A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
원문 초록
There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff's alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Understanding and Tackling Label Errors in Individual-Level Nature Language Understanding
DCR: Quantifying Data Contamination in LLMs Evaluation
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles
Characterizing Linguistic Shifts in Croatian News via Diachronic Word Embeddings
CAtCh: Cognitive Assessment through Cookie Thief
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.