Feature-Based Opinion Classification Using the KPCA Technique: Concept and Performance Evaluation

Abstract

Over the last several years, a widespread trend on the internet has been the proliferation of online evaluations written by people with whom they share their ideas, interests, experiences, and opinions. Opinion mining, also known as sentiment analysis, is the process of classifying pieces of text written in a natural language on a subject into positive, negative, or neutral categories according to the human emotions, views, and feelings that are communicated in that text. The field of sentiment analysis has progressed to the point that it can now analyse internet evaluations and provide significant information to people as well as corporations, which may assist these parties in the decision-making process. In the proposed model, feature extraction extracts the collection of features that are both semantically and statistically significant using the kernel principal component analysis (KPCA) method. According to the findings of the simulations, the suggested model performs better than other existing models.