@inproceedings{steinberger-etal-2017-large,
title = "Large-scale news entity sentiment analysis",
author = "Steinberger, Ralf and
Hegele, Stefanie and
Tanev, Hristo and
Della Rocca, Leonida",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing, {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-049-6_091",
doi = "10.26615/978-954-452-049-6_091",
pages = "707--715",
abstract = "We work on detecting positive or negative sentiment towards named entities in very large volumes of news articles. The aim is to monitor changes over time, as well as to work towards media bias detection by com-paring differences across news sources and countries. With view to applying the same method to dozens of languages, we use lin-guistically light-weight methods: searching for positive and negative terms in bags of words around entity mentions (also consid-ering negation). Evaluation results are good and better than a third-party baseline sys-tem, but precision is not sufficiently high to display the results publicly in our multilin-gual news analysis system Europe Media Monitor (EMM). In this paper, we focus on describing our effort to improve the English language results by avoiding the biggest sources of errors. We also present new work on using a syntactic parser to identify safe opinion recognition rules, such as predica-tive structures in which sentiment words di-rectly refer to an entity. The precision of this method is good, but recall is very low.",
}
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%0 Conference Proceedings
%T Large-scale news entity sentiment analysis
%A Steinberger, Ralf
%A Hegele, Stefanie
%A Tanev, Hristo
%A Della Rocca, Leonida
%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 steinberger-etal-2017-large
%X We work on detecting positive or negative sentiment towards named entities in very large volumes of news articles. The aim is to monitor changes over time, as well as to work towards media bias detection by com-paring differences across news sources and countries. With view to applying the same method to dozens of languages, we use lin-guistically light-weight methods: searching for positive and negative terms in bags of words around entity mentions (also consid-ering negation). Evaluation results are good and better than a third-party baseline sys-tem, but precision is not sufficiently high to display the results publicly in our multilin-gual news analysis system Europe Media Monitor (EMM). In this paper, we focus on describing our effort to improve the English language results by avoiding the biggest sources of errors. We also present new work on using a syntactic parser to identify safe opinion recognition rules, such as predica-tive structures in which sentiment words di-rectly refer to an entity. The precision of this method is good, but recall is very low.
%R 10.26615/978-954-452-049-6_091
%U https://doi.org/10.26615/978-954-452-049-6_091
%P 707-715
Markdown (Informal)
[Large-scale news entity sentiment analysis](https://doi.org/10.26615/978-954-452-049-6_091) (Steinberger et al., RANLP 2017)
ACL
- Ralf Steinberger, Stefanie Hegele, Hristo Tanev, and Leonida Della Rocca. 2017. Large-scale news entity sentiment analysis. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 707–715, Varna, Bulgaria. INCOMA Ltd..