Analyzing Consumer Reviews with Text Mining Approach

Dasgupta, Subhasis; Sengupta, Kalyan

doi:10.1177/0971890716637700

Cited by 8 publications

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References 16 publications

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“…Equation (5) gives the posterior distribution of the possible values of variables, and further obtains the point estimation. LDA works by mapping documents into kdimensional space through , dj  , which is an extension of probabilistic latent semantic analysis.…”

Section: )Text Feature Extraction Methodsmentioning

confidence: 99%

“…At present, sentiment analysis of review text has become an important research field, which has attracted more and more attention in the academic domain. Text sentiment classification refers to the comprehensive use of machine learning technology and natural language processing technology to analyze, process, induce and infer the review text, analyze the emotion contained in the text, and judge the emotion, attitude, and viewpoint contained in the review text [5]. At present, text sentiment analysis is widely used in many fields, including social network users [2,6,7], product evaluation [8,9,10], etc.…”

Section: Related Researchmentioning

confidence: 99%

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Sentiment Classification of Crowdsourcing Participants’ Reviews Text Based on LDA Topic Model

et al. 2021

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The review text received by crowdsourcing participants contains valuable knowledge, opinions, and preferences, which is an important basis for employers to make trading decisions, and crowdsourcing participants to improve service level and quality. However, there are two kinds of emotional polarity in the review text, the attention paid to sentiment classification of review text with fuzzy emotional boundaries is insufficient. This paper proposes a supervised text sentiment classification method with Latent Dirichlet Allocation (LDA) to improve the classification performance of review texts with fuzzy sentiment boundaries. Taking the text reviews of crowdsourcing participants on the Zhubajie platform as the data set, using N-gram, Word2vec, and TF-IDF algorithms to extract text features. The LDA topic model is applied to expand the number of text features and extract eight topics that affect employers' sentiment tendencies. Text classifiers are constructed based on Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GDBT), and Extreme Gradient Boosting (XGBoost) algorithms, and the effectiveness of the sentiment classification methods are verified by ten-fold crossvalidation and confusion matrix. Experimental results show that using the LDA topic model to extend the features of review text can effectively alleviate the problem that the classifier is difficult to distinguish the sentiment categories of different emotion polarity words coexisting text, and enhance the ability of emotion boundary fuzzy text classification. The GBDT text sentiment classifier based on TF-IDF and LDA has the best performance in accuracy, recall, and F1-measure.INDEX TERMS sentiment analysis; text classification; LDA topic model; crowdsourcing participants

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Section: )Text Feature Extraction Methodsmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Sentiment Classification of Crowdsourcing Participants’ Reviews Text Based on LDA Topic Model

et al. 2021

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“…Text association analysis is to discover the correlation between different texts. Association is defined as the association of two or more variables with certain regularity (Dasgupta et al, 2016). Meanwhile, it also contains four categories, including simple relevance, temporal relevance, causal relevance, and semantic relevance.…”

Section: Text Miningmentioning

confidence: 99%

Design of a User Comment Management System Based on Text Mining

Abudureheman

2023

Journal of Organizational and End User Computing

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Presently, text mining in e-commerce reviews predominantly focus on singular sentiment analysis, yet constraints persist in sentiment score computation, semantic inclination discernment, and lexicon construction. To address these limitations, this study establishes an e-commerce user comment management system based on text mining. It performs part-of-speech tagging and dependency grammar analysis on the historical corpus of e-commerce, unveiling collocations that potentially convey users' emotive predispositions. Subsequently, a dependency grammar rule table is formulated for the extraction of emotional words. The enhanced BiGRU model is employed for bidirectional extraction of textual features, which are subsequently fused with the TextCNN model. Test results evince that the system effectively accomplishes the desired objectives, with positive comments attaining accuracy and recall rates of 93.49% and 96.98%, respectively, thereby mitigating the drawbacks associated with laborious operations and inadequate precision inherent in extant e-commerce comment analysis systems.

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Text Mining Tutorial

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2017

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Analyzing Consumer Reviews with Text Mining Approach

Cited by 8 publications

References 16 publications

Sentiment Classification of Crowdsourcing Participants’ Reviews Text Based on LDA Topic Model

Sentiment Classification of Crowdsourcing Participants’ Reviews Text Based on LDA Topic Model

Design of a User Comment Management System Based on Text Mining

Text Mining Tutorial

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