Systemic Functional Linguistics Approach for Implicit Topic Sentiment Analysis

The identification of implicit topics within text data from online review platforms poses a significant challenge to existing sentiment analysis tools. This study proposes a novel sentiment analysis framework based on revised Support Vector Machine (SVM) mode. It integrates a multidimensional analysis of context, semantics, and lexical-grammatical features, and is enhanced with feature extraction methods optimized for implicit expressions. Experimental results confirm the effectiveness of this model in enhancing sentiment classification accuracy, particularly in dealing with texts that contain implicit topics.