A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews

Jain, Praphula Kumar; Pamula, Rajendra; Srivastava, Gautam

doi:10.1016/j.cosrev.2021.100413

Cited by 209 publications

(80 citation statements)

References 126 publications

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“…As the carrier of information dissemination, online comments have attracted increasing attention from companies and consumers. The function of online comments not only reduces consumers' uncertainty about product quality and experience attributes [33], but also significantly affects the company's operational decisions and helps reshape business growth [34]. Combined with the characteristics of live commerce, this study defines online comments as real-time comments sent by consumers during live watching.…”

Section: Hypothetical Research On Online Comment Factorsmentioning

confidence: 99%

The Influencing Factors on Impulse Buying Behavior of Consumers under the Mode of Hunger Marketing in Live Commerce

Zhang

Wang

2022

Sustainability

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The sustainability of live commerce is closely dependent on the impulse buying behavior of consumers because of its live characters compared to the traditional e-commerce mode. Hunger marketing is a widely adopted mode in live commerce, however, the influencing factors on impulse buying behavior of consumers under the mode of hunger marketing in live commerce is currently lacking. Here, based on SOR theory, we carefully studied how external stimulus variables (anchor characteristics, online comments, logistics service quality, promotion incentive information, and promotional time limit) affected consumers’ impulse buying behavior under the inner mechanism reaction factors (perceived trust and perceived value). By collecting questionnaires from 533 Chinese online consumers, we processed these data using SPSS23.0 and analyzed them using the structural equation model (SEM) generated by AMOS 25.0. The results showed that extrinsic stimulus variables (except for the promotional time limit variable) have significant positive effects on impulse buying behavior through partial or total positive effects on intrinsic mechanisms. We analyzed the reasons for all these results in detail, and thought the reason that the promotional time limit did not have a significant impact on the perceived value might be due to the rapid increase in the frequency of live commerce. Moreover, we also put forward the suggestions on the basis of these influencing factors to improve the sustainable development of live commerce.

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Section: Hypothetical Research On Online Comment Factorsmentioning

confidence: 99%

The Influencing Factors on Impulse Buying Behavior of Consumers under the Mode of Hunger Marketing in Live Commerce

Zhang

Wang

2022

Sustainability

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“…the evaluation measures such as sensitivity, specificity, accuracy, FPR, FNR, PPV, and NPV definition and its mathematical notations refered from [ 9 ] and demonstrated as follows: Sensitivity- The ratio of the number of true positives to the sum of true positive and false negative is called sensitivity. …”

Section: Resultsmentioning

confidence: 99%

Multi-type skin diseases classification using OP-DNN based feature extraction approach

Jain

Rao

Jain

et al. 2022

Multimed Tools Appl

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In the current world, the disorders occurring in dermatological images are among the foremost widespread diseases. Despite being common, its identification is tremendously hard because of the complexities like skin tone and color variation due to the presence of hair regions. Therefore the type of skin disease prediction is not accurately achieved in many pieces of research. To deal with mentioned concerns, a novel optimal probability-based deep neural network is proposed to assist medical professionals in appropriately diagnosing the type of skin disease. Initially, the input dataset is fed into the pre-processing stage, which helps to remove unwanted contents in the image. Afterward, features extracted for all the pre-processed images are subjected to the proposed Optimal Probability-Based Deep Neural Network (OP-DNN) for the training process. This classification algorithm classifies incoming clinical images as different skin diseases with the help of probability values. While learning OP-DNN, it is essential to determine the optimal weight values for reducing the training error. For optimizing weight in OP-DNN structure, an optimization approach is implemented in this research. For that, whale optimization is utilized because it works faster than other methods. The proposed multi-type skin disease prediction model is implemented in MatLab software and achieved 95% of accuracy, 0.97 of specificity, and 0.91 of sensitivity. This exposes the superiority of the proposed multi-type skin disease prediction model using an effective OP-DNN based feature extraction approach to attain a high accuracy rate and also it predict several kinds of skin disease than the previous models, which can protect the patients survives as well as can assist the physicians in making a decision certainly.

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“…Jain et al ( 2021a , b , c , d , ) a proposed a Cuckoo Search-eXtreme gradient boosting model and optimized the model to recommend airlines. They also (2021 b) proposed a sparse self-attentive network-based aspect-aware model that can effectively predict consumer recommendation decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of agricultural exports based on deep learning and text mining

Hsu

2022

J Supercomput

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Agricultural exports are an important source of economic profit for many countries. Accurate predictions of a country’s agricultural exports month on month are key to understanding a country’s domestic use and export figures and facilitate advance planning of export, import, and domestic use figures and the resulting necessary adjustments of production and marketing. This study proposes a novel method for predicting the rise and fall of agricultural exports, called agricultural exports time series-long short-term memory (AETS-LSTM). The method applies Jieba word segmentation and Word2Vec to train word vectors and uses TF-IDF and word cloud to learn news-related keywords and finally obtain keyword vectors. This research explores whether the purchasing managers’ index (PMI) of each industry can effectively use the AETS-LSTM model to predict the rise and fall of agricultural exports. Research results show that the inclusion of keyword vectors in the PMI values of the finance and insurance industries has a relative impact on the prediction of the rise and fall of agricultural exports, which can improve the prediction accuracy for the rise and fall of agricultural exports by 82.61%. The proposed method achieves improved prediction ability for the chemical/biological/medical, transportation equipment, wholesale, finance and insurance, food and textiles, basic materials, education/professional, science/technical, information/communications/broadcasting, transportation and storage, retail, and electrical and machinery equipment categories, while its performance for the electrical and optical categories shows improved prediction by combining keyword vectors, and its accuracy for the accommodation and food service, and construction and real estate industries remained unchanged. Therefore, the proposed method offers improved prediction capacity for agricultural exports month on month, allowing agribusiness operators and policy makers to evaluate and adjust domestic and foreign production and sales.

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A systematic literature review on machine learning applications for consumer sentiment analysis using online reviews

Cited by 209 publications

References 126 publications

The Influencing Factors on Impulse Buying Behavior of Consumers under the Mode of Hunger Marketing in Live Commerce

The Influencing Factors on Impulse Buying Behavior of Consumers under the Mode of Hunger Marketing in Live Commerce

Multi-type skin diseases classification using OP-DNN based feature extraction approach

Analysis of agricultural exports based on deep learning and text mining

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