Transferring Sentiment Cross-Lingually within and across Same-Family Languages-Reference-Cited by-同舟云学术

Transferring Sentiment Cross-Lingually within and across Same-Family Languages

Published:2024-06-28 Issue:13 Volume:14 Page:5652
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Thakkar Gaurish¹^ORCID,Preradović Nives Mikelić¹,Tadić Marko¹

Affiliation:

1. Faculty of Humanities and Social Sciences, University of Zagreb, Ivana Lućića 3, 10000 Zagreb, Croatia

Abstract

Natural language processing for languages with limited resources is hampered by a lack of data. Using English as a hub language for such languages, cross-lingual sentiment analysis has been developed. The sheer quantity of English language resources raises questions about its status as the primary resource. This research aims to examine the impact on sentiment analysis of adding data from same-family versus distant-family languages. We analyze the performance using low-resource and high-resource data from the same language family (Slavic), investigate the effect of using a distant-family language (English) and report the results for both settings. Quantitative experiments using multi-task learning demonstrate that adding a large quantity of data from related and distant-family languages is advantageous for cross-lingual sentiment transfer.

Funder

European Union’s Horizon 2020 research and innovation program

Publisher

MDPI AG

Link

https://www.mdpi.com/2076-3417/14/13/5652/pdf

Reference124 articles.

1. Go, A., Bhayani, R., and Huang, L. (2009). Twitter Sentiment Classification Using Distant Supervision, Stanford University. CS224N Project Report.

2. Nakov, P., Rosenthal, S., Kozareva, Z., Stoyanov, V., Ritter, A., and Wilson, T. (2013, January 14–15). SemEval-2013 Task 2: Sentiment Analysis in Twitter. Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), Atlanta, GA, USA.

3. Saif, H., Fernández, M., He, Y., and Alani, H. (2013, January 3). Evaluation datasets for Twitter sentiment analysis: A survey and a new dataset, the STS-Gold. Proceedings of the 1st Interantional Workshop on Emotion and Sentiment in Social and Expressive Media: Approaches and Perspectives from AI (ESSEM 2013), Turin, Italy.

4. Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis;Wilson;Comput. Linguist.,2009

5. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., and Passonneau, R.J. (2011, January 23). Sentiment analysis of twitter data. Proceedings of the Workshop on Language in Social Media (LSM 2011), Portland, OR, USA.