TRALSem: A Robust Model for Textual Sentiment Analysis

Sentiment analysis has gained widespread applications across various domains due to its versatility and practicality. With the increasing availability of data and advancements in machine learning technologies, its utilization is expected to continue expanding. Deep learning (DL)-based sentiment analysis has demonstrated high accuracy and efficiency in numerous application areas, such as marketing, customer service, politics, healthcare, and finance, thereby highlighting its high potential. Despite recent progress, DL-based sentiment analysis methods still face significant challenges, particularly concerning the robustness of sentiment classification and scoring. To address these issues, our study introduces TRALSem, a novel text-centered sentiment analysis framework designed to tackle the unique challenges of sentiment classification and scoring, with a particular focus on enhancing the overall robustness of the model. We conducted extensive experiments on multilingual script datasets, including Chinese and English scripts, as well as the IMDB and SST datasets. The experimental results show that TRALSem significantly outperforms existing state-of-the-art sentiment analysis methods. In terms of sentiment classification, it achieves remarkable improvements, far surpassing the previous benchmarks. More importantly, TRALSem substantially enhances the model’s robustness, enabling it to maintain stable performance even in the face of complex and noisy data. It also significantly reduces the model’s sensitivity to noise data, effectively filtering out interference and providing more reliable and accurate sentiment analysis results. Moreover, it offers better interpretability to users, making the sentiment analysis process and outcomes more understandable and actionable.