Online Reviews System using Aspect Based Sentimental Analysis & Opinion Mining

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Akshada Y. Doifode
Sonali D. Mali
T. Srinivasa Rao
S. B. Vanjale
Mrunal Bewoor
Sampat P. Medhane
Laxmi Adhav


Aspect extraction is the most critical and thoroughly researched process in SA (Sentiment Analysis) for conducting an accurate classification of feelings. Over the last decade, massive amounts of research have focused on identifying and removing elements. Products have centralized distribution channels, and certain apps may occasionally operate close to the most recent product to be created. Any e-commerce business enterprise must analyses user / customer feedback in order to provide better products and services to them. Because broad reviews frequently include remarks in a consolidated manner when a customer gives his thoughts on various product attributes within the same summary, it is difficult to determine the exact feeling. The key components of this software are included in their release, making it a valuable tool for management to improve the consistency of their own system's specifications. The goal was to categories the aspects of the target entities provided, as well as the feelings conveyed for each aspect. First, we are implementing a supervised classification framework that is tightly restricted and relies solely on training sets for knowledge. As a result, the key terms comes from associated at various elements of a thing within its entirety perform customer sentiment using certain elements. In contrast to current sentiment analysis approaches, synthetic and actual data set experiments yield positive results.

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Doifode, A. Y. ., Mali, S. D. ., Rao, T. S. ., Vanjale, S. B. ., Bewoor, M. ., Medhane, S. P. ., & Adhav, L. . (2023). Online Reviews System using Aspect Based Sentimental Analysis & Opinion Mining. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 381–385.


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