MODELS FOR IDENTIFYING CONSUMER OPINIONS USING DIGITAL TECHNOLOGIES IN E-COMMERCE ACTIVITIES
Keywords:
opinion mining, sentiment analysis, e commerce, aspect based sentiment classification, natural language processing, deep learningAbstract
In the rapidly evolving field of ecommerce, the ability to identify, analyse and act upon consumer opinions is a critical competitive advantage. This article examines stateoftheart models for consumer opinion identification through digital technologies, especially focusing on the domain of ecommerce review mining and sentiment analysis. It presents a structured overview of methodological frameworks (including documentlevel, sentencelevel and aspectlevel opinion mining), describes the typical dataprocessing pipeline, compares machinelearning and deeplearning models applied in ecommerce contexts, and discusses key empirical results and implementation challenges (e.g., sarcasm, multilingual reviews, aspect extraction). The article further reflects on the business implications of deploying such models in ecommerce operations, including product development, customer service, and personalisation of marketing. Finally, it summarises future research directions for more robust, scalable and interpretable opinionmining systems.
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