By Frank Hopfgartner
This textual content offers an summary of clever info structures for either the personal and public quarter, highlighting the examine questions that may be studied by means of utilising computational intelligence. The ebook demonstrates how you can rework uncooked info into potent shrewdpermanent info providers, protecting the demanding situations and strength of this process. each one bankruptcy describes the algorithms, instruments, measures and reviews used to reply to vital questions. this can be then extra illustrated by way of a various choice of case reports reflecting real difficulties confronted via SMEs, multinational brands, provider businesses, and the general public region. good points: offers a cutting-edge advent to the sphere, integrating contributions from either academia and undefined; experiences novel details aggregation providers; discusses personalization and advice structures; examines sensor-based wisdom acquisition companies, describing how the research of sensor facts can be utilized to supply a transparent photo of our world.
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The authors use parts of the dataset to generate sentiment dictionaries containing entries scored with different measures. For the sake of media response analysis, the authors evaluate news material and propose a supervised machine learning approach which is similar to ours. Scholz et al. analyze the news in two stages. First, they detect subjective statements and, second, they classify the subjective statements as either positive or negative. In contrast to our findings, the subjectivity detection seems to perform better on the dataset of Scholz et.
Inspired by Polanyi and Zaenen [40] we intend in our future work to imply more contextual shifters and patterns for German to calculate contextual feature weights instead of only encoding the presence and frequency of valence shifters. At the same time we plan to consider discourse markers for feature weight calculation following Mukherjee and Bhattacharyya [32]. In addition, appraisal groups may serve as supplementary information for the feature vectors [53]. Considering sentiment 40 D. Ploch targets our future work will include on the one hand the extension of our corpus by more text-anchored sentiment targets and on the other hand we will shift our work toward topic-oriented sentiment target detection that aims at determining supporting or opposing statements [34].
3 “Der Fakt alleine ist eine absolute Unverschämtheit gegenüber dem Klub und dem Team”, sagte Horstmann. 3 Corpus Overview The final corpus exclusively contains quotations annotated by at least two annotators. The gold standard answers were determined by majority voting. We discarded quotations where the annotators predominantly disagreed or where the majority of the annotators marked a quotation as don’t know. The inter-annotator agreement amounts to 79 %. 6 shows the distribution of neutral, positive, negative, and mixed quotations.