基于情感分析改进的在线评论分类研究
Research on Online Review Classification Based on Improved Sentiment Analysis
摘要: 面对海量的评论文本，单纯依靠人力对其进行分类，任务繁重且效率低下。文章提出一种基于情感分析改进的在线评论分类模型，将灰色关联分析和朴素贝叶斯算法相结合，考虑在线评论中的用户情感倾向，并将灰色关联分析结果作为一项特征属性嵌入朴素贝叶斯文本分类模型中。文章以京东商城Dyson V10 Fluffy Extra手持无线吸尘器为研究对象，对真实的在线评论数据进行挖掘，以检验模型的分类效果。结果显示，改进后的模型的分类性能明显领先于传统朴素贝叶斯分类模型，评价指标F值提升了3.06%，表明该方法在在线评论文本分类应用中具有一定的优势。
Abstract: In the face of a large number of comments, it is cumbersome and inefficient to classify them only by relying on human resources. This paper proposes an improved online comment classification model based on sentiment analysis, which combines grey correlation analysis with Naive Bayes algorithm to consider the user sentiment orientation in online comments, and embed the result of grey correlation analysis as a feature attribute into the Naive Bayes classification model. This paper takes the Dyson V10 Fluffy Extra handheld wireless vacuum cleaner of JD.com as the research object, and mines real online review data to test the classification effect of the model. The results show that the classification performance of the improved model is significantly better than that of the traditional Naive Bayes classification model, and the evaluation index F value increases by 3.06%. This method has certain advantages in the application of online review text classification.
文章引用：张姝. 基于情感分析改进的在线评论分类研究[J]. 软件工程与应用, 2022, 11(3): 445-455. https://doi.org/10.12677/SEA.2022.113047

参考文献

[1] 佚名. 第48次《中国互联网络发展状况统计报告》发布超十亿用户接入互联网我国成全球最庞大数字社会[J]. 网络传播, 2021(9): 76-81.
[2] 祝琳琳, 李贺, 李世钰, 等. 面向电子商务网站的在线评论信息质量感知机理研究[J]. 图书情报工作, 2021, 65(6): 32-40.
[3] 谢法举, 刘臣, 唐莉. 在线评论情感分析研究综述[J]. 软件导刊, 2018, 17(2): 1-4+7.
[4] 赵宇晴, 阮平南, 刘晓燕, 等. 基于在线评论的用户满意度评价研究[J]. 管理评论, 2020, 32(3): 179-189.
[5] Arndt, J. (1967) Role of Product-Related Conversations in the Diffusion of a New Product. Journal of Marketing Research, 4, 291-295. [Google Scholar] [CrossRef] 
[6] Chatterjee, P. (2001) Online Reviews: Do Consumers Use Them? Advances in Consumer Research, 28, 129-133.
[7] 李磊, 宋建伟, 刘继. 基于在线评论情感分析的声誉影响效应研究[J]. 管理学报, 2020, 17(4): 583-591.
[8] 何有世, 王明. 基于多特征组合的在线产品评论情感挖掘研究[J]. 软件导刊, 2017, 16(5): 1-5.
[9] Das, S.R. and Chen, M.Y. (2007) Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web. Management Science, 53, 1375-1388. [Google Scholar] [CrossRef] 
[10] Georgiadou, E., Angelopoulos, S. and Drake, H. (2020) Big Data Analytics and International Negotiations: Sentiment Analysis of Brexit Negotiating Outcomes. International Journal of Information Management, 51, Article ID: 102048. [Google Scholar] [CrossRef] 
[11] Whissell, C. (2009) Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language. Psychological Reports, 105, 509-521. [Google Scholar] [CrossRef] 
[12] Wang, L., Guo, W., Yao, X., et al. (2021) Multimodal Event-Aware Network for Sentiment Analysis in Tourism. IEEE MultiMedia, 28, 49-58. [Google Scholar] [CrossRef] 
[13] Sanglerdsinlapachai, N., Plangprasopchok, A., Tu, B.H., et al. (2021) Improving Sentiment Analysis on Clinical Narratives by Exploiting UMLS Semantic Types. Artificial Intelligence in Medicine, 113, Article ID: 102033. [Google Scholar] [CrossRef] [PubMed]
[14] Zhang, K., Zhu, Y., Zhang, W., et al. (2021) Cross-Modal Image Sentiment Analysis via Deep Correlation of Textual Semantic. Knowledge-Based Systems, 216, Article ID: 106803. [Google Scholar] [CrossRef] 
[15] 贾晓帆, 何利力. 融合朴素贝叶斯与决策树的用户评论分类算法[J]. 软件导刊, 2021, 20(7): 1-5.
[16] Kollur, J. and Razia, S. (2020) Text Classification Using Nave Bayes Classifier. Materials Today: Proceedings, in Press. [Google Scholar] [CrossRef] 
[17] Hindi, K., Alijulaidan, R.R. and Alsalman, H. (2020) Lazy Fine-Tuning Algorithms for Nave Bayesian Text Classification. Applied Soft Computing, 96, Article ID: 106652. [Google Scholar] [CrossRef] 
[18] Wei, Z. and Feng, G. (2011) An Improvement to Naive Bayes for Text Classification. Procedia Engineering, 15, 2160-2164. [Google Scholar] [CrossRef] 
[19] Zhang, H., Jiang, L. and Yu, L. (2021) Attribute and Instance Weighted Naive Bayes. Pattern Recognition, 111, Article ID: 107674. [Google Scholar] [CrossRef] 
[20] 熊志斌, 刘冬. 朴素贝叶斯在文本分类中的应用[J]. 软件导刊, 2013, 12(2): 49-51.

为你推荐