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Abstract

Die dritte Session, die von Michael Vielhaber vom Österreichischen Rundfunk moderiert wurde, machte die Teilnehmerinnen und Teilnehmer mit zukunftsweisenden Werkzeugen und Konzepten zur KI-unterstützten Erschließung von Audio- und Videodateien bekannt. Alle vier vorgestellten Technologien bewähren sich bereits in ihren praktischen Anwendungsumgebungen.

Date

22. 5.2021 12:43:05

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Abstract

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

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Abstract

Since information science concerns the transmission of records, it concerns context. The transmission of documents ensures their arrival in new contexts. Documents and their copies are spread across times and places. The amount of labor required to discover and retrieve relevant documents is also formulated by context. Thus, any serious consideration of communication and of information technologies quickly leads to a concern with context, relevance, and labor. Information scientists have developed many theories of context, relevance, and labor but not a framework for organizing them and describing their relationship with one another. We propose the words context and relevance can be used to articulate a useful framework for considering the diversity of approaches to context and relevance in information science, as well as their relations with each other and with labor.

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Abstract

The purpose of this paper is to provide an overview of aboutness and conceptual analysis, essential concepts for LIS practitioners to understand. Aboutness refers to the subject matter and genre/form properties of a resource. It is identified during conceptual analysis, which yields an aboutness statement, a summary of a resource's aboutness. While few scholars have discussed the aboutness determination process in detail, the methods described by Patrick Wilson, D.W. Langridge, Arlene G. Taylor, and Daniel N. Joudrey provide exemplary frameworks for determining aboutness and are presented here. Discussions of how to construct an aboutness statement and the challenges associated with aboutness determination follow.