Identification and Categorizing the Sentiment Polarity for Fine Food Product Using Machine Learning Approaches
Keywords:
Sentiment Analysis; Fine Food reviews; Polarity categorization; Machine Learning Natural Language ProcessingAbstract
Social media serves as a platform for individuals to share their opinions on various subjects. Opinion mining or sentiment analysis are applications of Natural Language Processing (NLP), involves studying people's sentiments towards specific entities. This analytical approach proves valuable for companies seeking insights into public responses to their products. Sentiment analysis has gained significant traction in recent years, especially concerning product reviews. This paper focuses on sentiment polarity categorization as a fundamental aspect of sentiment analysis in the context of product reviews, specifically Fine Food products available online.The proposed methodology outlines a comprehensive detailed explanation of sentimental polarity categorization of each step. The study utilizes a dataset comprising online reviews of Fine Food products. The analysis is conducted at both sentence and review levels. Three distinct modelsSupport Vector Machine (SVM), Naïve Bayes and Random Forest are employed to compare their effectiveness in the sentiment polarity categorization of Fine Food product reviews.The research findings are presented as a comparative evaluation of the three models, highlighting their performance in accurately categorizing sentiment polarity in Fine Food product reviews. The proposed mode helps the companies in understanding the sentiments expressed by consumers and informs decision-making processes related to find marketing strategies,product development and customer satisfaction.
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