Fuzzy Content Mining for Targeted Advertisement

Content-targeted advertising system is becoming an increasingly important part of the funding source of free web services. Highly efficient content analysis is the pivotal key of such a system. This project aims to establish a content analysis engine involving fuzzy logic that is able to automatically analyze real user-posted Web documents such as blog entries. Based on the analysis result, the system matches and retrieves the most appropriate Web advertisements. The focus and complexity is on how to better estimate and acquire the keywords that represent a given Web document. Fuzzy Web mining concept will be applied to synthetically consider multiple factors of Web content. A Fuzzy Ranking System is established based on certain fuzzy (and some crisp) rules, fuzzy sets, and membership functions to get the best candidate keywords. Once it is has obtained the keywords, the system will retrieve corresponding advertisements from certain providers through Web services as matched advertisements, similarly to retrieving a products list from Amazon.com. In 87% of the cases, the results of this system can match the accuracy of the Google Adwords system. Furthermore, this expandable system will also be a solid base for further research and development on this topic

Fashion Conversation Data on Instagram

The fashion industry is establishing its presence on a number of visual-centric social media like Instagram. This creates an interesting clash as fashion brands that have traditionally practiced highly creative and editorialized image marketing now have to engage with people on the platform that epitomizes impromptu, realtime conversation. What kinds of fashion images do brands and individuals share and what are the types of visual features that attract likes and comments? In this research, we take both quantitative and qualitative approaches to answer these questions. We analyze visual features of fashion posts first via manual tagging and then via training on convolutional neural networks. The classified images were examined across four types of fashion brands: mega couture, small couture, designers, and high street. We find that while product-only images make up the majority of fashion conversation in terms of volume, body snaps and face images that portray fashion items more naturally tend to receive a larger number of likes and comments by the audience. Our findings bring insights into building an automated tool for classifying or generating influential fashion information. We make our novel dataset of {24,752} labeled images on fashion conversations, containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1

Intuition for the intuitive : On the interplay between lay conceptions of intuition and the influence of intuition appeals in persuasion

Tese apresentada para cumprimento dos requisitos necessários à obtenção do grau de Doutor em Psicologia na área de especialização de Psicologia Social apresentada no ISPA - Instituto Universitário no ano de 2021

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Ontology-based employer demand management

Skills shortages globally pose a real and urgent need for proper investigation and workforce development planning into the future. Analysing workforce development and employer demand needs through electronic job market allows much deeper and wider research into skill shortages. Current methods do not provide the level of depth required to address such important economic implications. In this paper, we present a system aiming to gather and analyse current employer demand information from online job advertisements. It identifies current employer demand needs analysed from electronic job market

Proxy documents as a source of measurement error in the Comparative Manifestos Project

This paper considers the issue of document type diversity in the Comparative Manifestos Project (CMP). For many years the CMP has been collecting and coding a variety of documents, such as speeches, pamphlets, newspaper articles and leaflets, as manifesto proxies. By using previously unexplored archival material to perform controlled comparisons between different types of documents, this paper argues that the coding of such documents introduced considerable measurement error to party position estimates. Statistical analyses indicate that this measurement error is systematic rather than random as it is often manifested as centrist bias in parties' left–right position estimates. Consequently, the paper argues that random error correction methods cannot always correct for error attributed to the coding of proxy documents. The paper concludes with some recommendations for third-party users of the CMP data and documents and a plea to the CMP research tea

Collaborative e-science architecture for Reaction Kinetics research community

This paper presents a novel collaborative e-science architecture (CeSA) to address two challenging issues in e-science that arise from the management of heterogeneous distributed environments: (i) how to provide individual scientists an integrated environment to collaborate with each other in distributed, loosely coupled research communities where each member might be using a disparate range of tools; and (ii) how to provide easy access to a range of computationally intensive resources from a desktop. The Reaction Kinetics research community was used to capture the requirements and in the evaluation of the proposed architecture. The result demonstrated the feasibility of the approach and the potential benefits of the CeSA

Transforming Unstructured Text into Data with Context Rule Assisted Machine Learning (CRAML)

We describe a method and new no-code software tools enabling domain experts to build custom structured, labeled datasets from the unstructured text of documents and build niche machine learning text classification models traceable to expert-written rules. The Context Rule Assisted Machine Learning (CRAML) method allows accurate and reproducible labeling of massive volumes of unstructured text. CRAML enables domain experts to access uncommon constructs buried within a document corpus, and avoids limitations of current computational approaches that often lack context, transparency, and interpetability. In this research methods paper, we present three use cases for CRAML: we analyze recent management literature that draws from text data, describe and release new machine learning models from an analysis of proprietary job advertisement text, and present findings of social and economic interest from a public corpus of franchise documents. CRAML produces document-level coded tabular datasets that can be used for quantitative academic research, and allows qualitative researchers to scale niche classification schemes over massive text data. CRAML is a low-resource, flexible, and scalable methodology for building training data for supervised ML. We make available as open-source resources: the software, job advertisement text classifiers, a novel corpus of franchise documents, and a fully replicable start-to-finish trained example in the context of no poach clauses