SPECTRAL SENTIMENT ANALYSIS: UNVEILING RESTAURANT REVIEWS THROUGH SPECT-BASED TECHNIQUES

Аннотация

In the realm of sentiment analysis for restaurant reviews, the advent of Spectral Sentiment Analysis (SSA) techniques has opened new avenues for uncovering nuanced insights. This paper explores the application of SSA methodologies to analyze restaurant reviews, utilizing Spectral Clustering (SC) and Spectral Embedding (SE) techniques. By harnessing the spectral properties of the review data, SSA enables the detection of underlying sentiment patterns, facilitating more accurate sentiment classification. We present a comprehensive overview of SSA methodologies and demonstrate their efficacy through experimental evaluations on real-world restaurant review datasets. Our findings highlight the potential of SSA in enhancing sentiment analysis tasks and provide valuable insights for researchers and practitioners in the field of natural language processing and data analytics.