InforLorV4, Hal, Corpus, bibRecord, 001648

Comparing Multilingual Comparable Articles Based On Opinions

Identifieur interne : 001648 ( Hal/Corpus ); précédent : 001647; suivant : 001649

Comparing Multilingual Comparable Articles Based On Opinions

Auteurs : Motaz Saad ; David Langlois ; Kamel Smaïli

Source :

RBID : Hal:hal-00851959

Abstract

Multilingual sentiment analysis attracts increased attention as the massive growth of multilingual web contents. This conducts to study opinions across different languages by comparing the underlying messages written by different people having different opinions. In this paper, we propose Sentiment based Comparability Measures (SCM) to compare opinions in multilingual comparable articles without translating source/target into the same language. This will allow media trackers (journalists) to automatically detect public opinion split across huge multilingual web contents. To develop SCM, we need either to get or to build parallel sentiment corpora. Because this kind of corpora are not available, we decided to build them. For that, we propose a new method to automatically label parallel corpora with sentiment classes. Then we use the extracted parallel sentiment corpora to develop multilingual sentiment analysis system. Experimental results show that, the proposed measure can capture differences in terms of opinions. The results also show that comparable articles variate in their objectivity and positivity.

Url:

https://hal.inria.fr/hal-00851959

Links to Exploration step

Hal:hal-00851959

Le document en format XML

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<front><div type="abstract" xml:lang="en">Multilingual sentiment analysis attracts increased attention as the massive growth of multilingual web contents. This conducts to study opinions across different languages by comparing the underlying messages written by different people having different opinions. In this paper, we propose Sentiment based Comparability Measures (SCM) to compare opinions in multilingual comparable articles without translating source/target into the same language. This will allow media trackers (journalists) to automatically detect public opinion split across huge multilingual web contents. To develop SCM, we need either to get or to build parallel sentiment corpora. Because this kind of corpora are not available, we decided to build them. For that, we propose a new method to automatically label parallel corpora with sentiment classes. Then we use the extracted parallel sentiment corpora to develop multilingual sentiment analysis system. Experimental results show that, the proposed measure can capture differences in terms of opinions. The results also show that comparable articles variate in their objectivity and positivity.</div>
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<abstract xml:lang="en">Multilingual sentiment analysis attracts increased attention as the massive growth of multilingual web contents. This conducts to study opinions across different languages by comparing the underlying messages written by different people having different opinions. In this paper, we propose Sentiment based Comparability Measures (SCM) to compare opinions in multilingual comparable articles without translating source/target into the same language. This will allow media trackers (journalists) to automatically detect public opinion split across huge multilingual web contents. To develop SCM, we need either to get or to build parallel sentiment corpora. Because this kind of corpora are not available, we decided to build them. For that, we propose a new method to automatically label parallel corpora with sentiment classes. Then we use the extracted parallel sentiment corpora to develop multilingual sentiment analysis system. Experimental results show that, the proposed measure can capture differences in terms of opinions. The results also show that comparable articles variate in their objectivity and positivity.</abstract>
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Comparing Multilingual Comparable Articles Based On Opinions

Comparing Multilingual Comparable Articles Based On Opinions

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