中国管理科学 ›› 2023, Vol. 31 ›› Issue (3): 217-227.doi: 10.16381/j.cnki.issn1003-207x.2020.0164
• 论文 • 上一篇
孙冰, 沈瑞
收稿日期:
2020-02-07
修回日期:
2020-07-21
发布日期:
2023-04-03
通讯作者:
孙冰(1972-),女(汉族),黑龙江哈尔滨人,哈尔滨工程大学经济管理学院,教授,博士生导师,研究方向:技术管理、数据分析与知识管理,Email:heusun@hotmail.com.
E-mail:heusun@hotmail.com
基金资助:
SUN Bing, SHEN Rui
Received:
2020-02-07
Revised:
2020-07-21
Published:
2023-04-03
Contact:
孙冰
E-mail:heusun@hotmail.com
摘要: 在数据为王的时代背景下,在线评论因具有信息多样性和参与群体性的特征而备受国内外学者的关注。本文以京东和天猫网络购物平台上的4款智能手机为研究对象,获取有效的在线评论26489条。首先,采用BAE判定算法提取了手机产品的特征词,借助基于互信息和语义相似度的相关性分析对消费者的产品需求偏好进行了归类与判别。其次,基于情感倾向分析得出消费者的7个需求偏好维度的分值,形成了用于表征消费者的多维分值向量,并采用改进的两步聚类法对智能手机的消费者进行了群组划分,总结了各类消费者群组的特征,进而对4款智能手机的消费者群组的构成进行了分析和比较。最后,根据研究结果得出了相关管理启示。
中图分类号:
孙冰, 沈瑞. 基于在线评论的产品需求偏好判别与客户细分——以智能手机为例[J]. 中国管理科学, 2023, 31(3): 217-227.
SUN Bing, SHEN Rui. Online Reviews for Product Demand Preference Discrimination and Customer Segmentation: A Case Study of the Smart Phone Data[J]. Chinese Journal of Management Science, 2023, 31(3): 217-227.
[1] 中国网信网. CNNIC发布第44次《中国互联网络发展状况统计报告》[EB/OL]. (2019-8-30) [2019-08-30]. http://www.cac.gov.cn/2019-08/30/c_1124939590.htm.China network information network. CNNIC released the 44th Statistical Report on Internet Development in China[EB/OL]. (2019-8-30) [2019-08-30]. http://www.cac.gov.cn/2019-08/30/c_1124939590.htm. [2] 李光敏, 陈炽, 邢江,等. 网络文本评论中产品特征抽取综述[J]. 现代情报, 2016(8):168-173.Li Guangmin, Chen Chi, Xing Jiang, et al. Overview of extracting product feature from text reviews[J]. Journal of Modern Information, 2016(8):168-173. [3] 闵庆飞, 覃亮, 张克亮. 影响在线评论有用性的因素研究[J]. 管理评论, 2017, 29(10): 95-107.Min Qingfu, Qin Liang, Zhang Keliang. Factors affecting the perceived usefulness of online reviews[J]. Management Review, 2017, 29(10): 95-107. [4] Wilson T, Wiebe J, Hoffmann P. Recognizing contextual polarity in phrase-level sentiment analysis[J]. International Journal of Computer Applications, 2005, 7(5): 347-354. [5] Ye Qiang, Law R, Gu Bin. The impact of online user reviews on hotel room sales[J]. International Journal of Hospitality Management, 2009, 28(1): 180-182. [6] Schindler R M, Bickart B. Perceived helpfulness of online consumer reviews: the role of message content and style[J]. Journal of Consumer Behaviour, 2012, 11(3): 234-243. [7] Sen S, Lerman D. Why are you telling me this? An examination into negative consumer reviews on the Web[J]. Journal of Interactive Marketing, 2007, 21(4): 76-94. [8] Ghose A, Ipeirotis P G. Designing novel review ranking systems: predicting the usefulness and impact of reviews[J]. International Conference on Electronic Commerce 2007, 60: 303-310. [9] 邓卫华,张宇. 在线评论信息内容对阶段性有用性评价的影响研究[J]. 情报理论与实践, 2018, 4(8): 90-95.Deng Weihua, Zhang Yu. Study on the effects of online review content on periodical usefulness evaluation[J]. Information Studies: theory & application, 2018, 4(8): 90-95. [10] 刘伟, 徐鹏涛. O2O电商平台在线点评有用性影响因素的识别研究——以餐饮行业O2O模式为例[J]. 中国管理科学, 2016, 24(5): 168-176.Liu Wei, Xu Pengtao. A study on influencing factors of the helpfulness of online reviews in O2O of restaurant industry:Based on Tobit model[J]. Chinese Journal of Management Science, 2016, 24(5): 168-176. [11] 黄静, 朱丽娅, 周南. 企业家微博信息对其形象评价的影响机制研究[J]. 管理世界, 2014(9):107-119.Huang Jing, Zhu Liya, Zhou Nan. A study on the impact of the influencing mechanism of entrepreneur’s micro-blogging information on the evaluation of the image of the entrepreneur[J]. Journal of Management World, 2014(9):107-119. [12] Lipizzi C, Iandoli L, Marquez R, et al. Extracting and evaluating conversational patterns in social media: a socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams[J]. International Journal of Information Management, 2015, 35(4): 490-503. [13] Huang Ni, Burtch G, Hong Yili, et al. Effects of multiple psychological distances on construal and consumer evaluation: a field study of online reviews[J]. Journal of Consumer Psychology, 2016, 26(4): 474-482. [14] Jiang Guoyin, Tadikamalla P R, Shang J, et al. Impacts of knowledge on online brand success: an agent-based model for online market share enhancement[J]. European Journal of Operational Research, 2016, 248(3): 1093-1103. [15] Al-Daihani S M, Abrahams A. A text mining analysis of academic libraries’ tweets[J]. The Journal of Academic Librarianship, 2016, 42(2): 135-143. [16] Chen Z, Lurie N H. Temporal contiguity and negativity bias in the impact of online word of mouth[J]. 2013, 50(4): 463-476. [17] Filieri R, Mcleay F. E-WOM and accommodation: an analysis of the factors that influence travelers' adoption of information from online reviews[J]. Journal of Travel Research, 2013, 53(1): 44-57. [18] Korfiatis N, García-Bariocanal E, Sánchez-Alonso S. Evaluating content quality and helpfulness of online product reviews: the interplay of review helpfulness vs. review content[J]. Electronic Commerce Research and Applications, 2012, 11(3): 205-217. [19] Akapnar G. How automated feedback through text mining changes plagiaristic behavior in online assignments[J]. Computers & Education, 2015, 87: 123-130. [20] Cao Qing, Duan Weijing, Gan Qiwei. Exploring determinants of voting for the “helpfulness” of online user reviews: a text mining approach[J]. Decision Support Systems, 2011, 50(2): 511-521. [21] Reyes A, Rosso P. Making objective decisions from subjective data: detecting irony in customer reviews[J]. Decision Support Systems, 2012, 53(4): 754-760. [22] Abrahams A S, Jiao Jian, Wang G A, et al. Vehicle defect discovery from social media[J]. Decision Support Systems, 2012, 54(1): 87-97. [23] 谭学清, 何珊. 用户情境下基于信息增益和项目的协同过滤推荐技术研究[J]. 情报杂志, 2014(7): 165-170.Tan Xueqing, He Shan. Study of context-aware recommendation technology based on information gain and item-based collaborative filtering[J]. Journal of Intelligence, 2014(7): 165-170. [24] 刘忠宝, 赵文娟. 基于互信息的不平衡Web文本分类方法研究[J]. 情报科学, 2015(10): 23-26.Liu Zhongbao, Zhao Wenjuan. Imbalanced web text classification method based on mutual information[J]. Information Science, 2015(10): 23-26. [25] Hatzivassiloglou V, Mckeown K R. Predicting the semantic orientation of adjectives[C]//Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics, Suntec, Singapore, August 2-7, 2009. [26] Turney P D. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews[C]//Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, July 8-10, 2002. [27] Riloff E, Wiebe J. Learning extraction patterns for subjective expressions[C]//Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, Sapporo, Japan, July 11-12, 2003. [28] 严仲培, 陆文星, 束柬,等. 面向旅游在线评论情感词典构建方法[J]. 计算机应用研究, 2019, 36(6): 1660-1664.Yan Zhongpei, Lu Wenxing, Shu Jian, et al. Construction method of sentiment lexicon for online travel reviews[J]. Application Research of Computers, 2019, 36(6): 1660-1664. [29] 王伟, 王洪伟, 孟园. 协同过滤推荐算法研究:考虑在线评论情感倾向[J]. 系统工程理论与实践, 2014, 34(12): 3238-3249.Wang Wei, Wang Hongwei, Meng Yuan. The collaborative filtering recommendation based on sentiment analysis of online reviews[J]. Systems Engineering - Theory & Practice, 2014, 34(12): 3238-3249. [30] 尹裴, 王洪伟.面向产品特征的中文在线评论情感分类:以本体建模为方法[J]. 系统管理学报, 2016, 25(1): 103-114.Yin Pei, Wang Hongwei. Sentiment classification for Chinese online reviews at product feature level through domain ontology method[J]. Journal of Systems & Management, 2016, 25(1): 103-114. [31] Joachims T. Text categorization with suport vector machines: learning with many relevant features[C]//Proceedings of European Conference on Machine Learning, Chemnitz, Germany, April 21-23, 1998. [32] 何跃, 邓唯茹, 张丹. 中文微博的情绪识别与分类研究[J]. 情报杂志, 2014(2): 136-139.He Yue, Deng Weiru, Zhang Dan. Study on sentiments recognition and classification of Chinese microblog[J]. Journal of Intelligence, 2014(2): 136-139. [33] 张华鑫, 庞建刚. 基于SVM和KNN的文本分类研究[J]. 现代情报, 2015(5): 73-77.Zhang Huaxin, Pang Jiangang. Research on text classification based on SVM and KNN[J]. Journal of Modern Information, 2015(5): 73-77. [34] Sun Tao, Youn S, Wu Guohua, et al. Online word-of-mouth: an exploration of its antecedents and consequences[J]. Journal of Computer-Mediated Communication, 2006, 11(4): 1104-1127. [35] Ert E, Fleischer A, Magen N. Trust and reputation in the sharing economy: the role of personal photos in Airbnb[J]. Tourism Management, 2016, 55: 62-73. [36] 殷国鹏, 刘雯雯, 祝珊. 网络社区在线评论有用性影响模型研究——基于信息采纳与社会网络视角[J]. 图书情报工作, 2012(16): 140-147.Yin Guopeng, Liu Wenwen, Zhu Shan. What makes a helpful online review: the perspective of information adoption and social network[J]. Library and Information Service, 2012(16): 140-147. [37] Herr P M, Kardes F R, John K. Effects of word-of-mouth and product-attribute information on persuasion: an accessibility-diagnosticity perspective[J]. Journal of Consumer Research, 1991, 17(4): 454-462. [38] Bone P F. Word-of-mouth effects on short-term and long-term product judgments[J]. Journal of Business Research, 1995, 32(3): 213-223. [39] Park D H, Kim S. The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews[J]. Electronic Commerce Research & Applications, 2007, 7(4): 48-57. [40] Garbarino E, Strahilevitz M. Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation[J]. Journal of Business Research, 2004, 57(7): 768-775. [41] 张艳辉, 李宗伟. 在线评论有用性的影响因素研究:基于产品类型的调节效应[J]. 管理评论, 2016, 28(10): 123-132.Zhang Yanhui, Li Zongwei. Analysis of the factors that influence online reviews helpfulness: based on the regulating effect of product type[J]. Management Review, 2016, 28(10): 123-132. [42] 杜学美, 丁璟妤, 谢志鸿, 等. 在线评论对消费者购买意愿的影响研究[J]. 管理评论, 2016, 28(3): 173-183.Du Xuemei, Ding Jingyu, Xie Zhihong, et al. An empirical study on the impact of online reviews on consumers’ purchasing intention[J]. Management Review, 2016, 28(3): 173-183. [43] Baek H, Ahn J, Choi Y. Helpfulness of online consumer reviews: readers’ objectives and review cues[J]. International Journal of Electronic Commerce, 2014, 17(2): 99-126. [44] Guo Yue, Barnes S J, Jia Qiong. Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation[J]. Tourism Management, 2017, 59: 467-483. [45] 陶晓波, 张欣瑞, 杨建坤, 等.在线评论、感知有用性与新产品扩散的关系研究[J]. 中国软科学, 2017(7): 162-171.Tao Xiaobo, Zhang Xinrui, Yang Jiankun, et al. Online reviews, perceived usefulness and new product diffusion[J]. China Soft Science, 2017(7): 162-171. [46] Hu Nan, Koh N S, Reddy S K. Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales[J]. Decision Support Systems, 2014, 57: 42-53. [47] 石文华, 张绮, 蔡嘉龙. 在线评论矛盾性对消费者矛盾态度和购买意愿的影响研究[J]. 管理评论, 2018, 30(7): 77-88.Shi Wenhua, Zhang Qi, Cai Jialong. The impact of contradictory online reviews on ambivalent attitude and purchase intention[J]. Management Review, 2018, 30(7): 77-88. [48] 刘通, 张聪, 吴鸣远. 在线评论中基于边界平均信息熵的产品特征提取算法[J]. 系统工程理论与实践, 2016, 36(9): 2416-2423.Liu Tong, Zhang Cong, Wu Mingyuan. An algorithm of online product feature extraction based on boundary average entropy[J]. Systems Engineering-Theory & Practice, 2016, 36(9): 2416-2423. [49] 唐晓波, 胡华. 中文社会化媒体的本体概念抽取研究[J]. 情报科学, 2014, 32(4): 9-15.Tang Xiaobo, Hu Hua. Research on ontology extraction for Chinese social media[J]. Information Science, 2014, 32(4): 9-15. [50] Yi J, Nasukawa T,Bunescu R, et al. Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques[C]//Proceedings of IEEE International Conference on Data Mining, Melbourne, Florida, November 19-22, 2003. [51] 唐守忠, 齐建东. 一种结合关键词与共现词对的向量空间模型[J]. 计算机工程与科学, 2014, 36(5): 971-976.Tang Shouzhong, Qi Jiandong. Vector space model based on keywords and co-occurrence word pairs[J]. Computer Engineering & Science, 2014, 36(5): 971-976. [52] 吴光远, 何丕廉, 曹桂宏, 等. 基于向量空间模型的词共现研究及其在文本分类中的应用[J]. 计算机应用, 2003(S1): 138-140+145.Wu Guangyuan, He Pilian, Cao Guihong, et al. Word co-occurrence study based on vector space model and its application in text classification[J]. Computer Applications, 2003(S1): 138-140+145. [53] 任莉莉, 方元康. 基于词汇链与互信息的关键词抽取研究[J]. 池州学院学报, 2013, 27(6): 48-50.Ren Lili, Fang Yuankang. Study on keyword extraction based on word chain and mutual information[J]. Journal of Chizhou University, 2013, 27(6): 48-50. [54] 朱征宇, 孙俊华. 改进的基于《知网》的词汇语义相似度计算[J]. 计算机应用, 2013, 33(8): 2276-2279+2288.Zhu Zhengyu, Sun Junhua. Improved vocabulary semantic similarity calculation based on HowNet[J]. Journal of Computer Applications, 2013, 33(8): 2276-2279+2288. [55] 葛斌, 李芳芳, 郭丝路, 等. 基于知网的词汇语义相似度计算方法研究[J]. 计算机应用研究, 2010, 27(9): 3329-3333.Ge Bin, Li Fangfang, Guo Silu, et al. Word’s semantic similarity computation method based on HowNet[J]. Application Research of Computers, 2010, 27(9): 3329-3333. [56] 江敏, 肖诗斌, 王弘蔚, 等. 一种改进的基于《知网》的词语语义相似度计算[J]. 中文信息学报, 2008, 5(2): 59-76.Jiang Min, Xiao Shibin, Wang Hongwei, et al. An improved word similarity computing method based on HowNet[J]. Journal of Chinese Information Processing, 2008, 5(2): 59-76. [57] 韩雪婷, 李炜, 沈奇威. 用户评论中产品特征的抽取及聚类[J]. 计算机系统应用, 2013, 22(5): 188-192.Han Xueting, Li Wei, Shen Qiwei. Extracting and clustering product features from user reviews[J]. Computer Systems & Applications, 2013, 22(5): 188-192. [58] 杨源, 马云龙, 林鸿飞. 评论挖掘中产品属性归类问题研究[J]. 中文信息学报, 2012, 26(3): 104-108+115.Yang Yuan, Ma Yunlong, Lin Hongfei. Clustering product features in opinion mining[J]. Journal of Chinese Information Processing, 2012, 26(3): 104-108+115. [59] 邓楠, 余本功. 基于情感词向量和BLSTM的评论文本情感倾向分析[J]. 计算机应用研究, 2018, 35(12): 3547-3550.Deng Nan, Yu Bengong. Sentiment orientation analysis of review text based on sentiment word embedding and BLSTM[J]. Application Research of Computers, 2018, 35(12): 3547-3550. [60] 张洪. 聚类集成算法在客户细分中的研究及应用[D]. 合肥: 安徽大学, 2016.Zhang Hong. Research and application of clustering ensemble algorithm in customer segmentation[D]. Hefei: Anhui University, 2016. [61] 胡新明, 夏火松. 在线评论中用户商品属性偏好识别方法研究[J]. 情报杂志, 2012, 31(9): 197-201.Hu Xinming, Xia Huosong. Research of methods of recognizing user preference of product attributes from online product reviews[J]. Journal of Intelligence, 2012, 31(9): 197-201. |
[1] | 戢守峰, 刘红玉, 赵鹏云, 戢婷婷. 基于PI的企业动态库存补货模型与算法[J]. 中国管理科学, 2023, 31(2): 205-214. |
[2] | 李美娟, 卢锦呈. 一种新的基于双前沿面的交叉效率方法及其应用[J]. 中国管理科学, 2023, 31(1): 168-175. |
[3] | 刘祥官, 郜传厚, 罗世华, 王义康, 吴武林. 华罗庚管理科学与工业大数据分析的系统工程[J]. 中国管理科学, 2022, 30(11): 8-19. |
[4] | 姚锋敏, 闫颍洛, 滕春贤. 考虑CSR行为意识的闭环供应链运作与协调[J]. 中国管理科学, 2022, 30(11): 52-63. |
[5] | 曲国华, 王彬宇, 曲卫华, , 徐泽水, 张强. 基于对偶犹豫模糊几何Heronian平均算子的多属性决策方法及其应用[J]. 中国管理科学, 2022, 30(11): 216-228. |
[6] | 赵雪峰, 吴伟伟, 吴德林, 时辉凝, 廉莹, 赵德从. 基于TPCBoost模型的新型交通服务定价研究—以纽约网约车为实例[J]. 中国管理科学, 2022, 30(10): 210-223. |
[7] | 李坚飞, 孙梦霞, 李蓓. 零售服务供应链动态演化中存在“质量桥”吗?[J]. 中国管理科学, 2022, 30(8): 130-142. |
[8] | 苏兵, 周佳其, LIN Guohui, 姬浩, 徐阳. 应急救援物资紧缺的两级配送路径选择研究[J]. 中国管理科学, 2022, 30(8): 155-163. |
[9] | 张艳辉, 高云帆. 在线声誉水平对消费者关注度的影响:对大众点评的实证分析[J]. 中国管理科学, 2022, 30(8): 277-286. |
[10] | 代建生, 刘悦. 公平偏好和销售努力下供应链期权契约协调[J]. 中国管理科学, 2022, 30(7): 20-30. |
[11] | 黄肖玲, 陈焕平, 高晓楠, 王丹, 刘进平, 陈继红. 需求可拆分Milk-run与 Supply-hub协同的汽车零部件入厂物流优化[J]. 中国管理科学, 2022, 30(7): 59-68. |
[12] | 胡玉真, 李倩倩, 江山. 跨境电商企业海外仓选址多目标优化研究[J]. 中国管理科学, 2022, 30(7): 201-209. |
[13] | 刘洋, 邓前前, 樊治平, 张琦. 网约车平台峰时补贴策略研究[J]. 中国管理科学, 2022, 30(7): 210-220. |
[14] | 孙彩虹, 李肖依, 于辉. 跨国双向供应链物流合作模型分析[J]. 中国管理科学, 2022, 30(6): 66-76. |
[15] | 周正龙, 简昕格. 零售商嵌入专业技术的营销策略研究[J]. 中国管理科学, 2022, 30(6): 77-86. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||
|