Please wait a minute...
Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (10): 12-20    DOI: 10.11925/infotech.2096-3467.2017.0313
Orginal Article Current Issue | Archive | Adv Search |
Analyzing Characteristics of Weibo Users Based on Their Sentiments and Influences —— Case Study of Cell Phone Brand
He Yue, Yin Xiaojia(), Zhu Chao
Business School, Sichuan University, Chengdu 610064,China
Download: PDF (709 KB)   HTML ( 5
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study tries to identify the characteristics of consumers, aiming to improve the performance of accurate marketing. [Methods] First, we conducted sentiment analysis of the Weibo texts. Then, we divided the Weibo users into nine groups with Ward clustering technique, and identified their influences. Thirdly, we analyzed each user group from the perspectives of sentiment and influence. Finally, we extracted the users’ characteristics with a modified customer value matrix. [Results] We found significant differences among users’ sentiments on a specific cell phone brand. The fashion-chasing women and IT industry workers were in favor of this brand. They could also convince members of other groups choose the same brand. [Limitations] We only included the common indicators to examine Weibo users’ influences. [Conclusions] The proposed method could effectively identify consumers’ characteristics and promote accurate marketing.

Key wordsGroup Feature Analysis      Sentiment Analysis      User Influence Identification      Customer Value Matrix     
Received: 19 April 2017      Published: 08 November 2017
ZTFLH:  G353.12  

Cite this article:

He Yue,Yin Xiaojia,Zhu Chao. Analyzing Characteristics of Weibo Users Based on Their Sentiments and Influences —— Case Study of Cell Phone Brand. Data Analysis and Knowledge Discovery, 2017, 1(10): 12-20.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0313     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I10/12

类目名称 情感值 表情符号
很好 2.5 笑哈哈; 大笑; 嘻嘻; 爱你; 给力; 威武; 顶; 鼓掌; 赞; good; gst耐你; 好开心
2 花心; 可怜; 好激动; 江南style; 偷笑; 亲亲; 抱抱; 挤眼; ala加油; 爱心; 耶
较好 1.5 It切克闹; din推撞; 兔子; 互粉; 礼物; 微笑; 可爱; 钱; 嘴馋; ok; ala蹦; 害羞;
稍好 0.5 转发; 围观; 熊猫; 奥特曼; 酷; 猪头; 蜡烛; 坏笑; 勾引
没感觉 0 抠鼻; 浮云; 神马; 时间; 话筒; 疑问; 思考; 国旗;
较差 -1.5 晕; 黑线; 流汗; 囧; 困; 睡觉; 打哈欠; 左哼哼; 右哼哼; 吃惊; 闭嘴; 懒得理你
-2 快哭了; 草泥马; xb压力; 吐血; 衰; 委屈; 吐; 生病; 巨汗; 非常汗; 悲催; 石化; 结冰; 给跪了
很差 -2.5 怒; 怒骂; 抓狂; 崩溃; 哼; 流泪; 鄙视; 失望; 狂躁症; 弱
轮次 Kappa值
第1轮 0.46
第2轮 0.59
第3轮 0.75
第4轮 0.81
评估参数 传统算法
得到的结果
改进后的算法
得到的结果
Macro-P 0.7362 0.8457
Macro-R 0.7498 0.8590
群体关键字 用户数目 主要特征
投资者 308 1、主要是金融行业从业者; 大多为男性;
2、主要来自于北京、上海、广东和香港等经济发达地区;
3、微博主要通过iPhone手机客户端发布;
4、主要集中在35-45岁和45-55岁两个年龄段。
IT业精英 209 1、主要是移动互联网和IT企业的企业主和管理层;
2、主要来自于北京和广东两个地区;
3、微博主要通过iPhone、三星Galaxy手机客户端和其他Android系统平台发布, 其中
包含少量小米手机, 但比重仅占到8%;
4、主要集中在35-45岁年龄段; 大多为男性。
宅男 465 1、主要集中在15-25岁和25-35岁两个年龄段;
2、微博主要通过个人电脑或者是类似塞班这样的老式智能手机系统发布。
IT从业人员 916 1、主要是IT企业官方微博和IT从业人员;
2、主要来自于北京和广东两个地区;
3、微博主要通过三星Galaxy, 小米手机客户端和其他Android系统平台发布, 小米手机比重为33%;
4、主要集中在25-35岁和35-45岁两个年龄段。
群体关键字 用户数目 主要特征
时尚女性 640 1、时尚杂志官方微博, 企业白领和主要从事模特、设计师等工作的时尚潮流女士;
2、主要来自于北京、上海、香港和海外;
3、微博主要通过iPhone和三星Galaxy手机客户端发布;
4、主要集中在15-25岁和25-35岁两个年龄段。
大龄消费者 378 1、微博主要通过三星Galaxy、小米手机客户端、塞班和其他Android系统平台发布, 小米手机比重为0.02%;
2、年龄段主要集中在35-45岁以及45-55岁两个年龄段。
智能手机发烧友 552 1、主要是智能手机论坛官方微博以及智能手机分析师、发烧友;
2、主要来自于北京、上海和广东三个地区;
3、主要集中于25-35岁年龄段。
宅女 551 1、微博主要通过个人电脑或者是类似于塞班这样的老式智能手机系统发布;
2、主要集中在15-25岁和25-35岁两个年龄段。
青年学生 981 1、主要集中在15-25岁年龄段。
等级 1级 2级 3级
粉丝数 $\left[ 10000,+\infty \right)$ $\left[ 1000,10000 \right)$ $\left[ 0,1000 \right)$
评论数 $\left[ 50,+\infty \right)$ $\left[ 1,50 \right)$ 0
转发数 $\left[ 100,+\infty \right)$ $\left[ 1,100 \right)$ 0
粉丝数/关注数 $\left[ 100,+\infty \right)$ $\left[ 1,100 \right)$ 0
粉丝数/微博数 $\left[ 50,+\infty \right)$ $\left[ 2,50 \right)$ $\left[ 0,2 \right)$
用户名 粉丝数(个) 评论数(条) 转发数(条) 粉丝数/
关注数
粉丝数/
微博数
A 11 305 4 92 25.1222 25.3475
B 42 984 54 200 55.4632 7.4547
C 147 906 0 891 68.3897 7.5824
D 121 846 130 906 74.1607 14.0262
E 1 050 3 7 2.4083 0.2385
F 1 123 4 0 0.5831 2.0912
[1] 中国互联网络信息中心. 第39次中国互联网络发展状况统计报告[R/OL]. [2017-01-22].
[1] (China Internet Network Information Center. The 33rd Statistical Report on Internet Development in China [R/OL]. [2017-01-22].
[2] Li Q.Characteristics and Social Impact of the Use of Social Media by Chinese Dama[J]. Telematics and Informatics, 2017, 34(3): 797-810.
doi: 10.1016/j.tele.2016.05.020
[3] Koustuv S, Ingmar W.Characterizing Awareness of Schizophrenia Among Facebook Users by Leveraging Facebook Advertisement Estimates[J]. Journal of Medical Internet Research, 2017,19(5): e156. DOI: 10.2196/jmir.6815.
doi: 10.2196/jmir.6815 pmid: 28483739
[4] Gonzalez-Pardo A, Jung J J, Camacho D.ACO-based Clustering for Ego Network Analysis[J]. Future Generation Computer Systems, 2017, 66: 160-170.
doi: 10.1016/j.future.2016.06.033
[5] Han S C, Chen H L, Zhang Z J.Influence Model of User Behavior Characteristics on Information Dissemination[J]. International Journal of Computers Communications & Control, 2016, 11(2): 209-223.
doi: 10.15837/ijccc.2016.2.2441
[6] Step M M, Bracken C C, Trapl E S, et al.User and Content Characteristics of Public Tweets Referencing Little Cigars[J]. American Journal of Health Behavior, 2016, 40(1): 38-47.
doi: 10.5993/AJHB.40.1.5 pmid: 26685812
[7] 曾鸿, 吴苏倪. 基于微博的大数据用户画像与精准营销[J]. 现代经济信息, 2016(16): 306-308.
[7] (Zeng Hong, Wu Suni.Based on Microblogging Large Data User Portrait and Precise Marketing[J]. Modern Economic Information, 2016(16): 306-308.)
[8] 彭希羡, 朱庆华, 刘璇. 微博客用户特征分析及分类研究——以“新浪微博”为例[J]. 情报科学, 2015, 33(1): 69-75.
[8] (Peng Xixian, Zhu Qinghua, Liu Xuan.Research on Behavior Characteristics and Classification of Micro-blog Users— Taking “Sina Micro-blog”as an Example[J]. Information Science, 2015, 33(1): 69-75.)
[9] 陈梅梅, 董平军. 中国网络消费者行为特征[J]. 中国流通经济, 2017, 31(2): 80-85.
[9] (Chen Meimei, Dong Pingjun.Behavior Analysis of Chinese Internet Consumer[J]. China Circulation Economics, 2017, 31(2): 80-85.)
[10] 符丹, 刘洪超. “海淘族”的发展与群体特征[J]. 学术探索, 2016(12): 50-55.
[10] (Fu Dan, Liu Hongchao.The Development and Group Characteristics of International Shoppers in China[J]. Academic Exploration, 2016(12): 50-55.)
[11] 张继东. 移动社交网络环境下基于情景化偏好的用户行为感知研究[J]. 情报理论与实践, 2017, 40(1): 110-114.
[11] (Zhang Jidong.Study on User Behavior Perception Based on Situational Preference in Mobile Social Network Environment[J]. Information Studies: Theory & Application, 2017, 40(1): 110-114.)
[12] Giatsoglou M, Vozalis M G.Sentiment Analysis Leveraging Emotions and Word Embeddings[J]. Expert Systems with Applications, 2017,69: 214-224.
doi: 10.1016/j.eswa.2016.10.043
[13] Suresh H, Raj S G.An Unsupervised Fuzzy Clustering Method for Twitter Sentiment Analysis[C]// Proceedings of the 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). 2016: 80-85.
[14] Jendoubi S, Martin A.Two Evidential Data Based Models for Influence Maximization in Twitter[J]. Knowledge-based Systems, 2017,121: 58-70.
doi: 10.1016/j.knosys.2017.01.014
[15] Francalanci C, Hussain A.Influence-based Twitter Browsing with NavigTweet[J]. Information Systems, 2017, 64:119-131.
doi: 10.1016/j.is.2016.07.012
[16] Lahuerta-Otero E.Looking for the Perfect Tweet. The Use of Data Mining Techniques to Find Influencers on Twitter[J]. Computers in Human Behavior, 2016, 64: 575-583.
doi: 10.1016/j.chb.2016.07.035
[17] 贺飞艳, 何炎祥, 刘楠, 等. 面向微博短文本的细粒度情感特征抽取方法[J]. 北京大学学报: 自然科学版, 2016, 50(1): 48-54.
[17] (He Feiyan, He Yanxiang, Liu Nan, et al.A Microblog Short Text Oriented Multi-class Feature Extraction Method of Fine-Grained Sentiment Analysis[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2016, 50(1): 48-54.)
[18] 刘洋. 汉语转折关联词语语义背景探析及教学应用[D]. 济南: 山东大学, 2010.
[18] (Liu Yang.The Semantic Backgrounds Study of Adversative Words and Expressions and Application in Chinese Teaching[D]. Ji’nan: Shandong University, 2010.)
[19] Allen B, Reser D.Content Analysis in Library and Information Science Research[J]. Library & Information Science Research, 1990,12(3): 251-262.
doi: 10.1002/(SICI)1097-4571(199007)41:5<390::AID-ASI12>3.0.CO;2-G
[20] 朱郭峰, 杨彦, 周竹荣, 等. 基于领域的微博用户影响力计算方法[J]. 西南大学学报: 自然科学版, 2014, 78(3): 145-151.
[20] (Zhu Guofeng, Yang Yan, Zhou Zhurong, et al.Calculation Method of User Influence Based on Domain[J]. Journal of Southwestern University: Natural Science Edition, 2014, 78(3): 145-151.)
[21] 原福永, 冯静, 符茜落. 微博用户的影响力指数模型[J]. 现代图书情报技术, 2012(6): 60-64.
[21] (Yuan Fuyong, Feng Jing, Fu Qianluo.Influence Index Model of Micro-blog User[J]. New Technology of Library and Information Service, 2012(6): 60-64.)
[22] 冯波, 郝文宁, 陈刚, 等. K-means算法初始聚类中心选择的优化[J]. 计算机工程与应用, 2013, 49(14): 182-185,192.
doi: 10.3778/j.issn.1002-8331.1111-0289
[22] (Feng Bo, Hao Wenning, Chen Gang, et al.Optimization to K-means Initial Cluster Centers[J]. Computer Engineering and Applications, 2013, 49(14): 182-185, 192.)
doi: 10.3778/j.issn.1002-8331.1111-0289
[23] 曹庆垒, 李琴, 李丽杰. 基于未确知测度模型的高新区技术创新能力评价研究[J]. 科技管理研究, 2008, 28(5): 134-135.
doi: 10.3969/j.issn.1000-7695.2008.05.044
[23] (Cao Qinglei, Li Qin, Li Lijie.Evaluation of Technological Innovation Capability of High-tech Zones Based on Unascertained Measurement Model[J]. Science and Technology Management Research, 2008, 28(5): 134-135.)
doi: 10.3969/j.issn.1000-7695.2008.05.044
[24] 章煜溢, 徐德华. 基于BSC和未确知测度理论的C2C网商绩效评价模型研究——以淘宝网店铺数据为例[J]. 经营管理者, 2017(4): 4-5.
[24] (Zhang Yuyi, Xu Dehua.Study on Performance Evaluation Model of C2C Network Business Based on BSC and Unascertained Measure Theory - Taking Taobao Store Data as an Example[J]. Management Manager, 2017(4): 4-5.)
[25] 周荣虎. 基于信息熵和未确知测度理论的供应链风险系数定量测度模型研究[J]. 中国市场, 2016(45): 52-54.
[25] (Zhou Ronghu.Study on Quantitative Model of Supply Chain Risk Coefficient Based on Information Entropy and Unascertained Measure Theory[J]. China Market, 2016(45): 52-54.)
[26] Shanon C E, Weaver W.The Mathematical Theory of Communication [M]. The University of Illinois Press, 1971.
[27] 薛宇, 吴凤平, 王长青, 等. 基于离差最大化和Ward系统聚类的医疗服务水平研究[J]. 统计与决策, 2014(16): 86-88.
[27] (Xue Yu, Wu Fengping, Wang Changqing, et al.Research on Medical Service Level Based on Maximizing Deviations and Clustering Ward Systems[J]. Statistics and Decision, 2014(16): 86-88.)
[1] Xu Yuemei, Wang Zihou, Wu Zixin. Predicting Stock Trends with CNN-BiLSTM Based Multi-Feature Integration Model[J]. 数据分析与知识发现, 2021, 5(7): 126-138.
[2] Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
[3] Liu Tong,Liu Chen,Ni Weijian. A Semi-Supervised Sentiment Analysis Method for Chinese Based on Multi-Level Data Augmentation[J]. 数据分析与知识发现, 2021, 5(5): 51-58.
[4] Wang Yuzhu,Xie Jun,Chen Bo,Xu Xinying. Multi-modal Sentiment Analysis Based on Cross-modal Context-aware Attention[J]. 数据分析与知识发现, 2021, 5(4): 49-59.
[5] Li Feifei,Wu Fan,Wang Zhongqing. Sentiment Analysis with Reviewer Types and Generative Adversarial Network[J]. 数据分析与知识发现, 2021, 5(4): 72-79.
[6] Chang Chengyang,Wang Xiaodong,Zhang Shenglei. Polarity Analysis of Dynamic Political Sentiments from Tweets with Deep Learning Method[J]. 数据分析与知识发现, 2021, 5(3): 121-131.
[7] Zhang Mengyao, Zhu Guangli, Zhang Shunxiang, Zhang Biao. Grouping Microblog Users of Trending Topics Based on Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(2): 43-49.
[8] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[9] Lv Huakui,Liu Zhenghao,Qian Yuxing,Hong Xudong. Relationship Between Financial News and Stock Market Fluctuations[J]. 数据分析与知识发现, 2021, 5(1): 99-111.
[10] Xu Hongxia,Yu Qianqian,Qian Li. Studying Content Interaction Data with Topic Model and Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(7): 110-117.
[11] Jiang Lin,Zhang Qilin. Research on Academic Evaluation Based on Fine-Grain Citation Sentimental Quantification[J]. 数据分析与知识发现, 2020, 4(6): 129-138.
[12] Shi Lei,Wang Yi,Cheng Ying,Wei Ruibin. Review of Attention Mechanism in Natural Language Processing[J]. 数据分析与知识发现, 2020, 4(5): 1-14.
[13] Li Tiejun,Yan Duanwu,Yang Xiongfei. Recommending Microblogs Based on Emotion-Weighted Association Rules[J]. 数据分析与知识发现, 2020, 4(4): 27-33.
[14] Shen Zhuo,Li Yan. Mining User Reviews with PreLM-FT Fine-Grain Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(4): 63-71.
[15] Xue Fuliang,Liu Lifang. Fine-Grained Sentiment Analysis with CRF and ATAE-LSTM[J]. 数据分析与知识发现, 2020, 4(2/3): 207-213.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn