TSRB: Transformer-based Semantic Refinement Block for Sentiment Analysis using Scene Text Images

Abstract

Sentiment analysis is essential for several real-world applications, such as opinion mining and predicting a person's intent and personality. Most existing work aims to address challenges of sentiment analysis using normal text and images uploaded on social media. This work aims to use scene text images for sentiment analysis to assist in understanding the intentions of captured scenes. We present TSRB (Transformer-based Semantic Refinement Block), which comprises a multimodal approach and semantic gating. The proposed method constructs hierarchically fused image and text representations and then routes them through a TSRB and a learned three-way Semantic Gating module. The image branch encodes both the full meme image and text image extracted from the input image through a convolutional network with spatial attention; the text branch encodes OCR text, raw tweet text, and image captions via three independent Distil-BERT+CNN encoders and hierarchically fuses them. The resulting visual and textual embeddings are jointly refined by three stacked Transformer encoder layers within the proposed TSRB and then selectively blended by a softmax-weighted Semantic Gate that dynamically arbitrates among the post-attention, visual, and textual streams. Experiments are conducted on two standard datasets (MVSA-Single and Memotion) and compared with state-of-the-art models to demonstrate the effectiveness of the proposed method.