全球化背景下需求预测的社交媒体大数据分析:创新框架的发展与案例实施

IF 3.6 3区管理学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Organizational and End User Computing Pub Date : 2023-06-30 DOI:10.4018/joeuc.325217

Yifang Gao, Junwei Wang, Zhi Li, Zengjun Peng

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引用次数: 0

摘要

本文旨在分析大数据社交媒体中人工智能对用户需求的预测作用，为企业创新框架的制定和营销策略的实施提供建议。针对企业产品和服务供给与市场需求不一致的问题，利用社交媒体大数据分析引入深度学习算法。对该算法进行改进，构建了基于双向长短期记忆(BiLSTM)与Word2Vec融合的社交媒体大数据用户需求预测模型。该模型采用数据采集和预处理，Word2Vec算法对数据信息进行矢量化，BiLSTM网络对序列进行建模和训练。最后，以实例对模型进行了评价。

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The Social Media Big Data Analysis for Demand Forecasting in the Context of Globalization: Development and Case Implementation of Innovative Frameworks

This paper aims to analyze the predictive effect of artificial intelligence on user demand in big data social media and to provide suggestions for developing enterprise innovation frameworks and implementing marketing strategies. In response to the inconsistency between the supply of enterprise products and services and market demand, deep learning algorithms have been introduced using social media big data analysis. This algorithm has been improved to construct a user demand prediction model in social media big data based on bidirectional long short-term memory (BiLSTM) fused with Word2Vec. The model uses data acquisition and pre-processing, Word2Vec algorithm to vectorization the data information, and BiLSTM network to model and train the sequence. Finally, the model is evaluated as an example.

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来源期刊

Journal of Organizational and End User Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

6.00

自引率

9.20%

发文量

期刊介绍： The Journal of Organizational and End User Computing (JOEUC) provides a forum to information technology educators, researchers, and practitioners to advance the practice and understanding of organizational and end user computing. The journal features a major emphasis on how to increase organizational and end user productivity and performance, and how to achieve organizational strategic and competitive advantage. JOEUC publishes full-length research manuscripts, insightful research and practice notes, and case studies from all areas of organizational and end user computing that are selected after a rigorous blind review by experts in the field.