This work is like a guidebook that explains how computers learn to understand whether people’s opinions in reviews, posts, or comments are positive, negative, or neutral, and how to apply those techniques in real-world consumer settings.
Organizations struggle to systematically interpret large volumes of unstructured customer opinions (reviews, surveys, social media) into actionable insights. This research maps out methods and applications in sentiment analysis so teams can choose and apply the right techniques to better understand consumer attitudes at scale.
Methodological breadth and synthesis of techniques across applications, which can inform more robust, domain-adapted sentiment systems when combined with proprietary consumer data.
Hybrid
Vector Search
Medium (Integration logic)
Label quality and domain adaptation for diverse consumer text sources (reviews, chats, social media).
Early Majority
Positions sentiment analysis as a bridge between underlying techniques (classical ML, deep learning, possibly LLM-based methods) and concrete consumer-facing applications, offering a structured overview rather than a single-point solution.