A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing †

Abstract

Business and science are using sentiment analysis to extract and assess subjective information from the web, social media, and other sources using NLP, computational linguistics, text analysis, image processing, audio processing, and video processing. It models polarity, attitudes, and urgency from positive, negative, or neutral inputs. Unstructured data make emotion assessment difficult. Unstructured consumer data allow businesses to market, engage, and connect with consumers on social media. Text data are instantly assessed for user sentiment. Opinion mining identifies a text’s positive, negative, or neutral opinions, attitudes, views, emotions, and sentiments. Text analytics uses machine learning to evaluate “unstructured” natural language text data. These data can help firms make money and decisions. Sentiment analysis shows how individuals feel about things, services, organizations, people, events, themes, and qualities. Reviews, forums, blogs, social media, and other articles use it. DD (data-driven) methods find complicated semantic representations of texts without feature engineering. Data-driven sentiment analysis is three-tiered: document-level sentiment analysis determines polarity and sentiment, aspect-based sentiment analysis assesses document segments for emotion and polarity, and data-driven (DD) sentiment analysis recognizes word polarity and writes positive and negative neutral sentiments. Our innovative method captures sentiments from text comments. The syntactic layer encompasses various processes such as sentence-level normalisation, identification of ambiguities at paragraph boundaries, part-of-speech (POS) tagging, text chunking, and lemmatization. Pragmatics include personality recognition, sarcasm detection, metaphor comprehension, aspect extraction, and polarity detection; semantics include word sense disambiguation, concept extraction, named entity recognition, anaphora resolution, and subjectivity detection.