Search (35 results, page 1 of 2)

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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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Abstract

Content analysis is a highly flexible research method that has been widely used in library and information science (LIS) studies with varying research goals and objectives. The research method is applied in qualitative, quantitative, and sometimes mixed modes of research frameworks and employs a wide range of analytical techniques to generate findings and put them into context. This article characterizes content analysis as a systematic, rigorous approach to analyzing documents obtained or generated in the course of research. It briefly describes the steps involved in content analysis, differentiates between quantitative and qualitative content analysis, and shows that content analysis serves the purposes of both quantitative research and qualitative research. The authors draw on selected LIS studies that have used content analysis to illustrate the concepts addressed in the article. The article also serves as a gateway to methodological books and articles that provide more detail about aspects of content analysis discussed only briefly in the article.

Source

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

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Abstract

This paper, presented at the Ottawa Conference an the Conceptual Basis of the Classification of Knowledge, in 1971, is one of Fairthorne's more perceptive works and deserves a wide audience, especially as it breaks new ground in classification theory. In discussing the notion of discourse, he makes a "distinction between what discourse mentions and what discourse is about" [emphasis added], considered as a "fundamental factor to the relativistic nature of bibliographic classification" (p. 360). A table of mathematical functions, for example, describes exactly something represented by a collection of digits, but, without a preface, this table does not fit into a broader context. Some indication of the author's intent ls needed to fit the table into a broader context. This intent may appear in a title, chapter heading, class number or some other aid. Discourse an and discourse about something "cannot be determined solely from what it mentions" (p. 361). Some kind of background is needed. Fairthorne further develops the theme that knowledge about a subject comes from previous knowledge, thus adding a temporal factor to classification. "Some extra textual criteria are needed" in order to classify (p. 362). For example, "documents that mention the same things, but are an different topics, will have different ancestors, in the sense of preceding documents to which they are linked by various bibliographic characteristics ... [and] ... they will have different descendants" (p. 363). The classifier has to distinguish between documents that "mention exactly the same thing" but are not about the same thing. The classifier does this by classifying "sets of documents that form their histories, their bibliographic world lines" (p. 363). The practice of citation is one method of performing the linking and presents a "fan" of documents connected by a chain of citations to past work. The fan is seen as the effect of generations of documents - each generation connected to the previous one, and all ancestral to the present document. Thus, there are levels in temporal structure-that is, antecedent and successor documents-and these require that documents be identified in relation to other documents. This gives a set of documents an "irrevocable order," a loose order which Fairthorne calls "bibliographic time," and which is "generated by the fact of continual growth" (p. 364). He does not consider "bibliographic time" to be an equivalent to physical time because bibliographic events, as part of communication, require delay. Sets of documents, as indicated above, rather than single works, are used in classification. While an event, a person, a unique feature of the environment, may create a class of one-such as the French Revolution, Napoleon, Niagara Falls-revolutions, emperors, and waterfalls are sets which, as sets, will subsume individuals and make normal classes.

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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

The arts, humanities, and social sciences commonly borrow concepts and methods from the sciences, but interdisciplinary borrowing seldom occurs in the opposite direction. Research on narrative discourse is relevant to problems of documentary storage and retrieval, for the arts and humanities in particular, but also for other broad areas of knowledge. This paper views the potential application of narrative discourse analysis to information storage and retrieval problems from 2 perspectives: 1) analysis and comparison of narrative documents in all disciplines may be simplified if fundamental categories that occur in narrative documents can be isolated; and 2) the possibility of subdividing the world of knowledge initially into narrative and non-narrative documents is explored with particular attention to Werlich's work on text types

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Abstract

The philosophy of history can help articulate problems relevant to information organization. One such problem is "aboutness": How do texts relate to the world? In response to this problem, philosophers of history have developed theories of colligation describing how authors bind together phenomena under organizing concepts. Drawing on these ideas, I present a theory of subject analysis that avoids the problematic illusion of an independent "landscape" of subjects. This theory points to a broad vision of the future of information organization and some specific challenges to be met.

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Abstract

Sentiment analysis on Twitter has attracted much attention recently due to its wide applications in both, commercial and public sectors. In this paper we present SentiCircles, a lexicon-based approach for sentiment analysis on Twitter. Different from typical lexicon-based approaches, which offer a fixed and static prior sentiment polarities of words regardless of their context, SentiCircles takes into account the co-occurrence patterns of words in different contexts in tweets to capture their semantics and update their pre-assigned strength and polarity in sentiment lexicons accordingly. Our approach allows for the detection of sentiment at both entity-level and tweet-level. We evaluate our proposed approach on three Twitter datasets using three different sentiment lexicons to derive word prior sentiments. Results show that our approach significantly outperforms the baselines in accuracy and F-measure for entity-level subjectivity (neutral vs. polar) and polarity (positive vs. negative) detections. For tweet-level sentiment detection, our approach performs better than the state-of-the-art SentiStrength by 4-5% in accuracy in two datasets, but falls marginally behind by 1% in F-measure in the third dataset.

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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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Abstract

Purpose - To provide a better-informed view of the extent of the semantic gap in image retrieval, and the limited potential for bridging it offered by current semantic image retrieval techniques. Design/methodology/approach - Within an ongoing project, a broad spectrum of operational image retrieval activity has been surveyed, and, from a number of collaborating institutions, a test collection assembled which comprises user requests, the images selected in response to those requests, and their associated metadata. This has provided the evidence base upon which to make informed observations on the efficacy of cutting-edge automatic annotation techniques which seek to integrate the text-based and content-based image retrieval paradigms. Findings - Evidence from the real-world practice of image retrieval highlights the existence of a generic-specific continuum of object identification, and the incidence of temporal, spatial, significance and abstract concept facets, manifest in textual indexing and real-query scenarios but often having no directly visible presence in an image. These factors combine to limit the functionality of current semantic image retrieval techniques, which interpret only visible features at the generic extremity of the generic-specific continuum. Research limitations/implications - The project is concerned with the traditional image retrieval environment in which retrieval transactions are conducted on still images which form part of managed collections. The possibilities offered by ontological support for adding functionality to automatic annotation techniques are considered. Originality/value - The paper offers fresh insights into the challenge of migrating content-based image retrieval from the laboratory to the operational environment, informed by newly-assembled, comprehensive, live data.

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Abstract

Aboutness ranks amongst our field's greatest bugbears. What is a work about? How can this be known? This mirrors debates within the philosophy of language, where the concept of representation has similarly evaded satisfactory definition. This paper proposes that we abandon the strong sense of the word aboutness, which seems to promise some inherent relationship between work and subject, or, in philosophical terms, between word and world. Instead, we seek an etymological reset to the older sense of aboutness as "in the vicinity, nearby; in some place or various places nearby; all over a surface." To distinguish this sense in the context of information studies, we introduce the term episemantics. The authors have each independently applied this term in slightly different contexts and scales (Hauser 2018a; Tennis 2016), and this article presents a unified definition of the term and guidelines for applying it at the scale of both words and works. The resulting weak concept of aboutness is pragmatic, in Star's sense of a focus on consequences over antecedents, while reserving space for the critique and improvement of aboutness determinations within various contexts and research programs. The paper finishes with a discussion of the implication of the concept of episemantics and methodological possibilities it offers for knowledge organization research and practice. We draw inspiration from Melvil Dewey's use of physical aroundness in his first classification system and ask how aroundness might be more effectively operationalized in digital environments.

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Date

8. 3.1997 10:05:29

Source

Computers and the humanities. 29(1995) no.5, S.339-361

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Date

8. 3.1997 10:05:29

Source

Computers and the humanities. 29(1995) no.4, S.259-270

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Source

Journal of documentation. 29(1973) no.2, S.137-168

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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

5. 8.2006 13:22:44

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Date

22. 9.1997 19:16:05

Footnote

Arguing that catalogers need to work both quickly and accurately, Bade maintains that employing specialists is the most efficient and effective way to achieve this outcome. Far less compelling than these arguments are Bade's concluding remarks, in which he offers meager suggestions for correcting the problems as he sees them. Overall, this essay is little more than a curmudgeon's diatribe. Addressed primarily to catalogers and library administrators, the analysis presented is too superficial to assist practicing catalogers or cataloging managers in developing solutions to any systemic problems in current cataloging practice, and it presents too little evidence of pervasive problems to convince budget-conscious library administrators of a need to alter practice or to increase their investment in local cataloging operations. Indeed, the reliance upon anecdotal evidence and the apparent nit-picking that dominate the essay might tend to reinforce a negative image of catalogers in the minds of some. To his credit, Bade does provide an important reminder that it is the intellectual contributions made by thousands of erudite catalogers that have made shared cataloging a successful strategy for improving cataloging efficiency. This is an important point that often seems to be forgotten in academic libraries when focus centers an cutting costs. Had Bade focused more narrowly upon the issue of deintellectualization of cataloging and written a carefully structured essay to advance this argument, this essay might have been much more effective." - KO 29(2002) nos.3/4, S.236-237 (A. Sauperl)

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Date

5. 8.2006 13:22:08