Enhanced Sentiment Classification Using Smoothed Skeleton Sketching Attention and Reasoning Through Reverse Curriculum Reinforcement Learning

Sentiment classification is a critical task in Natural Language Processing (NLP), facilitating the extraction of sentiment information from textual data. Traditional models frequently exhibit limitations in accurately capturing contextual nuances and mitigating noise within lengthy sequences, which hampers their performance and generalizability. To overcome these challenges, this study proposes a novel framework that synergistically integrates the Smoothed Skeleton Sketching (S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>) attention mechanism with Reasoning through Reverse Curriculum Reinforcement Learning (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>). The S<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> mechanism emphasizes the identification and prioritization of salient features within the input data, thereby enhancing the model's focus on relevant information. Concurrently, the R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> framework optimizes the learning trajectory by systematically refining the model's sentiment understanding through a progressive and structured curriculum. The proposed method was rigorously implemented across diverse sentiment datasets, including TSB, Waimai, Weibo, and Amazon, utilizing Python alongside TensorFlow and PyTorch for computational efficiency. Experimental evaluations yielded a substantial performance improvement, with the model achieving an accuracy of 92.7%, precision of 91.5%, Recall of 93.2%, and an F1-score of 92.3% significantly surpassing existing sentiment classification techniques.