Abstract: This work evaluates textual favorability by means of Natural Language Processing (NLP) analysis of sentiment. Favorability is the degree of like or hate of an individual, policy, or product or service. Text preparation, sentiment scoring, sentiment intensity classification and evaluation via machine training and deep learning models are part of the process. Advanced models such as BERT grasp its context, psychological tone, and minute clues. This approach is valuable in feedback from customers systems, brand monitoring, and political analysis as it enables businesses to make informed judgments grounded on public opinion. In this work, favorability capture in machine learning and lexicon-based approaches is compared. Faster and more comprehensible compared to transformer-based approaches like BERT and RoBERTa, which have greater contextual knowledge for sensitive gestures, sarcasm, and domain-specific emotion, lexicon-based techniques like VADER and TextBlob have These models are built using datasets with annotations of real-world attitudes to better separate obvious favorability from generic positivity. The paper also covers linguistic ambiguity, social media writing noise, and sentiment classification subjectivity. One hybrid solution comprising named entity identification, sentiment ratings, and aspect-based analysis is proposed to overcome these problems. This helps to easily monitor sentiment trends around entities or subjects over time. In political forecasting, customer experience enhancement, and reputation management, sentiment-driven favorability analysis may support strategic decisions.
Keywords: sentiment analysis, favorability, NLP, machine learning, deep learning, BERT, emotion detection, text classification, VADER, public opinion.
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DOI:
10.17148/IJARCCE.2025.14707
