Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Sentiment Analysis in Social Streams

In this chapter, we review and discuss the state of the art on sentiment analysis in social streams—such as web forums, microblogging systems, and social networks, aiming to clarify how user opinions, affective states, and intended emo tional effects are extracted from user generated content, how they are modeled, and howthey could be finally exploited.We explainwhy sentiment analysistasks aremore difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the mainstream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Sentiment Analysis in Social Streams

In this chapter we review and discuss the state of the art on sentiment analysis in social streams –such as web forums, micro-blogging systems, and so- cial networks–, aiming to clarify how user opinions, affective states, and intended emotional effects are extracted from user generated content, how they are modeled, and how they could be finally exploited. We explain why sentiment analysis tasks are more difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the main- stream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Assessing public figures’ reputation through sentiment analysis on twitter using machine learning : creation of a system

Mestrado em Gestão de Sistemas de InformaçãoNunca se geraram tantos dados e a um ritmo tão alucinante como atualmente. Vive-se indubitavelmente numa era de Big Data e este termo não passa despercebido, trazendo consigo inúmeros desafios, mas também múltiplas oportunidades. Cerca de 80% dos dados encontra-se de forma desestruturada. Aqui, há um foco especial para o formato de texto, formato esse que para além de comum, agrega um grande potencial. Existem várias aplicações, técnicas e ferramentas associadas à análise de documentos textuais, e esta área surge fortemente ligada ao Processamento de Linguagem Natural. Um dos grandes desafios de ambos está relacionado com Análise de Sentimentos. Sendo interessante aliar tendências e abordar questões como a reputação online, o presente projeto focou-se na criação de um sistema capaz de identificar o sentimento associado a figuras públicas demonstrado através de publicações no Twitter. Com essa finalidade, o levou-se a cabo uma revisão de literatura capaz de explicitar os tópicos associados à temática escolhida. Relativamente ao sistema, optou-se por uma abordagem de Machine Learning com recurso a métodos supervisionados de aprendizagem. Para tal, criou-se um dataset manualmente anotado e procedeu-se ao treino de três classificadores (Naïve Bayes, Support Vector Machines e Entropia Máxima). O impacto de algumas técnicas de pré-processamento também foi medido. Os resultados obtidos não foram tão bons como desejado, mas o melhor modelo foi incorporado no sistema. Este projeto contribuiu para aumentar a base de conhecimento das áreas em que se insere, e fornece ainda um dataset manualmente anotado que poderá ser utilizado em investigações futuras.Never has so much data been generated and at such an astounding rate as nowadays. This is undoubtedly an era of Big Data and this term does not go unnoticed, bearing within innumerous challenges, but also a multitude of opportunities. Of the generated data, roughly 80% comes unstructured, and there is a special focus on the text format, which appears frequently and carries great potential. There are several applications, techniques and tools connected to the analysis of textual documents and this area is strongly linked to Natural Language Processing. One of the greatest challenges of both is related to Sentiment Analysis. Since it would be interesting to combine trends and address issues such as online reputation, this project focused on creating a system capable of identifying the sentiment associated with public figures, demonstrated through Twitter publications. Firstly, a literature review capable of exploring the topics associated with the chosen subject was carried out. Afterwards,and regarding the system, a Machine Learning approach using supervised learning methods was adopted. For this, a manually annotated dataset was created and three of the most used classifiers (Naïve Bayes, Support Vector Machines and Maximum Entropy) were trained. The impact of some pre-processing techniques was also assessed. The obtained results were not as good as initially desired, nonetheless the best model was chosen to incorporate the system. This project contributed to increase the knowledge base of the areas in which it is comprised and provides a manually annotated dataset that can be used in further research.info:eu-repo/semantics/publishedVersio

Environmental Scanning for Customer Complaint Identification in Social Media

Social media provides a platform for dissatisfied and frustrated customers to discuss matters of common concerns and share experiences about products and services. While listening to and learning from customer has long been recognized as an important marketing charge, how to identify customer complaints on social media is a nontrivial task. Customer complaint messages are highly distributed on social media, while non-complaint messages are unspecific and topically diverse. It is costly and time consuming to manually label a large number of customer complaint messages (positive examples) and non-complaint messages (negative examples) for training classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content on social media. In this paper, we propose a partially supervised learning approach to automatically extract high quality positive and negative examples from an unlabeled dataset. The empirical evaluation suggested that the proposed approach generally outperforms the benchmark techniques and exhibits more stable performance

An Analysis of Using Expert Systems and Intelligent Agents for the Virtual Library Project at the Naval Surface Warfare Center-Carderock Division

The Virtual Library Project1 at the Naval Surface Warfare Center/Carderock Division (NSWC/CD) is being developed to facilitate the incorporation and use of library documents via the Internet. These documents typically relate to the design and manufacture of ships for the U.S. Navy Fleet. As such, the libraries will store documents that contain not only text but also images, graphs and design configurations. Because of the dynamic nature of digital documents, particularly those related to design, rapid and effective cataloging of these documents becomes challenging. We conducted a research study to analyze the use of expert systems and intelligent agents to support the function of cataloging digital documents. This chapter provides an overview of past research in the use of expert systems and intelligent agents for cataloging digital documents and discusses our recommendations based on NSWC/CD’s requirements