Open AccessOnline review analysis on various networks’ consumer feedback using deep learning
Author(s) -
Liu Huajin
Publication year - 2022
Publication title -
iet networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.466
H-Index - 21
eISSN - 2047-4962
pISSN - 2047-4954
DOI - 10.1049/ntw2.12045
Subject(s) - computer science , sentiment analysis , deep learning , enhanced data rates for gsm evolution , product (mathematics) , artificial intelligence , mobile phone , feature (linguistics) , online learning , data science , machine learning , data mining , multimedia , telecommunications , mathematics , linguistics , philosophy , geometry
Abstract In order to understand the application of deep learning in consumer online review analysis, the text content of online review and the impact of national culture on it are studied. A consumer online comment analysis model based on deep learning is constructed and utilised using edge computing. With the online reviews of mobile phone and computer products as the case, the analysis is made. The data of online reviews on various network platforms is obtained. The model based on edge computing is used for analysis, and the results of product feature sentiment analysis are obtained. The results show that the deep learning model can achieve effective product feature extraction and high accuracy sentiment analysis, which is reasonable and effective in practical application.