Visualization System for Analyzing Customer Comments in Marketing Research Support System

Amusement parks are complex facilities with various attractions and shops, and many events for entertainment, making it a very difficult target for marketing research. Therefore, we proposed and built an opinion collection system using smartphones to efficiently collect user's opinions for improvement. In this paper, we propose and construct an opinion analysis and visualization system to support the grasping the situation of the amusement park, the planning of improvement. It is difficult for marketing researchers to extract important opinions from a large number of opinions. To cope with this problem, we propose a method to visualize emotional information of opinion on a map. The method classifies emotions for opinions based on Plutchik's wheel of emotions. The evaluation result by questionnaires is 4.75 on average out of 5 scales. The result shows that the proposed system can support the action of proposing business ideas.

[1]  Arafat Awajan,et al.  A Survey of Textual Emotion Detection , 2018, 2018 8th International Conference on Computer Science and Information Technology (CSIT).

[2]  Masafumi Hagiwara,et al.  Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese , 2017, ACML.

[3]  Rosa Meo,et al.  Processing Affect in Social Media , 2017, ACM Trans. Internet Techn..

[4]  H. H. Kassarjian.  Content Analysis in Consumer Research , 1977 .

[5]  Takashi Inui,et al.  Extracting Semantic Orientations of Words using Spin Model , 2005, ACL.

[6]  Yuji Matsumoto,et al.  Applying Conditional Random Fields to Japanese Morphological Analysis , 2004, EMNLP.

[7]  Andreas Kerren,et al.  The State of the Art in Sentiment Visualization , 2018, Comput. Graph. Forum.

[8]  T. Philipp,et al.  Interrater reliability. , 1985, The Journal of nursing administration.

[9]  P. Kotler,et al.  Principles of Marketing , 1983 .

[10]  R. Plutchik.  Emotion, a psychoevolutionary synthesis , 1980 .

[11]  淳 宗方,et al.  キャプション評価法による市民参加型景観調査 : 都市景観の認知と評価の構造に関する研究 その1 , 1999 .

[12]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[13]  R. Kolbe,et al.  Content-Analysis Research: An Examination of Applications with Directives for Improving Research Reliability and Objectivity , 1991 .

[14]  Marie Adele Hughes,et al.  Intercoder Reliability Estimation Approaches in Marketing: A Generalizability Theory Framework for Quantitative Data , 1990 .

[15]  Nigel Bradley,et al.  Marketing Research: Tools and Techniques , 2007 .

[16]  Fumiaki Saitoh,et al.  A Quality Table-Based Method for Sentiment Expression Word Identification in Japanese , 2017, HCI.

[17]  Concetto Spampinato,et al.  Assessment and visualization of geographically distributed event-related sentiments by mining social networks and news , 2016, 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[18]  Yasufumi Takama,et al.  Visualization system for analyzing user opinion , 2015, 2015 IEEE/SICE International Symposium on System Integration (SII).

[19]  Dominique Genoud,et al.  Mining and Visualizing Social Data to Inform Marketing Decisions , 2016, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA).