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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

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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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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Abstract

The fan of past documents may be seen across time as a philosophical "wake," translated documents as a sideways relationship and future documents as another fan spreading forward from a given document (p. 365). The "overlap of reading histories can be used to detect common interests among readers," (p. 365) and readers may be classified accordingly. Finally, Fairthorne rejects the notion of a "general" classification, which he regards as a mirage, to be replaced by a citation-type network to identify classes. An interesting feature of his work lies in his linkage between old and new documents via a bibliographic method-citations, authors' names, imprints, style, and vocabulary - rather than topical (subject) terms. This is an indirect method of creating classes. The subject (aboutness) is conceived as a finite, common sharing of knowledge over time (past, present, and future) as opposed to the more common hierarchy of topics in an infinite schema assumed to be universally useful. Fairthorne, a mathematician by training, is a prolific writer an the foundations of classification and information. His professional career includes work with the Royal Engineers Chemical Warfare Section and the Royal Aircraft Establishment (RAE). He was the founder of the Computing Unit which became the RAE Mathematics Department.