Sentiment Analysis of Product Reviews using Deep Learning

PANTHATI, JAGADEESH; Bhaskar, Jasmine; Ranga, Tarun Kumar; Challa, Manish Reddy

doi:10.1109/icacci.2018.8554551

Cited by 21 publications

(5 citation statements)

References 13 publications

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“…NLP was also used for Amazon reviews to enhance service by comparing K-Nearest Neighbour, SVM, and Decision Tree classification algorithms to analyze customer feedback [16]. Deep learning techniques like word2vec for word embedding and CNN were used to evaluate social marketing tactics and help consumers make informed purchase decisions [17]. Another study used classical machine learning and deep learning to classify multiple affective attributes with over 90% accuracy using customer emotional needs from online product reviews [18].…”

Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2023

Preprint

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In recent years, e-commerce platforms have replaced conventional marketplaces. People are fast adopting online shopping since they can purchase from home. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer evaluations, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges. This study covers 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the bulk of existing research relies on Amazon user reviews. Additionally, recent research has stressed the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates researchers' expanding interest and the field's continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

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Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2023

Preprint

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“…They have experimentally verified that CDR based sentiments from users' reviews over Movielens and Bookcrossing datasets provide 77.1% better F-Score. Panthati et al [19] have proposed a method for the sentiment analysis of products using deep learning. The method combines word2vec and Convolutional Neural Network (CNN) to predict the review sentiments from Amazon for the mobile phone product.…”

Section: Related Workmentioning

confidence: 99%

An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

Yadav¹,

Yadav

Jain

2018

ICST Transactions on Scalable Information Systems

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In today's society, sentiment analysis has gained due importance as it provides useful information about products that are used by variety of users. It gives a sneak peek of users' reactions towards the products that are available in the market at an early stage. It thus intimates users' perception and charts out a path that is beneficial for the market to grow as a whole. Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of the detailed components based products and their associated features, a novel method is desired to meet these criteria. So far, no such method is explored that analyses the product's components and their features simultaneously, on the basis of sentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis of product reviews while formulating a tree structure of product, components, and associated features. In addition, evaluation of double negative sentences, detecting questions and emotions from the review sentences are measured which increases efficiency of the FBA method. The comparison of product's components reviews is done with other existing algorithms-TF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.

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“…In the modern day, reviews and ratings have become an increasingly important part of the decision-making process for consumers [5]. Consumers often offer a number rating in the range of 1 to 5 in order to show their satisfaction with a product.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products

Marigowda,

Moldovan,

Siddig

et al. 2023

Proceedings of the 2023 5th International Conference on Information Technology and Computer Communications

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E-commerce is the activity of electronically purchasing or selling products in an online platform. Ecommerce recommender systems provide suggestions of products based on the consumer sentiment and ratings. There is often a mismatch between consumer rating and their sentiment. Identifying the accuracy of the mismatch is a challenge in machine learning. This research proposes a Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products based on consumer sentiment and product descriptions. This proposed frame work combines a text embeddings model, sentiment analysis model and a rating engine. The text embeddings model is implemented using gensim doc2vec for consumer reviews and product descriptions. Further it uses neural networks for capturing the consumer product interactions for collaborative filtering. The sentiment analysis model is implemented by inputting distributed text embeddings into neural networks that are trained to capture content feature of products and sentiment of consumer evaluations. The rating engine is implemented by aggregating several embeddings as attention weights for consumers and products, then outputting the prediction score for the consumer-product interaction. This research makes use of the realworld Amazon product category semi structured baby and digital music semi structured datasets, each of which contains information on consumer reviews and product metadata. Mean absolute error (MAE) and the root mean-square error (RMSE) are considered to evaluate the recommendation performance, thereby measuring the accuracy of prediction ratings. Experimental results on Amazon distinct product dataset with promising accuracy metric MAE and RMSE values by 0.5909 and 0.8080 respectively demonstrates that proposed framework performs better on rating prediction in enhancing consumers experience to find their preferences over the E-commerce products.

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Sentiment Analysis of Product Reviews using Deep Learning

Cited by 21 publications

References 13 publications

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

A Novel Hybrid Machine Learning Framework to Recommend E-Commerce Products

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