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Sentiment analysis, a burgeoning sector within natural language processing (NLP) plays a crucial role in discerning and perceiving attitudes, emotions and opinions expressed in the form of textual data in the social media platform. With the growing proliferation of social networks, sentiment analysis has become indispensable for the government, firms, and researchers, providing beneficial insights into customer feedback, public perception and new actuality. Over the years, Convolutional Neural Networks (CNN) have emerged as the most influential approach in NLP to implement text classification. In this article, we have incorporated Deep Neural Network (DNN) namely CNN in contrast with various basic supervised machine-learning (ML) approaches evaluated on the IMDB Movie Review Dataset. We have analyzed the pros and cons of each approach, simplifying their efficiency in analyzing the sentiments to compel the manifest model into a prolific and reliable approach for better text classification.

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Text-Based Sentiment Analysis: Experimentation with CNN and Basic Machine-Learning Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • Umang Kumar Agrawal,
  • Debashreet Das,
  • B. V. Ramana,
  • Nibedan Panda

摘要

Sentiment analysis, a burgeoning sector within natural language processing (NLP) plays a crucial role in discerning and perceiving attitudes, emotions and opinions expressed in the form of textual data in the social media platform. With the growing proliferation of social networks, sentiment analysis has become indispensable for the government, firms, and researchers, providing beneficial insights into customer feedback, public perception and new actuality. Over the years, Convolutional Neural Networks (CNN) have emerged as the most influential approach in NLP to implement text classification. In this article, we have incorporated Deep Neural Network (DNN) namely CNN in contrast with various basic supervised machine-learning (ML) approaches evaluated on the IMDB Movie Review Dataset. We have analyzed the pros and cons of each approach, simplifying their efficiency in analyzing the sentiments to compel the manifest model into a prolific and reliable approach for better text classification.