Sentiment Analysis of Amazon Review using Improvised Conditional Based Convolutional Neural Network and Word Embedding

Sentiment Analysis of Amazon Review using Improvised Conditional Based Convolutional Neural Network and Word Embedding

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : Madhuri V. Joseph
DOI : 10.14445/22315381/IJETT-V70I12P220

How to Cite?

Madhuri V. Joseph, "Sentiment Analysis of Amazon Review using Improvised Conditional Based Convolutional Neural Network and Word Embedding," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 194-209, 2022. Crossref,

Online shopping websites such as Amazon demands a platform for customers to share their opinion about various products. The objective is to evaluate the sentiment analysis of amazon product reviews using an improvised conditional-based Convolutional Neural Network (CNN) with word embedding. The employed improvised conditional-based CNN makes the corresponding model asynchronous in computation and hence speeds up the training period to a great extent. This conditional random field was applied to capture the dependencies between the neighboring tags to obtain the optimum tag for the whole sequence. Sentiment Analysis is the research process that helps the users to confront their opinions on the products in a review. In this proposed methodology, the data collected from the Amazon dataset contains customer reviews about various products. Before analysis, the collected data undergoes pre-processing, eliminating the unimportant text and keeping only the important information. The classification of sentiment analysis has been considered the major part, followed by extracting the important information. Here conditional-based CNN is used to classify the polarity set of reviews such as positive, negative, or neutral. Based on the reviews, the specified product has been evaluated. The data or the information is retrieved from the database of Amazon reviews of various products by customers. After the data is acquired, the data preprocessing is done. The significant features have been determined using word embedding. Using the proposed conditionalbased CNN approach, the sentiment analysis is evaluated accurately, and the performance is compared with various existing approaches in factors like recall, accuracy, f-measure and precision. The proposed method provides better performance, like identifying the product review. The system obtained an average recall of 0.605, an average F-measure value of 0.7332, and an accuracy of 0.4427, which are comparatively higher than existing traditional methods.

CNN-Convolutional Neural Network, Sentiment analysis, NLP-Natural Language Processing, DL-Deep Learning, Decision making, Word embedding

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