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    <titleInfo>
        <title>Good News vs. Bad News: What are they talking about?</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>Today’s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad but have to be delivered in neutral words as pure facts. Although there are applications for sentiment analysis of news, the task of news analysis is still a very actual problem because the latest news impacts people’s lives daily. In this paper, we explored the problem of sentiment analysis for Ukrainian and Russian news, developed a corpus of Ukrainian and Russian news and annotated each text using one of three categories: positive, negative and neutral. Each text was marked by at least three independent annotators via the web interface, the inter-annotator agreement was analyzed and the final label for each text was computed. These texts were used in the machine learning experiments. Further, we investigated what kinds of named entities such as Locations, Organizations, Persons are perceived as good or bad by the readers and which of them were the cause for text annotation ambiguity.</abstract>
    <identifier type="citekey">kanishcheva-bobicev-2017-good</identifier>
    <identifier type="doi">10.26615/978-954-452-049-6_044</identifier>
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%0 Conference Proceedings
%T Good News vs. Bad News: What are they talking about?
%A Kanishcheva, Olga
%A Bobicev, Victoria
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F kanishcheva-bobicev-2017-good
%X Today’s massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. The main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad but have to be delivered in neutral words as pure facts. Although there are applications for sentiment analysis of news, the task of news analysis is still a very actual problem because the latest news impacts people’s lives daily. In this paper, we explored the problem of sentiment analysis for Ukrainian and Russian news, developed a corpus of Ukrainian and Russian news and annotated each text using one of three categories: positive, negative and neutral. Each text was marked by at least three independent annotators via the web interface, the inter-annotator agreement was analyzed and the final label for each text was computed. These texts were used in the machine learning experiments. Further, we investigated what kinds of named entities such as Locations, Organizations, Persons are perceived as good or bad by the readers and which of them were the cause for text annotation ambiguity.
%R 10.26615/978-954-452-049-6_044
%U https://doi.org/10.26615/978-954-452-049-6_044
%P 325-333

Markdown (Informal)

[Good News vs. Bad News: What are they talking about?](https://doi.org/10.26615/978-954-452-049-6_044) (Kanishcheva & Bobicev, RANLP 2017)

• Good News vs. Bad News: What are they talking about? (Kanishcheva & Bobicev, RANLP 2017)

ACL

• Olga Kanishcheva and Victoria Bobicev. 2017. Good News vs. Bad News: What are they talking about?. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 325–333, Varna, Bulgaria. INCOMA Ltd..