis, though it is incremental as it focuses on evaluation rather than new methods.
The authors conducted a comprehensive experimental comparison of six state-of-the-art multimodal sentiment analysis methods for tweets, introducing a reproducible and fair evaluation scheme on two benchmark datasets.
Opinion and sentiment analysis is a vital task to characterize subjective information in social media posts. In this paper, we present a comprehensive experimental evaluation and comparison with six state-of-the-art methods, from which we have re-implemented one of them. In addition, we investigate different textual and visual feature embeddings that cover different aspects of the content, as well as the recently introduced multimodal CLIP embeddings. Experimental results are presented for two different publicly available benchmark datasets of tweets and corresponding images. In contrast to the evaluation methodology of previous work, we introduce a reproducible and fair evaluation scheme to make results comparable. Finally, we conduct an error analysis to outline the limitations of the methods and possibilities for the future work.