Sentiment classification is crucial for understanding tourism reviews, focusing on subjective emotions (Chen et al., 2020; Krishnan et al., 2022; Momani et al., 2022). Traditional single-modal analysis misses important cues in text and images (Ye et al., 2022).
Single-Modality Sentiment Classification Methods
Sentiment classification is crucial for understanding tourism reviews, focusing on subjective emotions (Chen et al., 2020; Krishnan et al., 2022; Momani et al., 2022). Traditional single-modal analysis, which often examines only text or images, misses important emotional cues (Ye et al., 2022). This study introduces an advanced algorithm combining BERT and Text-CNN for text extraction, ResNet-51 for image extraction, and an attention mechanism to integrate multimodal data, enhancing sentiment prediction accuracy.
Single-modality sentiment analysis has been pivotal in analyzing text (Wang & Shin, 2019) and images (Rao et al., 2020), using statistical methods like term frequency and inverse document frequency (Puh & Bagić, 2023). Advances in pretrained models like BERT (Devlin et al., 2018), GPT-2 (Veyseh et al., 2021), and RoBERTa have improved text sentiment analysis by capturing complex language structures. BERT-CNN integrations (Abas et al., 2022) further enhance the capture of emotional nuances. Prompt learning (Liu et al., 2023) has facilitated few-shot learning and improved semantic comprehension.
Visual sentiment analysis initially relied on handcrafted features such as composition and texture (Machajdik & Hanbury, 2010) and concepts like balance and harmony (Zhao et al., 2014). Innovations like adjective noun pairs (ANPs; Borth et al., 2013) and their emotional implications (Li et al., 2018) have been significant. Recently, deep neural networks and attention mechanisms have improved visual sentiment analysis by focusing on emotionally significant image areas (Yang et al., 2021; You et al., 2017). Integrating visual and textual analyses into a multimodal approach promises enhanced precision (Yang et al., 2021; Zhang et al., 2022).
Despite progress, challenges remain, including a scarcity of annotated datasets and model efficiency issues. Further research is needed to refine these multimodal techniques and improve their practical applications.