Machine Learning and Lexical Rule-Based Cost-Efficient Emotion Annotation of Hinglish Utterances
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a hybrid active learning framework for emotion annotation in Hinglish text. It achieves high accuracy and reduces costs by combining rules, machine learning, and expert feedback.
Area Of Science
- Computational Linguistics
- Natural Language Processing
- Affective Computing
Background
- Emotion annotation in code-mixed languages like Hinglish faces challenges due to linguistic complexity and limited resources.
- Existing methods often struggle with the nuances of Hinglish, impacting accuracy and cost-efficiency.
Purpose Of The Study
- To develop a cost-efficient, high-accuracy emotion annotation framework for Hinglish text.
- To integrate lexical rules, machine learning, and active learning for improved annotation.
- To address the unique challenges of emotion analysis in code-mixed languages.
Main Methods
- A hybrid active learning framework combining lexical rules, machine learning, and iterative expert feedback.
- Utilized bilingual emotion dictionaries, subword tokenization for compound terms, and active learning for sample prioritization.
- Grounded in Discrete Emotions Theory and Cognitive Appraisal Theory.
Main Results
- Achieved 81% accuracy (F-score: 0.76) on a 19,000 Hinglish tweets dataset related to war and conflict.
- Reduced operational costs by 40% compared to traditional manual annotation.
- Lexical rules resolved 89% of code-switching ambiguities, with iterative refinements increasing accuracy from 72% to 81%.
Conclusions
- The hybrid framework significantly enhances emotion annotation accuracy in Hinglish while reducing costs.
- Integrating rule-based methods with active learning and machine learning is effective for code-mixed language processing.
- Automated preprocessing of emojis, hashtags, and slang contributes to system efficiency.
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