Comparative Analyses of Multilingual Sentiment Analysis Systems for News and Social Media

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43970158" target="_blank" >RIV/49777513:23520/23:43970158 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/book/10.1007/978-3-031-24340-0" target="_blank" >https://link.springer.com/book/10.1007/978-3-031-24340-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-24340-0_20" target="_blank" >10.1007/978-3-031-24340-0_20</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Analyses of Multilingual Sentiment Analysis Systems for News and Social Media

Popis výsledku v původním jazyce

In this paper, we present evaluation of three in-house sentiment analysis (SA) systems originally designed for three distinct SA tasks, in a highly multilingual setting. For the evaluation, we collected a large number of available gold standard datasets, in different languages and varied text types. The aim of using different domain datasets was to achieve a clear snapshot of the level of overall performance of the systems and thus obtain a better quality of an evaluation. We compare the results obtained with the best performing systems evaluated on their basis and performed an in-depth error analysis. Based on the results, we can see that some systems perform better for different datasets and tasks than the ones they were designed for, showing that we could replace one system with another and gain an improvement in performance. Our results are hardly comparable with the original dataset results because the datasets often contain a different number of polarity classes than we used, and for some datasets, there are even no basic results. For the cases in which a comparison was possible, our results show that our systems perform very well in view of multilinguality.

Název v anglickém jazyce

Comparative Analyses of Multilingual Sentiment Analysis Systems for News and Social Media

Popis výsledku anglicky

In this paper, we present evaluation of three in-house sentiment analysis (SA) systems originally designed for three distinct SA tasks, in a highly multilingual setting. For the evaluation, we collected a large number of available gold standard datasets, in different languages and varied text types. The aim of using different domain datasets was to achieve a clear snapshot of the level of overall performance of the systems and thus obtain a better quality of an evaluation. We compare the results obtained with the best performing systems evaluated on their basis and performed an in-depth error analysis. Based on the results, we can see that some systems perform better for different datasets and tasks than the ones they were designed for, showing that we could replace one system with another and gain an improvement in performance. Our results are hardly comparable with the original dataset results because the datasets often contain a different number of polarity classes than we used, and for some datasets, there are even no basic results. For the cases in which a comparison was possible, our results show that our systems perform very well in view of multilinguality.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF17_048%2F0007267" target="_blank" >EF17_048/0007267: VaV inteligentních komponent pokročilých technologií pro plzeňskou metropolitní oblast</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computational Linguistics and Intelligent Text Processing

ISBN

978-3-031-24339-4

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

20

Strana od-do

260-279

Název nakladatele

Springer

Místo vydání

Cam

Místo konání akce

La Rochelle, France

Datum konání akce

7. 4. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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