Streamlining Text Data Preparation and Label Consistency with LexiCleanse and EmoLabel Mapper for Prompt-Based Sentiment and Emotion Detection
Keywords:
Text Preparation, Label Consistency, LexiCleanse, EmoLabel Mapper, Sentiment Detection, Emotion Detection, Data Streamlining, Prompt-Based Analysis, Data Cleaning, Label Mapping.Abstract
Prompt-Based Sentiment and Emotion Detection, an evolving area within Natural Language Processing (NLP), employs a unique approach where sentiment and emotions are analyzed based on specific prompts. This paper presents a comprehensive research methodology for streamlining text data preparation and ensuring label consistency in prompt-based sentiment and emotion detection. Although this publication does not include experimental results, the methodology provides valuable insights for researchers and practitioners in natural language processing (NLP). The methodology begins with Data Collection and Preparation, covering data source identification, retrieval, structured storage, and effective cleaning using the LexiCleanse algorithm. Model selection guidelines are then discussed, focusing on task requirements, model capabilities, and resource constraints. Domain-Specific Sentiment and Emotion Fine-Tuning (DSEFT) is introduced as a technique to enhance pre-trained language models' performance for specific domains. The methodology also outlines the importance of optimizing prompts to guide models effectively. EmoLabel Mapper, a technique for mapping model outputs to human-understandable labels, is introduced for result interpretation. While experimental results are not included here, this methodology serves as a roadmap for future research, encouraging its application to real-world datasets and further advancements in sentiment and emotion analysis in the evolving NLP landscape.
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