Running a Sustainable Social Media Business: The Use of Deep Learning Methods in Online-Comment Short Texts

Weibin Lin, Qian Zhang, Yenchun Jim Wu*, Tsung Chun Chen

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

摘要

With the prevalence of the Internet in society, social media has considerably altered the ways in which consumers conduct their daily lives and has gradually become an important channel for online communication and sharing activities. At the same time, whoever can rapidly and accurately disseminate online data among different companies affects their sales and competitiveness; therefore, it is urgent to obtain consumer public opinions online via an online platform. However, problems, such as sparse features and semantic losses in short-text online reviews, exist in the industry; therefore, this article uses several deep learning techniques and related neural network models to analyze Weibo online-review short texts to perform a sentiment analysis. The results show that, compared with the vector representation generated by Word2Vec’s CBOW model, BERT’s word vectors can obtain better sentiment analysis results. Compared with CNN, BiLSTM, and BiGRU models, the improved BiGRU-Att model can effectively improve the accuracy of the sentiment analysis. Therefore, deep learning neural network systems can improve the quality of the sentiment analysis of short-text online reviews, overcome the problems of the presence of too many unfamiliar words and low feature density in short texts, and provide an efficient and convenient computational method for improving the ability to perform sentiment analysis of short-text online reviews. Enterprises can use online data to analyze and immediately grasp the intentions of existing or potential consumers towards the company or product through deep learning methods and develop new services or sales plans that are more closely related to consumers to increase competitiveness. When consumers experience the use of new services or products again, they may provide feedback online. In this situation, companies can use deep learning sentiment analysis models to perform additional analyses, forming a dynamic cycle to ensure the sustainable operation of their enterprises.

原文英語
文章編號9093
期刊Sustainability (Switzerland)
15
發行號11
DOIs
出版狀態已發佈 - 2023 6月

ASJC Scopus subject areas

  • 電腦科學(雜項)
  • 地理、規劃與發展
  • 可再生能源、永續發展與環境
  • 環境科學(雜項)
  • 能源工程與電力技術
  • 硬體和架構
  • 電腦網路與通信
  • 管理、監督、政策法律

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