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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (6): 22-31    DOI: 10.11925/infotech.2096-3467.2021.1261
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Creating Consumer Psychology Portrait with Aspect Words
Xiao Hanqiong,Zhang Xinyu,Xiao Yuhan,Lin Huiping()
School of Software and Microelectronics, Peking University, Beijing 102600, China
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Abstract  

[Objective] This paper proposes a new method to explore consumer psychology and their preferences based on online comments, aiming to address the difficulties of drawing personality-based consumer portraits. [Methods] Firstly, we mapped relationship among the experience levels, product features and aspect words. Then, we extracted aspect words from user comments to examine their attentions at different experience levels. Third, we categorized users with their instinctual, behavioral, and reflective preferences. Finally, we utilized deep learning-based aspect sentiment analysis technology to examine user’s preferences for products. [Results] We evaluated our new model with more than 900 000 reviews on mobile phones from JD.com. Among them, users with instinctual preferences accounted for 41.60%, which was higher than behavioral preferences (33.01%) and reflective preferences (25.39%). We also analyzed their purchasing behaviors from the perspectives of brands and prices. [Limitations] We only collected review data on mobile phones sold by JD.com. More products and platforms need to be examined with our new model in the future. [Conclusions] The new model for creating user portraits can identify the preferences of different groups of consumers.
Key words： Three Level Experience Theory      Consumer Psychology      User Portrait      Product Features      Aspect Words      Sentiment Analysis     
Received: 04 November 2021      Published: 31 December 2021
ZTFLH:  TP391  
Fund:National Key R&D Program of China(2018YFB1702900)
Corresponding Authors: Lin Huiping     E-mail: linhp@ss.pku.edu.cn
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Hanqiong Xiao
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Cite this article:

Xiao Hanqiong, Zhang Xinyu, Xiao Yuhan, Lin Huiping. Creating Consumer Psychology Portrait with Aspect Words. Data Analysis and Knowledge Discovery, 2022, 6(6): 22-31.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1261     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I6/22

Methods of Creating User Portraits
三层次体验理论 在手机设计情境下的释义 产品特征 方面词（举例） 显式词数量 隐式词数量
本能层 使用手机前或轻度使用时可以获取的信息及感受 外观 颜色、边框、曲面 53 27
屏幕 显示、画质、清晰度 29 3
声音 音质、音量、杂音 19 0
价格 价位、性价比、降价 21 14
参数 GB、内存、芯片 22 0
行为层 手机的大部分功能的使用体验 性能 黑屏、游戏、速度 26 9
电池 电池、电量、续航 20 0
摄像 像素、夜景、美颜 26 0
通讯 网速、无线、信号 31 0
辅助功能 人脸识别、指纹、蓝牙 41 3
反思层 用户对自我形象和产品形象的认知 品牌 华为、小米、苹果 30 0
服务 店铺、物流、售后 51 1
使用者 老人、孩子、女生 34 0
整体感受 科技、国产、人性化 26 31
配件 耳机、数据线、手环 18 0
Experience Level - Product Feature - Aspect Word Mapping Table Established Based on the Three-Level Experience Theory （Example）
本能偏好用户 行为偏好用户 反思偏好用户
屏幕 速度 京东
外形外观 拍照 老人
速度 待机时间 客服
拍照 屏幕 物流
音效 外形外观 质量
待机时间 音效 华为
外观 卡 速度
声音 充电 快递
手感 电池 耳机
High-Frequency Aspect Words in Comments of Three-Level User Groups
品牌 喜爱度
本能偏好用户 行为偏好用户 反思偏好用户
苹果 1.570 363 1.314 607 1.326 808
小米 1.614 227 1.317 204 1.276 268
红米 1.615 018 1.284 013 1.325 327
华为 1.667 817 1.493 053 1.420 238
荣耀 1.619 561 1.424 737 1.356 480
OPPO 1.756 583 1.491 000 1.544 588
VIVO 1.711 509 1.488 905 1.413 475
飞利浦 1.556 918 1.198 661 1.596 631
Three-Level User Groups’ Preference Ratings for Different Brands of Mobile Phones
价位（元） 喜爱度
本能偏好用户 行为偏好用户 反思偏好用户
1 000以下 1.625 137 1.212 833 1.545 395
1 000~2 000 1.667 669 1.427 243 1.410 421
2 000~3 000 1.642 409 1.431 043 1.346 138
3 000~5 000 1.603 784 1.387 616 1.304 926
5 000以上 1.614 496 1.404 115 1.323 678
Three-Level User Groups’ Preference Ratings for Different Prices of Mobile Phones
特征 喜爱度
本能偏好用户 行为偏好用户 反思偏好用户
外观 1.312 435 1.165320 1.041837
屏幕 1.297 634 1.165610 1.005272
声音 1.410 963 1.198 691 1.065 646
价格 0.955 867 1.007 628 0.972 449
参数 1.156 625 1.048 055 1.018 367
性能 1.338 706 0.900 032 1.029 082
电池 1.182 451 1.015 084 1.011 395
摄像 1.249 389 1.030 503 1.012 517
通讯 0.986 910 0.813 709 0.959 184
辅助功能 1.063 318 0.953 914 0.999 660
整体感受 1.112 557 1.045 080 0.923 129
品牌 1.168 774 1.048 695 0.960 002
服务 0.941 993 0.928 649 0.584 262
配件 0.962 731 0.979 723 0.709 796
使用者 1.019 178 1.002 288 0.986 735
Three-Level User Groups’ Preference Ratings for Each Feature of Mi 10
[1] Goldberg L R. An Alternative “Description of Personality”: The Big-Five Factor Structure[J]. Journal of Personality and Social Psychology, 1990, 59(6): 1216-1229.
pmid: 2283588
[2] Carducci G, Rizzo G, Monti D, et al. TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning[J]. Information, 2018, 9(5): 127.
[3] Deeva I. Computational Personality Prediction Based on Digital Footprint of a Social Media User[J]. Procedia Computer Science, 2019, 156: 185-193.
[4] 唐纳德•A•诺曼. 设计心理学3:情感化设计[M]. 何笑梅, 欧秋杏译. 第1版. 北京: 中信出版社, 2012.
[4] (Norman D A. Emotional Design[M]. Translated by He Xiaomei, Ou Qiuxing. The 1st Edition. Beijing: China CITIC Press, 2012.)
[5] Nelson P. Advertising as Information[J]. Journal of Political Economy, 1974, 82(4): 729-754.
[6] 刘海鸥, 孙晶晶, 苏妍嫄, 等. 国内外用户画像研究综述[J]. 情报理论与实践, 2018, 41(11): 155-160.
[6] (Liu Haiou, Sun Jingjing, Su Yanyuan, et al. Literature Review of Persona at Home and Abroad[J]. Information Studies: Theory & Application, 2018, 41(11): 155-160.)
[7] Zhao P H, Ding Z J, Wang M M, et al. Behavior Analysis for Electronic Commerce Trading Systems: A Survey[J]. IEEE Access, 2019, 7: 108703-108728.
[8] Zheng J X, Li D Y, Arun Kumar S. Group User Profile Modeling Based on Neural Word Embeddings in Social Networks[J]. Symmetry, 2018, 10(10): 435.
[9] Zarrinkalam F, Kahani M, Bagheri E. User Interest Prediction over Future Unobserved Topics on Social Networks[J]. Information Retrieval Journal, 2019, 22(1-2): 93-128.
doi: 10.1007/s10791-018-9337-y
[10] Liu X T, Xu A B, Akkiraju R, et al. Understanding Purchase Behaviors Through Personality-Driven Traces[C]// Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2017: 1837-1843.
[11] Yarkoni T. Personality in 100,000 Words: A Large-Scale Analysis of Personality and Word Use Among Bloggers[J]. Journal of Research in Personality, 2010, 44(3): 363-373.
pmid: 20563301
[12] Qiu L, Lin H, Ramsay J, et al. You are What You Tweet: Personality Expression and Perception on Twitter[J]. Journal of Research in Personality, 2012, 46(6): 710-718.
[13] Lee C H, Kim K, Seo Y S, et al. The Relations Between Personality and Language Use[J]. The Journal of General Psychology, 2007, 134(4): 405-413.
[14] Jayaratne M, Jayatilleke B. Predicting Personality Using Answers to Open-Ended Interview Questions[J]. IEEE Access, 2020, 8: 115345-115355.
[15] Sun J, Schwartz H A, Son Y, et al. The Language of Well-Being: Tracking Fluctuations in Emotion Experience Through Everyday Speech[J]. Journal of Personality and Social Psychology, 2020, 118(2): 364-387.
[16] 韩忠明, 李梦琪, 刘雯, 等. 网络评论方面级观点挖掘方法研究综述[J]. 软件学报, 2018, 29(2): 417-441.
[16] (Han Zhongming, Li Mengqi, Liu Wen, et al. Survey of Studies on Aspect-Based Opinion Mining of Internet[J]. Journal of Software, 2018, 29(2): 417-441.)
[17] Blei D M, Ng A, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[18] Blei D M, McAuliffe J D. Supervised Topic Models[C]// Proceedings of the 20th International Conference on Neural Information Processing Systems. 2007: 121-128.
[19] Das S J, Chakraborty B. An Approach for Automatic Aspect Extraction by Latent Dirichlet Allocation[C]// Proceedings of the 10th International Conference on Awareness Science and Technology. IEEE, 2019: 1-6.
[20] Ekinci E, Omurca S I. NET-LDA: A Novel Topic Modeling Method Based on Semantic Document Similarity[J]. Turkish Journal of Electrical Engineering & Computer Sciences, 2020, 28(4): 2244-2260.
[21] Xia L X, Wang Z Y, Chen C, et al. Research on Feature-Based Opinion Mining Using Topic Maps[J]. The Electronic Library, 2016, 34(3): 435-456.
[22] 肖宇晗, 林慧苹, 汪权彬, 等. 基于双特征嵌套注意力的方面词情感分析算法[J]. 智能系统学报, 2021, 16(1): 142-151.
[22] (Xiao Yuhan, Lin Huiping, Wang Quanbin, et al. An Algorithm for Aspect-Based Sentiment Analysis Based on Dual Features Attention-over-Attention[J]. CAAI Transactions on Intelligent Systems, 2021, 16(1): 142-151.)
[23] Tang D Y, Qin B, Feng X C, et al. Effective LSTMs for Target-Dependent Sentiment Classification[OL]. arXiv Preprint, arXiv:1512.01100.
[24] Devlin J, Chang M W, Lee K, et al. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv: 1810.04805.
[25] Karimi A, Rossi L, Prati A, et al. Adversarial Training for Aspect-Based Sentiment Analysis with BERT[C]// Proceedings of the 25th International Conference on Pattern Recognition. IEEE, 2021: 8797-8803.
[26] 刘典. 基于情感化设计三层次理论的哈曼卡顿音箱设计研究[D]. 长沙: 湖南大学, 2017.
[26] (Liu Dian. Research on Harman Kardon Speaker Based on the Three Levels of Emotional Design[D]. Changsha: Hunan University, 2017.)
[27] 鲍玉雪, 林婷雯, 王军. 基于心智模型的层次化功能联合体设计研究[J]. 工业设计, 2020(1): 61-62.
[27] (Bao Yuxue, Lin Tingwen, Wang Jun. Research on Design Strategy of HFC Based on Mental Model[J]. Industrial Design, 2020(1): 61-62.)
[28] Fei G, Liu B, Hsu M, et al. A Dictionary Based Approach to Identifying Aspects Implied by Adjectives for Opinion Mining[C]// Proceedings of the 24th International Conference on Computational Linguistics. 2012: 309-318.
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