Contextual Ambiguity Framework for Enhanced Sentiment Analysis

Abstract

Negation is a universal linguistic phenomenon that affects the performance of Natural Language Processing (NLP) applications, especially opinion mining data. Many words exists in sentences that have multiple interpretations or sentiments depending on how they are placed with respect to the negation word in the sentence. A cutting-edge framework is designed to tackle the nuanced challenge of detecting contextual ambiguity through negation in sentiment analysis. The approach uniquely combines advanced natural language processing techniques with deep linguistic insights, enabling the accurate interpretation of sentiment in complex sentences where negation plays a key role. The framework identifies negation cues and their scope, then assesses their impact on sentiment, considering contextual dependencies and word semantics. The model's innovation lies in context-sensitive algorithms that adeptly handle different sentence structures and idiomatic expressions, a notable advancement over traditional sentiment analysis tools. Particularly effective in interpreting sarcastic or ironic statements, the framework significantly outperforms existing models in accuracy, especially in negation-heavy contexts. This advancement enhances sentiment analysis applications like social media monitoring and customer feedback analysis, offering a more nuanced understanding of public opinion.

Keywords

• contextual ambiguity
• linguistic nuances
• machine learning
• natural language processing
• sentiment interpretation
• text analytics