Sentiment Analysis For Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection.

This edited volume provides the reader with a fully updated, in-depth treatise on the emerging principles, conceptual underpinnings, algorithms and practice of Computational Intelligence in the realization of concepts and implementation of models of sentiment analysis and ontology –oriented engineering. The volume involves studies devoted to key issues of sentiment analysis, sentiment models, and ontology engineering. The book is structured into three main parts. The first part offers a comprehensive and prudently structured exposure to the fundamentals of sentiment analysis and natural language processing. The second part consists of studies devoted to the concepts, methodologies, and algorithmic developments elaborating on fuzzy linguistic aggregation to emotion analysis, carrying out interpretability of computational sentiment models, emotion classification, sentiment-oriented information retrieval, a methodology of adaptive dynamics in knowledge acquisition. The third part includes a plethora of applications showing how sentiment analysis and ontologies becomes successfully applied to investment strategies, customer experience management, disaster relief, monitoring in social media, customer review rating prediction, and ontology learning. This book is aimed at a broad audience of researchers and practitioners. Readers involved in intelligent systems, data analysis, Internet engineering, Computational Intelligence, and knowledge-based systems will benefit from the exposure to the subject matter. The book may also serve as a highly useful reference material for graduate students and senior undergraduate students.

"This book explores the key concepts of data mining and utilizing them on online social media platforms, offering valuable insight into data mining approaches for big data and sentiment analysis in online social media and covering many important security and other aspects and current trends"--

This book gathers high-quality papers presented at the First International Conference on Sustainable Technologies for Computational Intelligence (ICTSCI 2019), which was organized by Sri Balaji College of Engineering and Technology, Jaipur, Rajasthan, India, on March 29–30, 2019. It covers emerging topics in computational intelligence and effective strategies for its implementation in engineering applications.

A concise, handson guide with many practical examples and a detailed treatise on inference and social science research that will help you in mining data in the real world.Whether you are an undergraduate who wishes to get handson experience working with social data from the Web, a practitioner wishing to expand your competencies and learn unsupervised sentiment analysis, or you are simply interested in social data analysis, this book will prove to be an essential asset. No previous experience with R or statistics is required, though having knowledge of both will enrich your experience.

Social Media has transformed the ways in which individuals keep in touch with family and friends. Likewise, businesses have identified the profound opportunities present for customer engagement and understanding through the massive data available on social media channels, in addition to the customer reach of such sites. Social Media Listening and Monitoring for Business Applications explores research-based solutions for businesses of all types interested in an understanding of emerging concepts and technologies for engaging customers online. Providing insight into the currently available social media tools and practices for various business applications, this publication is an essential resource for business professionals, graduate-level students, technology developers, and researchers.