Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E-Commerce Product Reviews

Alzahrani, Mohammad Eid; Aldhyani, Theyazn H. H.; Alsubari, Saleh Nagi; Althobaiti, Maha M.; Fahad, Adil

doi:10.1155/2022/3840071

Cited by 25 publications

(12 citation statements)

References 44 publications

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“…The collected raw tweets contain some unnecessary and redundant information and are unlabeled. We used preprocessing steps to filter out irrelevant and redundant data from the tweets ( Alzahrani et al, 2022 ; García, Luengo & Herrera, 2015 ). Preprocessing is important for efficient training of the models and precise results.…”

Section: Methodsmentioning

confidence: 99%

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

Mujahid

Rustam

Alasim

et al. 2023

PeerJ Computer Science

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With the rise of social media platforms, sharing reviews has become a social norm in today’s modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald’s, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.

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Section: Methodsmentioning

confidence: 99%

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

Mujahid

Rustam

Alasim

et al. 2023

PeerJ Computer Science

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“…Alzahrani et al [20] proposed a framework to use an opinion on consumers' reviews to help businesses and organizations continually improve their market strategies and obtain an in-depth analysis of the consumers' opinions regarding their products and brands. The long short-term memory (LSTM) and deep learning convolutional neural network integrated with LSTM (CNN-LSTM) models were used.…”

Section: Literature Surveymentioning

confidence: 99%

ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review

et al. 2023

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Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.

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“…Glove feature extraction produced unsatisfactory results when combined with the RNN algorithm. A hybrid deep CNN and LSTM models were suggested by the authors of [ 25 ] in the e-commerce industry. However, this strategy uses more computing resources.…”

Section: Literature Reviewmentioning

confidence: 99%

LSTM‐DGWO‐Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

Barik¹,

Misra

Ray³

et al. 2023

Computational Intelligence and Neuroscience

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Sentiment analysis furnishes consumer concerns regarding products, enabling product enhancement development. Existing sentiment analysis using machine learning techniques is computationally intensive and less reliable. Deep learning in sentiment analysis approaches such as long short term memory has adequately evolved, and the selection of optimal hyperparameters is a significant issue. This study combines the LSTM with differential grey wolf optimization (LSTM-DGWO) deep learning model. The app review dataset is processed using the bidirectional encoder representations from transformers (BERT) framework for efficient word embeddings. Then, review features are extracted by the genetic algorithm (GA), and the optimal review feature set is extracted using the firefly algorithm (FA). Finally, the LSTM-DGWO model categorizes app reviews, and the DGWO algorithm optimizes the hyperparameters of the LSTM model. The proposed model outperformed conventional methods with a greater accuracy of 98.89%. The findings demonstrate that sentiment analysis can be practically applied to understand the customer’s perception of enhancing products from a business perspective.

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Developing an Intelligent System with Deep Learning Algorithms for Sentiment Analysis of E-Commerce Product Reviews

Cited by 25 publications

References 44 publications

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review

LSTM‐DGWO‐Based Sentiment Analysis Framework for Analyzing Online Customer Reviews

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