S2SA : an innovative dataset for sentiment evaluation across several levels and domains

A key component of natural language processing (NLP) is sentiment analysis, which helps analyse human emotions from a variety of textual data sources. This study presents a novel dataset intended to assess sentiment across many domains at the text, aspect, and sequence levels. The strengths and shortcomings of three deep learning models—BERT (Bidirectional Encoder Representations from Transformers) for document-level sentiment analysis, GCN (Graph Convolutional Networks) for aspect-level sentiment analysis, and BiLSTM (Bidirectional Long Short-Term Memory) for sequence-level sentiment analysis—in sentiment categorization are evaluated. Analysing these models' performance in various sentiment classification tasks, investigating their contextual comprehension, and assessing their applicability for different applications will be the main g oals of this study. The study will shed light on how successful each strategy is and suggest ways to improve sentiment analysis methods. This study adds to the expanding field of sentiment analysis by offering a multifaceted method for assessing sentiment, which has important uses in social media monitoring, market research, and customer feedback analysis.