Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 03. März 2020)
1Zhang, L. ; Wang, S. ; Liu, B.: Deep learning for sentiment analysis : a survey.
Abstract: Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results. Along with the success of deep learning in many other application domains, deep learning is also popularly used in sentiment analysis in recent years. This paper first gives an overview of deep learning and then provides a comprehensive survey of its current applications in sentiment analysis.
2Liu, B. ; Yuan, Q. ; Cong, G. ; Xu, D.: Where your photo is taken : geolocation prediction for social images.
In: Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1232-1243.
Abstract: Social image-sharing websites have attracted a large number of users. These systems allow users to associate geolocation information with their images, which is essential for many interesting applications. However, only a small fraction of social images have geolocation information. Thus, an automated tool for suggesting geolocation is essential to help users geotag their images. In this article, we use a large data set consisting of 221 million Flickr images uploaded by 2.2 million users. For the first time, we analyze user uploading patterns, user geotagging behaviors, and the relationship between the taken-time gap and the geographical distance between two images from the same user. Based on the findings, we represent a user profile by historical tags for the user and build a multinomial model on the user profile for geotagging. We further propose a unified framework to suggest geolocations for images, which combines the information from both image tags and the user profile. Experimental results show that for images uploaded by users who have never done geotagging, our method outperforms the state-of-the-art method by 10.6 to 34.2%, depending on the granularity of the prediction. For images from users who have done geotagging, a simple method is able to achieve very high accuracy.
Behandelte Form: Bilder
3Liu, B.: Web data mining : exploring hyperlinks, contents, and usage data.2nd ed.
Heidelberg : Springer, 2011. XX, 622 S.
(Data-centric systems and applications)
Abstract: Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semistructured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web data mining. Key topics of structure mining, content mining, and usage mining are covered both in breadth and in depth. His book brings together all the essential concepts and algorithms from related areas such as data mining, machine learning, and text processing to form an authoritative and coherent text. The book offers a rich blend of theory and practice, addressing seminal research ideas, as well as examining the technology from a practical point of view. It is suitable for students, researchers and practitioners interested in Web mining both as a learning text and a reference book. Lecturers can readily use it for classes on data mining, Web mining, and Web search. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.
Inhalt: Inhalt: 1. Introduction 2. Association Rules and Sequential Patterns 3. Supervised Learning 4. Unsupervised Learning 5. Partially Supervised Learning 6. Information Retrieval and Web Search 7. Social Network Analysis 8. Web Crawling 9. Structured Data Extraction: Wrapper Generation 10. Information Integration
Anmerkung: Elektronische Ausgabe unter: http://springer.r.delivery.net/r/r?2.1.Ee.2Tp.1gd0L5.C3WE8i..N.WdtE.3uq2.bW89MQ%5f%5fCXPUFOH0.
Themenfeld: Data Mining
RSWK: World Wide Web / Data Mining
BK: 54.72 ; 06.74 ; 06.70 ; 54.32
DDC: 006.312 / DDC22ger ; 005.7402854678 / DDC22ger ; 005.72 / DDC22ger
GHBS: TZG (FH K) ; TWX (FH GE)
RVK: ST 530