Domain-Adaptive Sentiment Analysis Pipeline for Competitor Intelligence

Domain-Adaptive Sentiment Analysis Pipeline for Competitor Intelligence Raja Banerjee, Koushik Ganguly, Sourabh GhoshRND Consultancy Services This paper presents a novel domain-adaptive sentiment analysis pipeline tailored for competitor intelligence, focusing on profiling corporate executives' personalities and communication styles. Unlike generic sentiment models, our approach integrates multiple NLP techniques—including sentiment classification, lexical-style profiling, topic modeling, and semantic embeddings—adapted specifically to corporate communication domains. By processing diverse public data sources such as blogs, social media posts, interviews, and press releases, the system generates structured psychological profiles aligned with the Big Five personality traits. Our pipeline achieves a 12% improvement in F1-score compared to generic baselines and outputs results in a transparent, standardized JSON schema suitable for integration with decision-support and recommendation systems. We also address ethical considerations around privacy and bias, emphasizing responsible use within competitive intelligence frameworks. This work advances automated personality profiling for strategic market insights and negotiation support by bridging domain gaps in sentiment analysis. Keywords: Competitor Analysis, Sentiment Analysis, Personality Profiling, Domain Adaptation, NLP, Big Five Traits, Ethical AI