Search (15 results, page 1 of 1)

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Abstract

This research explores the use of genre as a document descriptor in order to improve the effectiveness of Web searching. A major issue to be resolved is the identification of what document categories should be used as genres. As genre is a kind of folk typology, document categories must enjoy widespread recognition by their intended user groups in order to qualify as genres. Three user studies were conducted to develop a genre palette and show that it is recognizable to users. (Palette is a term used to denote a classification, attributable to Karlgren, Bretan, Dewe, Hallberg, and Wolkert, 1998.) To simplify the users' classification task, it was decided to focus on Web pages from the edu domain. The first study was a survey of user terminology for Web pages. Three participants separated 100 Web page printouts into stacks according to genre, assigning names and definitions to each genre. The second study aimed to refine the resulting set of 48 (often conceptually and lexically similar) genre names and definitions into a smaller palette of user-preferred terminology. Ten participants classified the same 100 Web pages. A set of five principles for creating a genre palette from individuals' sortings was developed, and the list of 48 was trimmed to 18 genres. The third study aimed to show that users would agree on the genres of Web pages when choosing from the genre palette. In an online experiment in which 257 participants categorized a new set of 55 pages using the 18 genres, on average, over 70% agreed on the genre of each page. Suggestions for improving the genre palette and future directions for the work are discussed.

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Abstract

Sentiment analysis is concerned with the automatic extraction of sentiment-related information from text. Although most sentiment analysis addresses commercial tasks, such as extracting opinions from product reviews, there is increasing interest in the affective dimension of the social web, and Twitter in particular. Most sentiment analysis algorithms are not ideally suited to this task because they exploit indirect indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms used to process social web texts can identify spurious sentiment patterns caused by topics rather than affective phenomena. This article assesses an improved version of the algorithm SentiStrength for sentiment strength detection across the social web that primarily uses direct indications of sentiment. The results from six diverse social web data sets (MySpace, Twitter, YouTube, Digg, Runners World, BBC Forums) indicate that SentiStrength 2 is successful in the sense of performing better than a baseline approach for all data sets in both supervised and unsupervised cases. SentiStrength is not always better than machine-learning approaches that exploit indirect indicators of sentiment, however, and is particularly weaker for positive sentiment in news-related discussions. Overall, the results suggest that, even unsupervised, SentiStrength is robust enough to be applied to a wide variety of different social web contexts.

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Date

5. 8.2006 13:22:44

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Date

5. 8.2006 13:22:08

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Date

22. 5.2021 12:43:05

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Abstract

In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik's circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections. The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.

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Date

22. 1.2012 13:02:10

Source

Knowledge organization. 39(2012) no.1, S.13-22

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Source

Cataloging and classification quarterly. 22(1996) no.2, S.5-18

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik

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Source

Library trends. 55(2006) no.1, S.22-45

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Abstract

The paper establishes a taxonomy of image-text relationships that reflects the ways that images and text interact. It is applicable to all subject areas and document types. The taxonomy was developed to answer the research question: how does an illustration relate to the text with which it is associated, or, what are the functions of illustration? Developed in a two-stage process - first, analysis of relevant research in children's literature, dictionary development, education, journalism, and library and information design and, second, subsequent application of the first version of the taxonomy to 954 image-text pairs in 45 Web pages (pages with educational content for children, online newspapers, and retail business pages) - the taxonomy identifies 49 relationships and groups them in three categories according to the closeness of the conceptual relationship between image and text. The paper uses qualitative content analysis to illustrate use of the taxonomy to analyze four image-text pairs in government publications and discusses the implications of the research for information retrieval and document design.

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Abstract

Some collections cover many topics, while others are narrowly focused an a limited number of topics. We introduce the concept of the "scope" of a collection of documents and we compare two ways of measuring lt. These measures are based an the distances between documents. The first uses the overlap of words between pairs of documents. The second measure uses a novel method that calculates the semantic relatedness to pairs of words from the documents. Those values are combined to obtain an overall distance between the documents. The main validation for the measures compared Web pages categorized by Yahoo. Sets of pages sampied from broad categories were determined to have a higher scope than sets derived from subcategories. The measure was significant and confirmed the expected difference in scope. Finally, we discuss other measures related to scope.

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Abstract

In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.

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Date

27. 9.2005 14:22:19

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Date

22. 9.1997 19:16:05