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A Novel Approach for Sentiment Classification by Using Convolutional Neural Network

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Proceedings of Second International Conference on Sustainable Expert Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 351))

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Abstract

Social media platforms facilitate communication and data exchange. There is a substantial number of opinionated information available in digital form. It is essential to validate unstructured Web data in order to extract knowledge from it. Sentiment analysis offers a wide variety of applications across all domains. The primary objective of sentiment analysis is to asses if the input text is positive or negative. When a buyer purchases a product, they submit feedback of the product. These reviews are essential for getting a general sense of how people feel about the product or service. Customer reviews on the Internet help to make purchases. Sentiment analysis results assist businesses, understand customer expectations, and enhance service and product quality. Several deep learning algorithms have been used in this sector with promising results. This paper suggests a deep learning approach for sentiment analysis by using convolutional neural networks.

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Author information

Authors and Affiliations

  1. Tagore College of Arts and Science, Chennai, India

    M. S. Kalaivani

  2. Vels Institute of Science, Technology and Advanced Studies, Chennai, India

    S. Jayalakshmi

Authors
  1. M. S. Kalaivani
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  2. S. Jayalakshmi
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Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Kirtipur, Nepal

    Subarna Shakya

  2. Department of Electrical and Computer Engineering, Concordia University, Montréal, QC, Canada

    Ke-Lin Du

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Wang Haoxiang

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Kalaivani, M.S., Jayalakshmi, S. (2022). A Novel Approach for Sentiment Classification by Using Convolutional Neural Network. In: Shakya, S., Du, KL., Haoxiang, W. (eds) Proceedings of Second International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 351. Springer, Singapore. https://doi.org/10.1007/978-981-16-7657-4_13

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