AI-POWERED EMPATHY: SENTIMENT ANALYSIS IN PERSONAL CARE USING RoBERTa AND XLNet
Year: 2025
Type: article
Abstract: Since they reveal vital information about consumer preferences and satisfaction, personal care products in the current digital era depend especially on user reviews. The intricacy and diversity of natural language make large-scale evaluation of these tests challenging. This study is motivated by the need of the personal care industry to discover how sentiment analysis grounded on Natural Language Processing (NLP) techniques could be useful. We want to use NLP models to quickly and more precisely detect consumer sentiment, so guiding classification. From this, companies can choose a lot of knowledge on consumer attitude and product performance. Covered include how to select appropriate models, compile sentiment analysis review data, and how sentiment classification influences marketing and product strategy. In the increasingly competitive personal care industry, natural language processing (NLP) can help to streamline user input thereby facilitating data-driven decisions and greater consumer satisfaction. People are realizing the full possibilities of Advanced Technology, which marks a period of perpetual and never-ending advancement. In the field of shopping and virtually purchasing goods using technology dubious issues of product quality surface. Reviews give us direct answers and allow us to sort through our questions. People are asked to post reviews, in which they provide honest and forthright comments, following purchases of goods. Our objective is to use Sentimental Analysis with NLP to classify the past reviews of the product as either favourable or negative with accuracy of 94%. Customers as well as the product company will benefit from this in order to make appropriate judgments and apply correct modifications.
Author: Journal of Theoretical and Applied Information Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7083451552
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