| 摘 要: 为了进一步了解情感分析方向的发展趋势，通过对基于机器学习的情感分析文献的整理与分析，首先对国内外基于机器学习的情感分析方法进行了梳理，介绍了相关方法的基本原理及算法改进；其次列举了几种方法在电子商务、餐馆评价和灾害管理中的实际应用，对当前情感分析应用中存在的主要困难进行探讨，对相关方法处理能力进行评价；最后得出了上下游任务结合的处理方法值得深入研究的结论，给出了对情感分析未来研究趋势的展望，提出了相关方法改进的挑战。 |
| 关键词: 情感分析；机器学习；BERT；支持向量机；卷积神经网络 |
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中图分类号: TP391.1
文献标识码: A
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| A Survey of Sentiment Analysis Methods and Application Research based on Machine Learning |
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LI Mengnan, WANG Mingyan
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(School of Management Studies, Shanghai University of Engineering Science, Shanghai 201620, China)
franklee24@163.com; wmy61610@126.com
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| Abstract: In order to further understand the development trend of sentiment analysis, this paper proposes first to sort out sentiment analysis methods of machine learning at home and abroad, after sorting and analyzing sentiment analysis literature. Basic principles and algorithm improvement are introduced, followed by the practical application of several methods in e-commerce, restaurant evaluation and disaster management. Difficulties in the current sentiment analysis application are discussed and the processing ability of related methods is evaluated. Finally, it is concluded that the processing method of combining upstream and downstream tasks is worthy of in-depth study, and the prospect of the future research trend of sentiment analysis and the challenges of related method improvement are given. |
| Keywords: sentiment analysis; machine learning; BERT; support vector machine; convolutional neural network |
