博碩士論文 108421045 詳細資訊




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姓名 陳俞君(YU-CHUN CHEN)  查詢紙本館藏   畢業系所 企業管理學系
論文名稱 以社群網站照片顏色預測人格特質之研究
(Study of predicting personality traits through social media image color)
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摘要(中) 人格特質是人類行為中心部分,由情感、行為、思維模式、情感組成,使人成為獨一無二的人。依照extended real-life hypothesis提到社群網站可能構成一個擴展的社會環境,因為社群網站集成了各種個人訊息,這些訊息都與人格特質相關。透過人格特質來了解不同群體,以便提高廣告、促銷、推薦以及其他行銷目的的有效性甚至是個人化,也有助於企業招募人才、衡量工作績效,或是其他用途。
在顏色心理學中顏色偏好被認為是影響人潛在人格的關鍵,過去從社群網站上的使用者照片顏色判斷人格特質的研究中,尚未有以顏色心理學提出的顏色應用在社群網站的照片與人格特質做連結,因此本研究想探討透過顏色心理學的角度是否能更好的了解人格特質;加上,過去研究將照片顏色是以色相取平均值作為輸入,遭到其他研究駁斥,因此本研究假設透過一張照片中單個顏色成分最多的部分作為的主色相(dominant color)可以更好的區分圖像中的主要區域。
本研究蒐集受測者社群網站上1080張照片,萃取照片主色調以顏色心理學連結,再以支持向量機(SVM)、隨機森林(Random forest)訓練預測模型並預測受測者之人格特質。
實驗結果首先證實了顏色心理學所述顏色偏好影響人格特質;其次是以照片主色調更可以預測人格特質,使用隨機森林最好的預測準確率高達0.95;最重要的發現是使用資料集一,可使預測準確率大幅提升。
摘要(英) Personality trait is the central part of human behavior. Social networking sites integrate all kinds of personal information, which are related to personality traits. Through personality traits to understand different groups, in order to improve the effectiveness and even personalization of advertising, promotion, recommendation and other marketing purposes, it also helps enterprises to recruit talents, measure work performance, or other purposes.
In color psychology, color preference is considered to be the key to affect a person′s potential personality. In the past, the research on judging personality traits from the color of users′ photos on social networking sites has not yet linked the photos and personality traits of social networking sites with the application of color proposed by color psychology, Therefore, this study wants to explore whether we can better understand personality traits through the perspective of color psychology; In addition, previous studies used the average value of hue as the input of image color, which was refuted by other studies. Therefore, this study hypothesized that the dominant color of a single image with the most color components could better distinguish the main areas in the image.
In this study, 1080 photos on social networking sites were collected. The main colors of the photos were extracted and connected by color psychology. Then SVM and random forest were used to train the prediction model and predict the personality traits of the subjects.
The results of the experiment firstly confirmed that color preference affects personality traits according to color psychology; Secondly, the main tone of the photo can predict personality traits, and the best prediction accuracy of random forest is 0.95; The most important finding is that when using dataset 1, the prediction accuracy can be greatly improved.
關鍵字(中) ★ 社群網站照片
★ 顏色心理學
★ 人格特質
★ 主色調
★ 支持向量機
★ 隨機森林
關鍵字(英) ★ social media images
★ color psychology
★ personality traits
★ dominant color
★ SVM
★ Random forest
論文目次 中文摘要 ii
ABSTRACT iii
目錄 iv
圖目錄 v
表目錄 vi
一、緒論 1
1-1  研究背景 1
1-2  研究動機 2
1-3  研究目的 4
1-4  研究架構 5
二、文獻探討 6
2-1 社群網站上的照片 6
2-2 五大人格特質 8
2-3  顏色心理學 10
2-4  照片低階特徵 13
2-5  支持向量機(SVM) 15
2-6  隨機森林(Random Forest) 16
三、研究方法 18
3-1  變數 18
3-2  分類預測 22
四、實驗 26
4-1  資料收集 26
4-2  前處理 27
4-3  實驗結果 30
五、討論 33
六、結論與未來研究建議 35
6-1  研究結論 35
6-2  研究限制與未來研究建議 35
參考文獻 36
參考文獻 〔1.〕 Kim, J. H., Kim, M.-S., & Nam, Y. (2010). An analysis of self-construals, motivations, Facebook use, and user satisfaction. Intl. Journal of Human–Computer Interaction, 26(11-12), 1077-1099.
〔2.〕 Ahmad, N., & Siddique, J. (2017). Personality assessment using Twitter tweets. Procedia Computer Science, 112, 1964-1973.
〔3.〕 Back, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., & Gosling, S. D. (2010). Facebook profiles reflect actual personality, not self-idealization. Psychological science, 21(3), 372-374.
〔4.〕 Neidhardt, J., Schuster, R., Seyfang, L., & Werthner, H. (2014). Eliciting the users′ unknown preferences. Paper presented at the Proceedings of the 8th ACM Conference on Recommender systems.
〔5.〕 Furnham, A., & Crump, J. (2015). The myers-briggs type indicator (mbti) and promotion at work. Psychology, 6(12), 1510.
〔6.〕 Biel, J.-I., Aran, O., & Gatica-Perez, D. (2011). You are known by how you vlog: Personality impressions and nonverbal behavior in youtube. Paper presented at the Proceedings of the International AAAI Conference on Web and Social Media.
〔7.〕 Dandannavar, P., Mangalwede, S., & Kulkarni, P. (2018). Social Media Text-A Source for Personality Prediction. Paper presented at the 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS).
〔8.〕 Yu, C.-E., Xie, S. Y., & Wen, J. (2020). Coloring the destination: The role of color psychology on Instagram. Tourism management, 80, 104110.
〔9.〕 Evans, D. C., Gosling, S. D., & Carroll, A. (2008). What elements of an online social networking profile predict target-rater agreement in personality impressions? Paper presented at the ICWSM.
〔10.〕 Kim, Y., & Kim, J. H. (2018). Using computer vision techniques on Instagram to link users’ personalities and genders to the features of their photos: An exploratory study. Information Processing & Management, 54(6), 1101-1114.
〔11.〕 Liu, L., Preotiuc-Pietro, D., Samani, Z. R., Moghaddam, M. E., & Ungar, L. (2016). Analyzing personality through social media profile picture choice. Paper presented at the Proceedings of the International AAAI Conference on Web and Social Media.
〔12.〕 Lüscher, M. (1971). The Luscher color test: Simon and Schuster.
〔13.〕 Ferwerda, B., Schedl, M., & Tkalcic, M. (2016). Using instagram picture features to predict users’ personality. Paper presented at the International Conference on Multimedia Modeling.
〔14.〕 Skowron, M., Tkalčič, M., Ferwerda, B., & Schedl, M. (2016). Fusing social media cues: personality prediction from twitter and instagram. Paper presented at the Proceedings of the 25th international conference companion on world wide web.
〔15.〕 Labrecque, L. I., Patrick, V. M., & Milne, G. R. (2013). The marketers’ prismatic palette: A review of color research and future directions. Psychology & Marketing, 30(2), 187-202.
〔16.〕 Ekin, A., & Tekalp, A. M. (2003). Robust dominant color region detection and color-based applications for sports video. Paper presented at the Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429).
〔17.〕 Kim, J. H., & Kim, Y. (2019). Instagram user characteristics and the color of their photos: Colorfulness, color diversity, and color harmony. Information Processing & Management, 56(4), 1494-1505.
〔18.〕 Utami, N. A., Maharani, W., & Atastina, I. (2021). Personality Classification of Facebook Users According to Big Five Personality Using SVM (Support Vector Machine) Method. Procedia Computer Science, 179, 177-184.
〔19.〕 Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual review of psychology, 41(1), 417-440.
〔20.〕 McCrae, R. R., & John, O. P. (1992). An introduction to the five‐factor model and its applications. Journal of personality, 60(2), 175-215.
〔21.〕 John, O. P., & Srivastava, S. (1999). The Big-Five trait taxonomy: History, measurement, and theoretical perspectives (Vol. 2): University of California Berkeley.
〔22.〕 Cervone, D., & Pervin, L. A. (2015). Personality: Theory and research: John Wiley & Sons.
〔23.〕 Gosling, S. D., Rentfrow, P. J., & Swann Jr, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in personality, 37(6), 504-528.
〔24.〕 Rammstedt, B., & John, O. P. (2007). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in personality, 41(1), 203-212.
〔25.〕 Gosling, S., Rentfrow, P., & Potter, J. (2014). Norms for the ten item personality inventory. Unpublished data.
〔26.〕 Birren, F. (1973). Color preference as a clue to personality. Art psychotherapy.
〔27.〕 Valdez, P., & Mehrabian, A. (1994). Effects of color on emotions. Journal of experimental psychology: General, 123(4), 394.
〔28.〕 Walters, J., Apter, M. J., & Svebak, S. (1982). Color preference, arousal, and the theory of psychological reversals. Motivation and emotion, 6(3), 193-215.
〔29.〕 Gao, X. P., Xin, J. H., Sato, T., Hansuebsai, A., Scalzo, M., Kajiwara, K., . . . Billger, M. (2007). Analysis of cross‐cultural color emotion. Color Research & Application: Endorsed by Inter‐Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Français de la Couleur, 32(3), 223-229.
〔30.〕 Mehta, R., & Zhu, R. J. (2009). Blue or red? Exploring the effect of color on cognitive task performances. Science, 323(5918), 1226-1229.
〔31.〕 Hartman, T. (2007). The color code: A new way to see yourself, your relationships, and life: Simon and Schuster.
〔32.〕 Jacobs, L., Keown, C., Worthley, R., & Ghymn, K. I. (1991). Cross‐cultural colour comparisons: global marketers beware! International marketing review.
〔33.〕 Madden, T. J., Hewett, K., & Roth, M. S. (2000). Managing images in different cultures: A cross-national study of color meanings and preferences. Journal of international marketing, 8(4), 90-107.
〔34.〕 Bottomley, P. A., & Doyle, J. R. (2006). The interactive effects of colors and products on perceptions of brand logo appropriateness. Marketing Theory, 6(1), 63-83.
〔35.〕 Eastlake, C. L. (2020). Goethe′s Theory of Colours: BoD–Books on Demand.
〔36.〕 Jung, C. G. (2020). Commentary on" The Secret of the Golden Flower": Princeton University Press.
〔37.〕 Tao, B., Xu, S., Pan, X., Gao, Q., & Wang, W. (2015). Personality trait correlates of color preference in schizophrenia. Translational neuroscience, 6(1), 174-178.
〔38.〕 Ghorawat, D., & Madan, R. (2014). Correlation between personality types and color shade preference. Int. J. Indian Psychol, 1(04), 70-79.
〔39.〕 Schaie, K. W. (1963). The Color Pyramid Test: A nonverbal technique for personality assessment. Psychological Bulletin, 60(6), 530.
〔40.〕 French, C. A., & Alexander, A. B. (1972). The Luscher Color Test: An investigation of validity and underlying assumptions. Journal of Personality Assessment, 36(4), 361-365.
〔41.〕 Kertzman, S., Spivak, B., Ben-Nahum, Z., Vainder, M., Weizman, A., & Mester, R. (2003). Variability of color choice in the Lüscher color test—Sex differences. Perceptual and motor skills, 97(2), 647-656.
〔42.〕 Holmes, C. B., Wurtz, P. J., Waln, R. F., Dungan, D. S., & Joseph, C. A. (1984). Relationship between the Luscher color test and the MMPI. Journal of Clinical Psychology, 40(1), 126-128.
〔43.〕 Basra, R., Cortes, E., Khullar, V., & Kelleher, C. (2009). Do colour and personality influence treatment seeking behaviour in women with lower urinary tract symptoms? A prospective study using the short Lüscher colour test. Journal of Obstetrics and Gynaecology, 29(5), 407-411.
〔44.〕 Savio, F., & Zanardo, V. (2015). Unconscious dynamics in twin pregnancy emerging from the lüscher color test. The Journal of Maternal-Fetal & Neonatal Medicine, 28(2), 199-203.
〔45.〕 Zanardo, V., Gabrieli, C., Volpe, F., Savio, F., Straface, G., & Soldera, G. (2017). Postpartum unconscious dynamics emerging from the lüscher color test in ethiopian women. The Journal of Maternal-Fetal & Neonatal Medicine, 30(12), 1446-1449.
〔46.〕 Carruthers, H. R., Morris, J., Tarrier, N., & Whorwell, P. J. (2010). The Manchester Color Wheel: development of a novel way of identifying color choice and its validation in healthy, anxious and depressed individuals. BMC medical research methodology, 10(1), 1-13.
〔47.〕 Carruthers, H. R., Morris, J., Tarrier, N., & Whorwell, P. J. (2010). Mood color choice helps to predict response to hypnotherapy in patients with irritable bowel syndrome. BMC complementary and alternative medicine, 10(1), 1-9.
〔48.〕 Carruthers, H. R., Magee, L., Osborne, S., Hall, L. K., & Whorwell, P. J. (2012). The Manchester Color Wheel: validation in secondary school pupils. BMC medical research methodology, 12(1), 1-12.
〔49.〕 Rosenbloom, T. (2006). Color preferences of high and low sensation seekers. Creativity Research Journal, 18(2), 229-235.
〔50.〕 Nolan, R. F., Dai, Y., & Stanley, P. D. (1995). An investigation of the relationship between color choice and depression measured by the Beck Depression Inventory. Perceptual and motor skills, 81(3_suppl), 1195-1200.
〔51.〕 Stimpson, D. V., & Stimpson, M. F. (1979). Relation of personality characteristics and color preferences. Perceptual and motor skills, 49(1), 60-62.
〔52.〕 Jonauskaite, D., Althaus, B., Dael, N., Dan‐Glauser, E., & Mohr, C. (2019). What color do you feel? Color choices are driven by mood. Color Research & Application, 44(2), 272-284.
〔53.〕 Wexner, L. B. (1954). The degree to which colors (hues) are associated with mood-tones. Journal of applied psychology, 38(6), 432.
〔54.〕 Hsieh, Y.-C., Chiu, H.-C., Tang, Y.-C., & Lee, M. (2018). Do colors change realities in online shopping? Journal of interactive marketing, 41, 14-27.
〔55.〕 Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. (1957). The measurement of meaning: University of Illinois press.
〔56.〕 Hanjalic, A. (2006). Extracting moods from pictures and sounds: Towards truly personalized TV. IEEE Signal Processing Magazine, 23(2), 90-100.
〔57.〕 Machajdik, J., & Hanbury, A. (2010). Affective image classification using features inspired by psychology and art theory. Paper presented at the Proceedings of the 18th ACM international conference on Multimedia.
〔58.〕 Lee, S., Heere, B., & Chung, K.-s. (2013). Which senses matter more? The impact of our senses on team identity and team loyalty. Sport Marketing Quarterly, 22(4).
〔59.〕 Celebi, M. E. (2011). Improving the performance of k-means for color quantization. Image and Vision Computing, 29(4), 260-271.
〔60.〕 Chen, T.-W., Chen, Y.-L., & Chien, S.-Y. (2008). Fast image segmentation based on K-Means clustering with histograms in HSV color space. Paper presented at the 2008 IEEE 10th Workshop on Multimedia Signal Processing.
〔61.〕 Mignotte, M. (2008). Segmentation by fusion of histogram-based $ k $-means clusters in different color spaces. IEEE Transactions on image processing, 17(5), 780-787.
〔62.〕 Mathur, A., & Foody, G. M. (2008). Multiclass and binary SVM classification: Implications for training and classification users. IEEE Geoscience and remote sensing letters, 5(2), 241-245.
〔63.〕 Gunn, S. R. (1998). Support vector machines for classification and regression. ISIS technical report, 14(1), 5-16.
〔64.〕 Huang, C., Davis, L., & Townshend, J. (2002). An assessment of support vector machines for land cover classification. International Journal of remote sensing, 23(4), 725-749.
〔65.〕 Bazi, Y., & Melgani, F. (2006). Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Transactions on geoscience and remote sensing, 44(11), 3374-3385.
〔66.〕 Ahlawat, S., & Choudhary, A. (2020). Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 167, 2554-2560.
〔67.〕 Chen, Y., & Zhang, Z. (2018). Research on text sentiment analysis based on CNNs and SVM. Paper presented at the 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).
〔68.〕 Rojas-Domínguez, A., Padierna, L. C., Valadez, J. M. C., Puga-Soberanes, H. J., & Fraire, H. J. (2017). Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis. Ieee Access, 6, 7164-7176.
〔69.〕 Levitan, S. I., Levitan, Y., An, G., Levine, M., Levitan, R., Rosenberg, A., & Hirschberg, J. (2016). Identifying individual differences in gender, ethnicity, and personality from dialogue for deception detection. Paper presented at the Proceedings of the second workshop on computational approaches to deception detection.
〔70.〕 Ong, V., Rahmanto, A. D., Suhartono, D., Nugroho, A. E., Andangsari, E. W., & Suprayogi, M. N. (2017). Personality prediction based on Twitter information in Bahasa Indonesia. Paper presented at the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS).
〔71.〕 Dietterich, T. G. (2002). Ensemble learning. The handbook of brain theory and neural networks, 2, 110-125.
〔72.〕 Breiman, L. (1999). 1 RANDOM FORESTS--RANDOM FEATURES.
〔73.〕 Quinlan, J. R. (2014). C4. 5: programs for machine learning: Elsevier.
〔74.〕 Mingers, J. (1989). An empirical comparison of pruning methods for decision tree induction. Machine learning, 4(2), 227-243.
〔75.〕 Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote sensing of environment, 86(4), 554-565.
〔76.〕 Krismayer, T., Schedl, M., Knees, P., & Rabiser, R. (2019). Predicting user demographics from music listening information. Multimedia Tools and Applications, 78(3), 2897-2920.
〔77.〕 Humston, E. M., Knowles, J. D., McShea, A., & Synovec, R. E. (2010). Quantitative assessment of moisture damage for cacao bean quality using two-dimensional gas chromatography combined with time-of-flight mass spectrometry and chemometrics. Journal of Chromatography A, 1217(12), 1963-1970.
〔78.〕 Costa, P. T., & McCrae, R. R. (1992). Revised NEO personality inventory (NEO-PI-R) and Neo five-factor inventory (NEO-FFI): Psychological Assessment Resources.
〔79.〕 Segalin, C., Cheng, D. S., & Cristani, M. (2017). Social profiling through image understanding: Personality inference using convolutional neural networks. Computer Vision and Image Understanding, 156, 34-50.
〔80.〕 Beck, K. (1996). Lüschers Farvetest, Psykologisk testning, personlighedsteorie og farvers universalitet. In: Kobenhavens Universitet, Psykologisk Laboratorium, Vejleder Simo Koppe.
〔81.〕 Clarke III, I., & Honeycutt Jr, E. D. (2000). Color usage in international business-to-business print advertising. Industrial Marketing Management, 29(3), 255-261.
〔82.〕 Hurvich, L. M., & Jameson, D. (1957). An opponent-process theory of color vision. Psychological review, 64(6p1), 384.
〔83.〕 Adams, E. Q. (1923). A Theory of Color Vision. Psychological review, 30(1), 56.
〔84.〕 Schwarz, M. W., Cowan, W. B., & Beatty, J. C. (1987). An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models. ACM Transactions on Graphics (TOG), 6(2), 123-158.
〔85.〕 Schanda, J. (2007). Colorimetry: understanding the CIE system: John Wiley & Sons.
〔86.〕 McLaren, K. (1976). XIII—The development of the CIE 1976 (L* a* b*) uniform colour space and colour‐difference formula. Journal of the Society of Dyers and Colourists, 92(9), 338-341.
〔87.〕 Annadurai, S. (2007). Fundamentals of digital image processing: Pearson Education India.
〔88.〕 Mokrzycki, W., & Tatol, M. (2011). Colour difference∆ E-A survey. Mach. Graph. Vis, 20(4), 383-411.
〔89.〕 Finlayson, G. D., Hubel, P. M., & Hordley, S. (1997). Color by correlation. Paper presented at the Color and Imaging Conference.
〔90.〕 Cucurull, G., Rodríguez, P., Yazici, V. O., Gonfaus, J. M., Roca, F. X., & Gonzàlez, J. (2018). Deep inference of personality traits by integrating image and word use in social networks. arXiv preprint arXiv:1802.06757.
〔91.〕 Steele Jr, F., Evans, D., & Green, R. (2009). Is your profile picture worth 1000 words? Photo characteristics associated with personality impression agreement. Paper presented at the Proceedings of the International AAAI Conference on Web and Social Media.
〔92.〕 Guntuku, S. C., Roy, S., & Weisi, L. (2015). Personality modeling based image recommendation. Paper presented at the International Conference on multimedia modeling.
〔93.〕 Provost, F. (2000). Machine learning from imbalanced data sets 101. Paper presented at the Proceedings of the AAAI’2000 workshop on imbalanced data sets.
〔94.〕 Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004). Special issue on learning from imbalanced data sets. ACM SIGKDD explorations newsletter, 6(1), 1-6.
〔95.〕 Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our twitter profiles, our selves: Predicting personality with twitter. Paper presented at the 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing.
〔96.〕 Kim, D., Son, S.-W., & Jeong, H. (2014). Large-scale quantitative analysis of painting arts. Scientific reports, 4(1), 1-7.
〔97.〕 Abry, P., Wendt, H., & Jaffard, S. (2013). When Van Gogh meets Mandelbrot: Multifractal classification of painting′s texture. Signal Processing, 93(3), 554-572.
指導教授 許秉瑜 審核日期 2021-7-13
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