Abstract
A consumer-dependent (business-to-consumer) organization tends to present itself as possessing a set of human qualities, which is termed the brand personality of the company. The perception is impressed upon the consumer through the content, be it in the form of advertisement, blogs, or magazines, produced by the organization. A consistent brand will generate trust and retain customers over time as they develop an affinity toward regularity and common patterns. However, maintaining a consistent messaging tone for a brand has become more challenging with the virtual explosion in the amount of content that needs to be authored and pushed to the Internet to maintain an edge in the era of digital marketing. To understand the depth of the problem, we collect around 300K web page content from around 650 companies. We develop trait-specific classification models by considering the linguistic features of the content. The classifier automatically identifies the web articles that are not consistent with the mission and vision of a company and further helps us to discover the conditions under which the consistency cannot be maintained. To address the brand inconsistency issue, we then develop a sentence ranking system that outputs the top three sentences that need to be changed for making a web article more consistent with the company’s brand personality.
- David Aaker. 1990. Brand extensions: The good, the bad, and the ugly. MIT Sloan Manage. Rev. 31, 4 (Summer 1990), 47--56.Google Scholar
- Jennifer L. Aaker. 1997. Dimensions of brand personality. J. Market. Res. 34, 3 (08 1997), 347--356.Google Scholar
Cross Ref
- Pankaj Aggarwal. 2004. The effects of brand relationship norms on consumer attitudes and behavior. J. Cons. Res. 31, 1 (2004), 87--101. https://doi.org/10.1086/383426Google Scholar
Cross Ref
- Enrique Amigó, Jorge Carrillo De Albornoz, Irina Chugur, Adolfo Corujo, Julio Gonzalo, Tamara Martín, Edgar Meij, Maarten De Rijke, and Damiano Spina. 2013. Overview of replab 2013: Evaluating online reputation monitoring systems. In Proceedings of the International Conference of the Cross-language Evaluation forum for European Languages. 333--352. https://doi.org/10.1007/978-3-642-40802-1_31 Google Scholar
Digital Library
- Erik Cambria, Soujanya Poria, Devamanyu Hazarika, and Kenneth Kwok. 2018. SenticNet 5: Discovering conceptual primitives for sentiment analysis by means of context embeddings. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. https://ojs.aaai.org/index.php/AAAI/article/view/11559.Google Scholar
- Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. SMOTE: Synthetic minority over-sampling technique. J. Artif. Int. Res. 16, 1 (Jun. 2002), 321--357. http://dl.acm.org/citation.cfm?id=1622407.1622416 Google Scholar
Digital Library
- Qimei Chen and Shelly Rodgers. 2006. Development of an instrument to measure web site personality. J. Interact. Advert. 7, 1 (2006), 4--46. DOI:https://doi.org/10.1080/15252019.2006.10722124Google Scholar
Cross Ref
- Judy Delin. 2007. Brand tone of voice. J. Appl. Ling. 2, 1 (2007).Google Scholar
- Baris Depecik, Yvonne M. van Everdingen, and Gerrit H. van Bruggen. 2014. Firm value effects of global, regional, and local brand divestments in core and non-core businesses. Global Strateg. J. 4, 2 (2014), 143--160. DOI:https://doi.org/10.1111/j.2042-5805.2014.1074.xGoogle Scholar
Cross Ref
- John M. Digman. 1990. Personality structure: Emergence of the five-factor model. Annu. Rev. Psychol. 41, 1 (1990), 417--440.Google Scholar
Cross Ref
- Alecia C. Douglas, Juline E. Mills, and Raphael Kavanaugh. 2007. Exploring the use of emotional features at romantic destination websites. In Information and Communication Technologies in Tourism 2007, Marianna Sigala, Luisa Mich, and Jamie Murphy (Eds.). Springer Vienna, Vienna, 331--340. https://doi.org/10.1007/978-3-211-69566-1_31Google Scholar
- Mauro Dragoni, Soujanya Poria, and Erik Cambria. 2018. OntoSenticNet: A commonsense ontology for sentiment analysis. IEEE Intell. Syst. 33, 3 (2018), 77--85. DOI:https://doi.org/10.1109/MIS.2018.033001419Google Scholar
Cross Ref
- Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL’05). Association for Computational Linguistics, 363--370. DOI:https://doi.org/10.3115/1219840.1219885 Google Scholar
Digital Library
- Clayton J. Hutto and Eric Gilbert. 2014. VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the 8th International Conference on Weblogs and Social Media (ICWSM’14). https://ojs.aaai.org/index.php/ICWSM/article/view/14550.Google Scholar
- J. Jagadeesh, Prasad Pingali, and Vasudeva Varma. 2005. Sentence extraction based single document summarization. International Institute of Information Technology, Hyderabad, India 5 (2005).Google Scholar
- Kevin Lane Keller. 1999. Managing brands for the long run: Brand reinforcement and revitalization strategies. Calif. Manage. Rev. 41, 3 (1999), 102--124. DOI:https://doi.org/10.2307/41165999Google Scholar
Cross Ref
- Kevin Lane Keller. 2009. Building strong brands in a modern marketing communications environment. J. Market. Commun. 15, 2-3 (2009), 139--155. DOI:https://doi.org/10.1080/13527260902757530Google Scholar
Cross Ref
- J. Peter Kincaid, Robert P. Fishburne Jr., Richard L. Rogers, and Brad S. Chissom. 1975. Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. Research Branch Report 8--75. Chief of Naval Technical Training: Naval Air Station Memphis.Google Scholar
- Shibamouli Lahiri. 2015. SQUINKY! A corpus of sentence-level formality, informativeness, and implicature. arXiv:1506.02306. Retrieved from https://arxiv.org/abs/1506.02306.Google Scholar
- Chin-Yew Lin and Eduard Hovy. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. 150--157. https://www.aclweb.org/anthology/N03-1020. Google Scholar
Digital Library
- X. Liu, A. Xu, L. Gou, H. Liu, R. Akkiraju, and H. Shen. 2016. SocialBrands: Visual analysis of public perceptions of brands on social media. In Proceedings of the 2016 IEEE Conference on Visual Analytics Science and Technology (VAST’16). 71--80. DOI:https://doi.org/10.1109/VAST.2016.7883513Google Scholar
Cross Ref
- Zhe Liu, Anbang Xu, Yi Wang, Jerald Schoudt, Jalal Mahmud, and Rama Akkiraju. 2017. Does personality matter?: A study of personality and situational effects on consumer behavior. In Proceedings of the 28th ACM Conference on Hypertext and Social Media (HT’17). 185--193. DOI:https://doi.org/10.1145/3078714.3078733 Google Scholar
Digital Library
- François Mairesse, Marilyn A. Walker, Matthias R. Mehl, and Roger K. Moore. 2007. Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Int. Res. 30, 1 (Nov. 2007), 457--500. http://dl.acm.org/citation.cfm?id=1622637.1622649 Google Scholar
Digital Library
- N. Majumder, S. Poria, A. Gelbukh, and E. Cambria. 2017. Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32, 2 (2017), 74--79. DOI:https://doi.org/10.1109/MIS.2017.23 Google Scholar
Digital Library
- Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. 2019. Encode, tag, realize: High-precision text editing. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP’19). Association for Computational Linguistics, 5053--5064. DOI:https://doi.org/10.18653/v1/D19-1510Google Scholar
Cross Ref
- Masoud Mazloom, Robert Rietveld, Stevan Rudinac, Marcel Worring, and Willemijn van Dolen. 2016. Multimodal popularity prediction of brand-related social media posts. In Proceedings of the 24th ACM International Conference on Multimedia (MM’16). 197--201. DOI:https://doi.org/10.1145/2964284.2967210 Google Scholar
Digital Library
- Rada Mihalcea and Paul Tarau. 2004. TextRank: Bringing order into text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 404--411.Google Scholar
- Brigitte Müller and Jean-Louis Chandon. 2003. The impact of visiting a brand website on brand personality. Electr. Markets 13, 3 (2003), 210--221. DOI:https://doi.org/10.1080/1019678032000108301Google Scholar
Cross Ref
- Vitobha Munigala, Abhijit Mishra, Srikanth G. Tamilselvam, Shreya Khare, Riddhiman Dasgupta, and Anush Sankaran. 2018. PersuAIDE! An adaptive persuasive text generation system for fashion domain. In Companion Proceedings of the The Web Conference 2018 (WWW’18). 335--342. DOI:https://doi.org/10.1145/3184558.3186345 Google Scholar
Digital Library
- Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017. SummaRuNNer: A recurrent neural network based sequence model for extractive summarization of documents. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. 3075--3081. https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14636/14080 Google Scholar
Digital Library
- Makbule Gulcin Ozsoy, Ferda Nur Alpaslan, and Ilyas Cicekli. 2011. Text summarization using latent semantic analysis. J. Inf. Sci. 37, 4 (2011), 405--417. DOI:https://doi.org/10.1177/0165551511408848 Google Scholar
Digital Library
- Ellie Pavlick and Joel Tetreault. 2016. An empirical analysis of formality in online communication. Trans. Assoc. Comput. Ling. 4 (2016), 61--74. DOI:https://doi.org/10.1162/tacl_a_00083Google Scholar
Cross Ref
- Soumyadeep Roy, Niloy Ganguly, Shamik Sural, Niyati Chhaya, and Anandhavelu Natarajan. 2019. Understanding brand consistency from web content. In Proceedings of the 10th ACM Conference on Web Science (WebSci’19). 245--253. DOI:https://doi.org/10.1145/3292522.3326048 Google Scholar
Digital Library
- Bernd Schmitt. 2012. The consumer psychology of brands. J. Consum. Psychol. 22, 1 (2012), 7--17. DOI:https://doi.org/10.1016/j.jcps.2011.09.005Google Scholar
Cross Ref
- Jeremiah D. Still. 2018. Web page visual hierarchy: Examining Faraday’s guidelines for entry points. Comput. Hum. Behav. 84 (2018), 352--359. DOI:https://doi.org/10.1016/j.chb.2018.03.014Google Scholar
Cross Ref
- Carlo Strapparava, Alessandro Valitutti, et al. 2004. Wordnet affect: An affective extension of wordnet. In Proceedings of the 4th International Conference on Language Resources and Evaluation, Vol. 4. 40. http://www.lrecconf.org/proceedings/lrec2004/pdf/369.pdf.Google Scholar
- X. Sun, B. Liu, J. Cao, J. Luo, and X. Shen. 2018. Who Am I? Personality detection based on deep learning for texts. In Proceedings of the 2018 IEEE International Conference on Communications (ICC’18). 1--6. DOI:https://doi.org/10.1109/ICC.2018.8422105Google Scholar
- Yla R. Tausczik and James W. Pennebaker. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29, 1 (2010), 24--54. https://doi.org/10.1177/0261927X09351676Google Scholar
Cross Ref
- Ziming Wu, Taewook Kim, Quan Li, and Xiaojuan Ma. 2019. Understanding and modeling user-perceived brand personality from mobile application UIs. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI’19). 213. DOI:https://doi.org/10.1145/3290605.3300443 Google Scholar
Digital Library
- Anbang Xu and Brian Bailey. 2012. What do you think?: A case study of benefit, expectation, and interaction in a large online critique community. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work. 295--304. DOI:https://doi.org/10.1145/2145204.2145252 Google Scholar
Digital Library
- Anbang Xu, Haibin Liu, Liang Gou, Rama Akkiraju, Jalal Mahmud, Vibha Sinha, Yuheng Hu, and Mu Qiao. 2016. Predicting perceived brand personality with social media. In Proceedings of the 10th International Conference on Web and Social Media. 436--445. https://ojs.aaai.org/index.php/ICWSM/article/view/14733.Google Scholar
Index Terms
An Integrated Approach for Improving Brand Consistency of Web Content: Modeling, Analysis, and Recommendation
Recommendations
Understanding Brand Consistency from Web Content
WebSci '19: Proceedings of the 10th ACM Conference on Web ScienceBrands produce content to engage with the audience continually and tend to maintain a set of human characteristics in their marketing campaigns. In this era of digital marketing, they need to create a lot of content to keep up the engagement with their ...
Revisiting Brand Personality Attributes: Mediating Role of Brand Attitude
This study aims to examine the influence of different brand personalities on buyers' purchase intention and examines the role of buyers' brand attitude. Data were collected through questionnaire survey. Analysis of 317 valid responses was carried out ...
An application of brand personality to advergames
The purpose of this study is to explore the advergame personality (AP) dimensions and to explicate the underlying relationships of the AP dimensions with company attributes, product categories, and consumers' behavioral intentions. A series of surveys ...






Comments