Abstract: This research explores a framework for selective answer analysis using keyword-based filtering and semantic similarity techniques. With the increasing volume of textual data generated through surveys, feedback mechanisms, and question-answer systems, it is often impractical and unnecessary to process every response. Our approach filters and analyzes only those answers that align with a specified set of keywords or topics of interest, leveraging advanced natural language processing (NLP) algorithms to prioritize relevance and reduce computational overhead. By combining lexical filtering with semantic matching, we aim to improve the efficiency, scalability, and interpretability of text analytics. The framework is evaluated on a diverse set of survey responses and demonstrates improved focus, accuracy, and thematic coherence in analysis. Additionally, the methodology incorporates dynamic thresholding to adapt to varying data densities and context-specific requirements, ensuring robust performance across datasets. Practical applications span customer sentiment analysis, educational assessment automation, and large-scale social research, offering a versatile solution for targeted data exploration. Future enhancements will focus on integrating machine learning models for adaptive keyword refinement and automated thematic categorization, further bridging the gap between precision and scalability in text analytics.
Keywords: Selective answer analysis, keyword-based filtering, semantic similarity, natural language processing (NLP), lexical filtering, thematic analysis, computational efficiency, dynamic thresholding, automated categorization, text analytics, survey response evaluation, domain adaptability.
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DOI:
10.17148/IJARCCE.2025.14595
