Sentiment Analysis (SA) has emerged as one of the fastest growing research trends in the last few years as exponential numbers of global internet users are expressing their opinions through various social media platforms across a wide range of issues. Emotion and polarity prediction, from customer feedback through various social media such as Facebook, Twitter, etc., is an important emerging subfield of predictive modelling. Most recently, many big companies have been using various computational intelligence algorithms to understand customers' attitudes towards their products and in order to successfully run their businesses. In this way, Sentiment Analysis has emerged as a critical tool in decision making because social media platforms are used as the most preferred medium to record such issues. Computational Intelligence Applications for Text and Sentiment Data Analysis explores the most recent advances in text information processing and data analysis technologies, specifically focusing on sentiment analysis from multi-faceted data. It investigates a wide range of challenges involved in the accurate analysis of online sentiments, including how to i) identify subjective information from text i.e. exclusion of 'neutral' or 'factual' comments that do not carry sentiment information, ii) identify sentiment polarity, and iii) domain dependency. Spam and fake news detection, short abbreviation, sarcasm, word negation, and a lot of word ambiguity are also explored. Further chapters look at the difficult process of extracting sentiment from different multimodal information (audio, video and text), semantic concepts. In each chapter, authors explore how computational intelligence (CI) techniques, such as deep learning, convolutional neural network, fuzzy and rough set, global optimizers, and hybrid machine learning techniques, play an important role in solving the inherent problems of sentiment analysis applications.

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