Sentiment Analysis of Consumer Reviews Using Deep Learning

Amjad Iqbal, Rashid Amin (), Javed Iqbal, Roobaea Alroobaea, Ahmed Binmahfoudh and Mudassar Hussain
Additional contact information
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
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

Sustainability, 2022, vol. 14, issue 17, 1-19

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.

Keywords: sentiment analysis; consumer reviews; artificial intelligence; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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