Aspect Embeddings and Beyond: Enhancing Machine Learning for Aspect-Based Multilabel Sentiment Analysis

Abstract

Sentiment analysis has become a pivotal aspect of natural language processing, facilitating the understanding of opinions within text. Traditional sentiment analysis methods, however, face challenges in capturing nuanced sentiments related to specific aspects. This research delves into the realm of aspect-based multilabel sentiment analysis, leveraging the power of aspect embeddings. We explore the generation and application of aspect embeddings, surpassing conventional approaches. Our methodology involves training machine learning models on a curated dataset, with a focus on enhancing performance through aspect embeddings. Results demonstrate the efficacy of this approach, showcasing improved sentiment classification across multiple aspects. Furthermore, we go "beyond" by discussing potential enhancements such as contextual information integration and deep learning techniques. This research not only contributes to the refinement of sentiment analysis methodologies but also lays the groundwork for future advancements in understanding nuanced opinions within diverse contexts.