Cross-lingual Subjectivity Detection for Resource Lean Languages

Ida Amini, Samane Karimi, Azadeh Shakery


Abstract
Wide and universal changes in the web content due to the growth of web 2 applications increase the importance of user-generated content on the web. Therefore, the related research areas such as sentiment analysis, opinion mining and subjectivity detection receives much attention from the research community. Due to the diverse languages that web-users use to express their opinions and sentiments, research areas like subjectivity detection should present methods which are practicable on all languages. An important prerequisite to effectively achieve this aim is considering the limitations in resource-lean languages. In this paper, cross-lingual subjectivity detection on resource lean languages is investigated using two different approaches: a language-model based and a learning-to-rank approach. Experimental results show the impact of different factors on the performance of subjectivity detection methods using English resources to detect the subjectivity score of Persian documents. The experiments demonstrate that the proposed learning-to-rank method outperforms the baseline method in ranking documents based on their subjectivity degree.
Anthology ID:
W19-1310
Volume:
Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
June
Year:
2019
Address:
Minneapolis, USA
Venues:
NAACL | WASSA | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
81–90
Language:
URL:
https://aclanthology.org/W19-1310
DOI:
10.18653/v1/W19-1310
Bibkey:
Cite (ACL):
Ida Amini, Samane Karimi, and Azadeh Shakery. 2019. Cross-lingual Subjectivity Detection for Resource Lean Languages. In Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 81–90, Minneapolis, USA. Association for Computational Linguistics.
Cite (Informal):
Cross-lingual Subjectivity Detection for Resource Lean Languages (Amini et al., 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-1310.pdf