An Aspect based Multi‑label Sentiment Analysis using Improved BERT System
Keywords:Aspect Based Sentiment Analysis (ABSA), Text classification, multilabel classifier, Dimensionality reduction, Bidirectional Encode Representation from Transformers (BERT), Artificial Intelligence
Digital interaction has become more prevalent as a result of the increasing development of social Media and Web, making the customer active players. Customers' reviews uploaded on the Internet today provide crucial data towards other clients due to the vast quantity of reviews provided by consumers today. Since this type of information is extremely important for decision-making, it is extremely popular among internet users. Because of this, an automation system to analyses and retrieve insight from textual data is required. Sentiment classification is a well-known sub-area of Artificial Intelligence and Natural Language Processing that studies how people feel (NLP). The sentiments of participants in previous studies were calculated without taking into account the aspects indicated in a reviewing instances. In recent years, scholars have become interested in aspect-based sentiment analysis (ABSA). Numerous existing systems treat ABSA as if it were a single-label classification problem. This issue is addressed in this paper by presenting ways that make use of multilabeling classifiers for classification, which overcomes the problem. So rather than single label classifiers, the suggested approach employs the upgraded BERT system just for word embedding, with classification performed using multilabeling classifiers rather than single label classifiers. According to all methodologies, the label that is utilised for all learning classifiers identifies aspects by expressing their emotions. In this technique, the findings achieved via experimentation show that they are superior to the findings acquired through other existing researches when employing the system provided in this approach.
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