Sentiment Analysis (SA) is a crucial aspect of Natural Language Processing (NLP), focusing on identifying and interpreting subjective assessments in textual content. Syntactic parsing is useful in SA as it improves accuracy and provides explainability; however, it often becomes a computational bottleneck due to slow parsing algorithms. This article proposes a solution to this bottleneck by using a Sequence Labeling Syntactic Parser (SELSP) to integrate syntactic information into SA via a rule-based sentiment analysis pipeline. By reformulating dependency parsing as a sequence labeling task, we significantly improve the efficiency of syntax-based SA. SELSP is trained and evaluated on a ternary polarity classification task, demonstrating greater speed and accuracy compared to conventional parsers like Stanza and heuristic approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER). The combination of speed and accuracy makes SELSP especially attractive for sentiment analysis applications in both academic and industrial contexts. Moreover, we compare SELSP with Transformer-based models trained on a 5-label classification task. In addition, we evaluate multiple sentiment dictionaries with SELSP to determine which yields the best performance in polarity prediction. The results show that dictionaries accounting for polarity judgment variation outperform those that ignore it. Furthermore, we show that SELSP outperforms Transformer-based models in terms of speed for polarity prediction.

翻译：情感分析是自然语言处理的关键领域，专注于识别和解释文本内容中的主观评价。句法解析在情感分析中具有重要作用，既能提升分析准确性，又能提供可解释性；然而，由于解析算法速度较慢，它常常成为计算瓶颈。本文提出一种解决方案：通过基于规则的情感分析流程，使用序列标注句法解析器将句法信息整合到情感分析中。通过将依存句法解析重构为序列标注任务，我们显著提升了基于句法的情感分析效率。SELSP在三元极性分类任务上进行了训练和评估，与Stanza等传统解析器及VADER等启发式方法相比，展现出更快的速度和更高的准确度。这种速度与准确度的结合使得SELSP在学术和工业领域的情感分析应用中极具吸引力。此外，我们将SELSP与基于Transformer的模型在五标签分类任务上进行了对比，并评估了SELSP与多种情感词典的组合效果，以确定哪种词典在极性预测中性能最优。结果表明：考虑极性判断变化的词典性能优于忽略该因素的词典。最后，我们证明在极性预测速度方面，SELSP优于基于Transformer的模型。