SyntaPulse: An unsupervised framework for sentiment annotation and semantic topic extraction

Sentiment analysis is a critical area within natural language processing, with applications in various domains like marketing, social media analytics, and politics. However, current methods encounter challenges in handling contextual ambiguities, accurately detecting sarcasm and irony, and effectively processing domain-specific vocabulary without extensive labeled datasets. Addressing these issues is essential, as the nuanced nature of language can lead to diverse interpretations across contexts, complicating reliable sentiment analysis. Furthermore, sarcasm and irony remain difficult to identify precisely, while reliance on labeled data and limitations in handling domain-specific vocabulary restrict adaptability across different fields. This paper presents SyntaPulse, a novel framework for sentiment classification in social networks, developed to overcome these challenges. The framework combines an innovative dictionary-based approach with Probabilistic Syntactic Latent Semantic Analysis (PSLSA) for semantic topic extraction. This integration enables it to handle homographs effectively, thereby enhancing sarcasm detection, facilitating the interpretation of domain-specific vocabulary, and reducing dependency on labeled data. Evaluated on 12 datasets, our framework demonstrates adaptability across various domains and achieves high Macro-F1 scores, ranging from 72.89 % to 96.22 %. SyntaPulse has also obtained improvements on seven datasets, with the lowest improvement rate being 0.21 % and the highest reaching 2.97 %.