Evolution of Sentiment Analysis: Methodologies and Paradigms

With the advent of the digital age, almost everything has come down to better understanding of the data. Natural language processing is equally in pursuit and is rather among the most researched areas of computer science. Post 1980, a major revolution in NLP embarked with the emergence of machine learning algorithms resulting from steady escalation in computational power. Unlike other data, text semantics becomes more complex both because of its contextual nature and daily evolving language usage. While the continuous efforts of improving language representation for logical units interpretation is still prevalent, much to our realization, traditional, and long established recurrent neural networks which were supposed to grasp a bi-directional context of language have been surpassed by attention models in constructing improved embeddings allowing systems to better understand language. Among numerous applications circumventing, understanding sentiment of text has been widespread in fields including but not limited to customer reviews, stock market, elections, healthcare analytics, online, and social media analytics. From binary classification of it to more challenging cases such as negation handling, sarcasm, toxicity, multiple attitudes, or polarity, this research chapter explores the evolution of sentiment analysis in the light of emerging text processing and the transition of text understanding from rule-based to a statistical one with a comparison of benchmark performance from state-of-the-art models over various applications and datasets.