IDENTIFYING A CUSTOMER CENTERED APPROACH FOR URBAN PLANNING: DEFINING A FRAMEWORK AND EVALUATING POTENTIAL IN A LIVABILITY CONTEXT

In transportation planning, public engagement is an essential requirement forinformed decision-making. This is especially true for assessing abstract concepts such aslivability, where it is challenging to define objective measures and to obtain input that canbe used to gauge performance of communities. This dissertation focuses on advancing adata-driven decision-making approach for the transportation planning domain in thecontext of livability. First, a conceptual model for a customer-centric framework fortransportation planning is designed integrating insight from multiple disciplines (chapter1), then a data-mining approach to extracting features important for defining customersatisfaction in a livability context is described (chapter 2), and finally an appraisal of thepotential of social media review mining for enhancing understanding of livability measuresand increasing engagement in the planning process is undertaken (chapter 3). The resultsof this work also include a sentiment analysis and visualization package for interpreting anautomated user-defined translation of qualitative measures of livability. The packageevaluates users satisfaction of neighborhoods through social media and enhances thetraditional approaches to defining livability planning measures. This approach has thepotential to capitalize on residents interests in social media outlets and to increase publicengagement in the planning process by encouraging users to participate in onlineneighborhood satisfaction reporting. The results inform future work for deploying acomprehensive approach to planning that draws the marketing structure of transportationnetwork products with residential nodes as the center of the structure

Big data and Sentiment Analysis considering reviews from e-commerce platforms to predict consumer behavior

Treballs Finals del Màster de Recerca en Empresa, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2019-2020, Tutor: Javier Manuel Romaní Fernández ; Jaime Gil LafuenteNowadays and since the last two decades, digital data is generated on a massive scale, this phenomenon is known as Big Data (BD). This phenomenon supposes a change in the way of managing and drawing conclusions from data. Moreover, techniques and methods used in artificial intelligence shape new ways of analysis considering BD. Sentiment Analysis (SA) or Opinion Mining (OM) is a topic widely studied for the last few years due to its potential in extracting value from data. However, it is a topic that has been more explored in the fields of engineering or linguistics and not so much in business and marketing fields. For this reason, the aim of this study is to provide a reachable guide that includes the main BD concepts and technologies to those who do not come from a technical field such as Marketing directors. This essay is articulated in two parts. Firstly, it is described the BD ecosystem and the technologies involved. Secondly, it is conducted a systematic literature review in which articles related with the field of SA are analysed. The contribution of this study is a summarization and a brief description of the main technologies behind BD, as well as the techniques and procedures currently involved in SA

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. Belgium: TIRO http://www.belspo.be/belspo/fedra/proj.asp?l=en&COD=TA/00/08Risks and Safety on the Internet: The Perspective of European Children: Full Findings and Policy Implications From the EU Kids Online Survey of 9–16 Year Olds and Their Parents in 25 Countries http://eprints.lse.ac.uk/33731/Vanderhoven, E., Schellens, T., & Valcke, M. (2014). Educating teens about the risks on social network sites. An intervention study in Secondary Education. Comunicar, 22(43), 123-132. doi:10.3916/c43-2014-12Christofides, E., Muise, A., & Desmarais, S. (2012). Risky Disclosures on Facebook. Journal of Adolescent Research, 27(6), 714-731. doi:10.1177/0743558411432635George, J. M., & Dane, E. (2016). Affect, emotion, and decision making. Organizational Behavior and Human Decision Processes, 136, 47-55. doi:10.1016/j.obhdp.2016.06.004Thelwall, M. (2017). TensiStrength: Stress and relaxation magnitude detection for social media texts. Information Processing & Management, 53(1), 106-121. doi:10.1016/j.ipm.2016.06.009Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020). Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals. Journal of Network and Computer Applications, 149, 102447. doi:10.1016/j.jnca.2019.102447Zhang, C., Zeng, D., Li, J., Wang, F.-Y., & Zuo, W. (2009). Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology, 60(12), 2474-2487. doi:10.1002/asi.21206Lu, B., Ott, M., Cardie, C., & Tsou, B. K. (2011). Multi-aspect Sentiment Analysis with Topic Models. 2011 IEEE 11th International Conference on Data Mining Workshops. doi:10.1109/icdmw.2011.125Nasukawa, T., & Yi, J. (2003). Sentiment analysis. Proceedings of the international conference on Knowledge capture - K-CAP ’03. doi:10.1145/945645.945658Borth, D., Ji, R., Chen, T., Breuel, T., & Chang, S.-F. (2013). Large-scale visual sentiment ontology and detectors using adjective noun pairs. Proceedings of the 21st ACM international conference on Multimedia - MM ’13. doi:10.1145/2502081.2502282Deb, S., & Dandapat, S. (2019). Emotion Classification Using Segmentation of Vowel-Like and Non-Vowel-Like Regions. IEEE Transactions on Affective Computing, 10(3), 360-373. doi:10.1109/taffc.2017.2730187Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013). Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. doi:10.1109/acii.2013.90Nicolaou, M. A., Gunes, H., & Pantic, M. (2011). Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space. IEEE Transactions on Affective Computing, 2(2), 92-105. doi:10.1109/t-affc.2011.9Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. (2016). Audio-Visual Emotion Recognition Using Big Data Towards 5G. Mobile Networks and Applications, 21(5), 753-763. doi:10.1007/s11036-016-0685-9Zhou, F., Jianxin Jiao, R., & Linsey, J. S. (2015). Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews. Journal of Mechanical Design, 137(7). doi:10.1115/1.4030159Ceci, F., Goncalves, A. L., & Weber, R. (2016). A model for sentiment analysis based on ontology and cases. IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. 2015 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2015.7351718Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Matsiola, M., Dimoulas, C., Kalliris, G., & Veglis, A. A. (2018). Augmenting User Interaction Experience Through Embedded Multimodal Media Agents in Social Networks. Information Retrieval and Management, 1972-1993. doi:10.4018/978-1-5225-5191-1.ch088Rosaci, D. (2007). CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents. Information Systems, 32(6), 793-825. doi:10.1016/j.is.2006.06.003Buccafurri, F., Comi, A., Lax, G., & Rosaci, D. (2016). Experimenting with Certified Reputation in a Competitive Multi-Agent Scenario. IEEE Intelligent Systems, 31(1), 48-55. doi:10.1109/mis.2015.98Rosaci, D., & Sarnè, G. M. L. (2014). Multi-agent technology and ontologies to support personalization in B2C E-Commerce. Electronic Commerce Research and Applications, 13(1), 13-23. doi:10.1016/j.elerap.2013.07.003Singh, A., & Sharma, A. (2017). MAICBR: A Multi-agent Intelligent Content-Based Recommendation System. Lecture Notes in Networks and Systems, 399-411. doi:10.1007/978-981-10-3920-1_41Villavicencio, C., Schiaffino, S., Diaz-Pace, J. A., Monteserin, A., Demazeau, Y., & Adam, C. (2016). A MAS Approach for Group Recommendation Based on Negotiation Techniques. Lecture Notes in Computer Science, 219-231. doi:10.1007/978-3-319-39324-7_19Rincon, J. A., de la Prieta, F., Zanardini, D., Julian, V., & Carrascosa, C. (2017). Influencing over people with a social emotional model. Neurocomputing, 231, 47-54. doi:10.1016/j.neucom.2016.03.107Aguado, G., Julian, V., Garcia-Fornes, A., & Espinosa, A. (2020). 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The European Physical Journal B, 77(4), 533-545. doi:10.1140/epjb/e2010-00292-

Information mining of customers preferences for product specifications determination using big sales data

Product competitiveness is highly influenced by its related design specifications. Information retrieval of customers preferences for the specification determination is essential to product design and development. Big sales data is an emerging resource for mining customers preferences on product specifications. In this work, information entropy is used for customers preferences information quantification on product specifications firstly. Then, a method of information mining for customers preferences estimation is developed by using big sales data. On this basis, a density-based clustering analysis is carried out on customers preferences as a decision support tool for the determination and selection of product design specifications. A case study related to electric bicycle specifications determination using big sales data is reported to illustrate and validate the proposed metho

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research

A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexiconsThis work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P)