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Sentiment Analysis of Consumer Reviews Using Deep Learning

Author

Listed:
  • Amjad Iqbal

    (Department of Computer Science, University of Engineering and Technology, Taxila 47080, Pakistan)

  • Rashid Amin

    (Department of Computer Science, University of Engineering and Technology, Taxila 47080, Pakistan
    Department of Computer Science, University of Chakwal, Punjab 48800, Pakistan)

  • Javed Iqbal

    (Department of Computer Science, University of Engineering and Technology, Taxila 47080, Pakistan)

  • Roobaea Alroobaea

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Ahmed Binmahfoudh

    (Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Mudassar Hussain

    (Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan)

Abstract

Internet and social media platforms such as Twitter, Facebook, and several blogs provide various types of helpful information worldwide. The increased usage of social media and e-commerce websites is constantly generating a massive volume of data about image/video, sound, text, etc. The text among these is the most significant type of unstructured data, requiring special attention from researchers to acquire meaningful information. Recently, many techniques have been proposed to obtain insights from these data. However, there are still challenges in dealing with the text of enormous size; therefore, accurate polarity detection of consumer reviews is an ongoing and exciting problem. Due to this, it is challenging to derive exact meanings from the textual data from consumer reviews, comments, tweets, posts, etc. Previously, a reasonable amount of work has been conducted to simplify the extraction of exact meanings from these data. A unique technique that includes data gathering, preprocessing, feature encoding, and classification utilizing three long short-term memory variations is presented to address sentiment analysis problems. Analysing appropriate data collection, preprocessing, and classification is crucial when interpreting such data. Different textual datasets were used in the studies to gauge the importance of the suggested models. The proposed technique of predicting sentiments shows better, or at least comparable, results with less computational complexity. The outcome of this work shows the significant importance of sentiment analysis of consumer reviews and social media content to obtain meaningful insights.

Suggested Citation

  • Amjad Iqbal & Rashid Amin & Javed Iqbal & Roobaea Alroobaea & Ahmed Binmahfoudh & Mudassar Hussain, 2022. "Sentiment Analysis of Consumer Reviews Using Deep Learning," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10844-:d:902900
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    References listed on IDEAS

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    1. Mangold, W. Glynn & Faulds, David J., 2009. "Social media: The new hybrid element of the promotion mix," Business Horizons, Elsevier, vol. 52(4), pages 357-365, July.
    2. Subarno Pal & Soumadip Ghosh & Amitava Nag, 2018. "Sentiment Analysis in the Light of LSTM Recurrent Neural Networks," International Journal of Synthetic Emotions (IJSE), IGI Global, vol. 9(1), pages 33-39, January.
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    Cited by:

    1. Nada Ali Hakami & Hanan A. Hosni Mahmoud, 2022. "Deep Learning Analysis for Reviews in Arabic E-Commerce Sites to Detect Consumer Behavior towards Sustainability," Sustainability, MDPI, vol. 14(19), pages 1-22, October.
    2. Soon Goo Hong & DonHee Lee, 2023. "Development of a citizen participation public service innovation model based on smart governance," Service Business, Springer;Pan-Pacific Business Association, vol. 17(3), pages 669-694, September.

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