Sentiment Analysis of Consumer Reviews Using Deep Learning

被引:22
|
作者
Iqbal, Amjad [1 ]
Amin, Rashid [1 ,2 ]
Iqbal, Javed [1 ]
Alroobaea, Roobaea [3 ]
Binmahfoudh, Ahmed [4 ]
Hussain, Mudassar [5 ]
机构
[1] Univ Engn & Technol, Dept Comp Sci, Taxila 47080, Pakistan
[2] Univ Chakwal, Dept Comp Sci, Chakwal 48800, Punjab, Pakistan
[3] Taif Univ, Coll Comp & Informat Technol, Dept Comp Sci, POB 11099, Taif 21944, Saudi Arabia
[4] Taif Univ, Coll Comp & Informat Technol, Dept Comp Engn, POB 11099, Taif 21944, Saudi Arabia
[5] Univ Wah, Dept Comp Sci, Wah Cantt 47040, Pakistan
关键词
sentiment analysis; consumer reviews; artificial intelligence; deep learning;
D O I
10.3390/su141710844
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:19
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