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Recent advances in machine learning have led to computer systems that are human-like in behaviour. Sentiment analysis, the automatic determination of emotions in text, is allowing us to capitalize on substantial previously unattainable opportunities in commerce, public health, government policy, social sciences, and art. Further, analysis of emotions in text, from news to social media posts, is improving our understanding of not just how people convey emotions through language but also how emotions shape our behaviour. This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications. We also discuss discuss how, without careful fore-thought, sentiment analysis has the potential for harmful outcomes. We outline the latest lines of research in pursuit of fairness in sentiment analysis.

Front Cover -- Sentiment Analysis in Social Networks -- Copyright -- Contents -- Contributors -- Editors' Biographies -- Preface -- Acknowledgments -- Chapter 1: Challenges of Sentiment Analysis in Social Networks: An Overview -- 1. Background -- 2. Sentiment Analysis in Social Networks: A New Research Approach -- 3. Sentiment Analysis Characteristics -- 3.1. Sentiment Categorization: Objective Versus Subjective Sentences -- 3.2. Levels of Analysis -- 3.3. Regular Versus Comparative Opinion -- 3.4. Explicit Versus Implicit Opinions -- 3.5. The Role of Semantics

Part 3: Mobile Device Forensics ; International audience ; Humans tend to use specific words to express their emotional states in written and oral communications. Scientists in the area of text mining and natural language processing have studied sentiment fingerprints residing in text to extract the emotional polarity of customers for a product or to evaluate the popularity of politicians. Recent research focused on micro-blogging has found notable similarities between Twitter feeds and SMS (short message service) text messages. This paper investigates the common characteristics of both formats for sentiment analysis purposes and verifies the correctness of the similarity assumption. A lexicon-based approach is used to extract and compute the sentiment scores of SMS messages found on smartphones. The data is presented along a timeline that depicts a sender's emotional fingerprint. This form of analysis and visualization can enrich a forensic investigation by conveying potential psychological patterns from text messages.

Sentiment analysis offers many benefits and opportunities from business, government and consumer perspective in this digital data explosive age. According to Google, in partnership with KPMG India report (April-2017), titled Indian Languages Defining Indias Internet currently India today has 234 million Indian Language users who are online, compared to 175 million English web users and expected 536 million Indians to use regional languages while online by 2021. However Marathi users are expected to make significant contribution to define India Internet volume. By considering aspects of Marathi language and benefits of sentiment analysis, this paper presents a approach to overcome the barriers and difficulties being faced for analyzing text in Marathi language. The proposed system detects hidden sentiments in text of Marathi language. The system uses sentiment analysis methodology in order to achieve desired functionality. In this system, a corpus based approach is proposed, i.e the creation of a diverse up to date corpus of Marathi keywords along with their individual polarities with respect to the Word Net which is consider as a corpus. The algorithm is used to calculate the cumulative polarity of the text and rank the sentence as positive negative or neutral on a set scale standard.

Sentiment analysis offers many benefits and opportunities from business, government and consumer perspective in this digital data explosive age. According to Google, in partnership with KPMG India report (April-2017), titled Indian Languages Defining Indias Internet currently India today has 234 million Indian Language users who are online, compared to 175 million English web users and expected 536 million Indians to use regional languages while online by 2021. However Marathi users are expected to make significant contribution to define India Internet volume. By considering aspects of Marathi language and benefits of sentiment analysis, this paper presents a approach to overcome the barriers and difficulties being faced for analyzing text in Marathi language. The proposed system detects hidden sentiments in text of Marathi language. The system uses sentiment analysis methodology in order to achieve desired functionality. In this system, a corpus based approach is proposed, i.e the creation of a diverse up to date corpus of Marathi keywords along with their individual polarities with respect to the Word Net which is consider as a corpus. The algorithm is used to calculate the cumulative polarity of the text and rank the sentence as positive negative or neutral on a set scale standard. https://journalnx.com/journal-article/20150361

Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.

Social networking websites are considered as major sources of opinions and emotions of the public on social issues. Twitter a microblogging website where users share millions of views on a wide range of topics on a daily basis which can be used for Sentiment analysis in order to identify user's opinions, likes, dislikes, feedbacks, etc. Analysis of people's sentiment in a text and classification in terms of positive, negative or neutral is a challenging task. In this paper, Twitter has been used as a forum to analyze and graphically represent people's opinion of India towards recently introduced Goods and Services Tax by Indian Government on 1st July 2017.The tweets originating in India on 30th June and 1st July have been extracted then classified as positive, negative and neutral sentiments.

This paper describes the importance of Neutrosophy Theory in order to find a method that could solve the uncertainties arising on discursive analysis. The aim of this pilot study is to find a procedure to diminish the uncertainties from public discourse induced, especially, by humans (politicians, journalists, etc.). We consider that Neutrosophy Theory is a sentiment analysis specific case regarding processing of the three states: positive, negative, and neutral. The study is intended to identify a method to answer to uncertainties solving in order to support politician's staff, NLP specialists, artificial intelligence researchers and generally the electors.

This paper describes the importance of neu-trosophy theory in order to find a method that could solve the uncertainties arising on discursive analysis. The aim of this pilot study is to find a procedure to diminish the uncertainties from public discourse induced, especially, by humans (politicians, journalists, etc.). We con-sider that Neutrosophy Theory1 is a sentiment analysis specific case regarding processing of the three states: positive, negative, and neutral. The study is intended to identify a method to answer to uncertainties solving in order to sup-port politician's staff, NLP specialists, artificial intelligence researchers and generally the electors.

Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.