SOUL : Towards Sentiment and Opinion Understanding of Language (2023)

Abstract: Sentiment analysis is a well-established natural language processing task, with sentiment polarity classification being one of its most popular and representative tasks. However, despite the success of pre-trained language models in this area, they often fall short of capturing the broader complexities of sentiment analysis. To address this issue, we propose a new task called Sentiment and Opinion Understanding of Language (SOUL). SOUL aims to evaluate sentiment understanding through two subtasks: Review Comprehension (RC) and Justification Generation (JG). RC seeks to validate statements that focus on subjective information based on a review text, while JG requires models to provide explanations for their sentiment predictions. To enable comprehensive evaluation, we annotate a new dataset comprising 15,028 statements from 3,638 reviews. Experimental results indicate that SOUL is a challenging task for both small and large language models, with a performance gap of up to 27% when compared to human performance. Furthermore, evaluations conducted with both human experts and GPT-4 highlight the limitations of the small language model in generating reasoning-based justifications. These findings underscore the challenging nature of the SOUL task for existing models, emphasizing the need for further advancements in sentiment analysis to address its complexities. The new dataset and code are available at https://github.com/DAMO-NLP-SG/SOUL .

## introduction sentiment analysis, a well-established natural language processing task, aims to analyze and understand subjective information from text @xcite . one of its most popular and representative tasks is sentiment classification (sc), which involves review: … as if the raptor graphics weren't amazing enough, the award-winning editors continued to use the exact same shot throughout the entire movie, even when the background didn't actually match up with the setting of the scene. wow, what genius. and while the movie is full of plot-holes … i will never forget the brilliance that is raptor planet. thank you sci-fi for another classic. ## conclusion this paper introduces a novel task called sentiment and opinion understanding of language (soul), including two subtasks: review comprehension and justification generation. our experimental results show that soul is a challenging task that demands a deep understanding of sentiment, with a performance gap of up to 27% when compared to human performance. moreover, evaluations conducted with both human experts and gpt-4 demonstrate model training data rc accuracy nli-roberta mnli & snli 55.02 roberta soul 75.49 the weakness of slms in generating reasoningbased justifications while showcasing chatgpt's powerful sentiment understanding ability. nevertheless, there is still scope for enhancing the overall accuracy, originality, and conciseness of chat-gpt's responses.