Reviews through Advanced Recurrent Neural Network-based Sentiment Analysis
B. Pujitha, A. Mary Sowjanya
Sentiment analysis, Amazon Pet Food Reviews, online commerce, Recurrent neural networks (RNN), natural language processing (NLP), Machine learning.
Sentiment analysis, a transformative automated technique for discerning emotions within textual content, holds unparalleled significance across diverse domains. This study explores its multifaceted applications, including the analysis of sentiments within Amazon Pet Food Reviews. These reviews, integral to the e-commerce landscape, influence purchasing decisions and reflect public sentiment. Our investigation spans online commerce, academia, and social media, showcasing sentiment analysis's far-reaching implications. By harnessing cutting-edge learning techniques, sentiment analysis reshapes how we comprehend reviews, enriching decision-making processes with profound insights. The academic sphere witnesses the emergence of a pioneering sentiment analysis paradigm, amplifying precision in scholarly publications and rendering complex content accessible. This scholarly exploration delves into the orchestration of machine learning algorithms that categorize textual narratives, shedding light on the dynamics of classifier performance. Additionally, the research journey navigates the intricate landscape of advanced recurrent neural networks (RNN) and sophisticated natural language processing (NLP) methodologies. The integration of RNN models with advanced NLP techniques transcends traditional benchmarks, accentuating the depth and nuances of insights unveiled. Through this symbiotic interplay, sentiment analysis emerges as a discerning lens, unraveling layered contexts and sentiments, especially in the realm of Amazon Pet Food Reviews.
Article Details
Unique Paper ID: 161475

Publication Volume & Issue: Volume 10, Issue 4

Page(s): 293 - 297
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