Hybrid Computational Intelligence

Challenges and Applications

Siddhartha Bhattacharyya, Vaclav Snasel, Deepak Gupta, Ashish Khanna

• Publisher: Academic Press
• Year: 2020
• ISBN:
  9780128187005
• Topic: Computer Science
• Subtopic: Computer Vision & Pattern Recognition
• Index: Computer Science

Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development.

Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems.

• Provides insights into the latest research trends in hybrid intelligent algorithms and architectures
• Focuses on the application of hybrid intelligent techniques for pattern mining and recognition, in big data analytics, and in human-computer interaction
• Features hybrid intelligent applications in biomedical engineering and healthcare informatics

Chapter 1

Application and techniques of opinion mining

Neha Gupta and Rashmi Agrawal, Faculty of Computer Applications, Manav Rachna International Institute of Research & Studies, Faridabad, India

Abstract

The study and analysis of human behavior using a computer modeling approach is known as opinion mining or sentiment analysis. Data mining, Web extraction, text mining, etc. are the key areas of opinion mining. Social media platforms are gaining popularity and are becoming essential components of most people’s lives. Various social networking websites, like Facebook, Twitter, and WhatsApp, are generating a huge amount of data and the mining of these data helps in discovering hidden and useful information with high potential. The calculation and evaluation of average inclinations to any opinion/sentiment toward any entity helps both the organization and the individual to get the right opinion about the ongoing trends or unfamiliar things.

Various computational intelligence techniques are also used to analyze the sentiments of users. In this chapter the authors cover the fundamental concepts of opinion mining and sentiment analysis. The chapter also includes various techniques of opinion mining, along with various tools used to analyze opinions. Some key areas related to feature extraction, ontologies, and deep learning have also been discussed. Toward the end of the chapter research and future directions, along with references, have been given for further study.

Keywords

Opinion mining; sentiment analysis; information retrieval; business intelligence; sentiment classification; textual analysis

1.1 Introduction

Various researchers have worked in the field of opinion mining—the initial research was carried out by Nasukawa and Dave in 2003. Because of the explosive growth of the World Wide Web and the use of various data-mining techniques, researchers are more inclined toward the analysis and mining of user sentiments. Opinion mining refers to the study of sentiments, opinions, attitudes, emotions, etc. that are analyzed and obtained from various written sources. We usually make a perception about a particular product or situation based on the beliefs and views of others. As people are very active on social media platforms and express their views using Facebook or Twitter, so organizations are working on analyzing the sentiments of people related to various issues to help them analyze the product/situation more accurately.

Nowadays, opinion mining is one of the most active research areas that include the concept of natural language processing (NLP) and data mining. Various opinion-mining tools, such as NLTK, WEKA, and Rapid miner, are used to mine the opinions of users. Opinion is mainly classified as positive or negative. NLP algorithms are used to track the mood of the public about a particular product. Opinion mining is widely used in various business applications to decide the utility of a particular product or a process based upon the sentiments/reviews of users.

1.2 Fundamentals of opinion mining

Over the last two decades, data-mining techniques in computer science have evolved significantly. The latest buzzword in this mining era is opinion mining, which has gone to a deeper level of understanding the behaviors of people in relation to particular events [1]. Opinion mining examines the feelings of people in a given situation by looking at opinions, emotions, or sentiments that are posted on social media. These opinions can be either positive or negative. Although a lot of research has been carried out by various researches on sentiment analysis, the term opinion mining was first introduced by Nasukawa and Dave in 2003. Since then research in this field has boomed at an exponential rate. The main reason for this growth is the expansion of the World Wide Web (www).

There are also various other factors that contribute to the ever-increasing demand of opinion mining and these factors are:

1. Evolution and expansion of various machine learning techniques to extract the information and to process any language.

2. Because of the dramatic growth of social networking sites and expansion of www, data sets used by machine learning algorithms can be easily trained.

People are now able to conveniently communicate with each other via various Internet channels that include email, Facebook, WhatsApp, LinkedIn, etc. This communication is happening because of the recent development of technology and social media and is generating a huge amount of data which are a gold mine to discovering hidden and useful information. In this chapter we use the terms “opinion mining” and “sentiment analysis” interchangeably. Let us first understand the meaning of two key terms: “opinion” and “sentiment.”

1.2.1 Defining opinion

Our decisions are dependent upon the opinions or sentiments of others. We are usually influenced by the opinions of others and take decisions accordingly. Opinion is a subjective statement as it describes the thinking or the beliefs of a person about a particular thing. Opinion can be defined as a judgment or a belief that lacks absolute conviction, certainty, or optimistic knowledge. It concludes that certain facts, ideas, etc. are likely to be true or are true.

Alternatively, “it is an estimation of the quality or worth of someone or something” [2].

1.2.2 Defining sentiments

Sentiments are central to almost all human activities [3] and act as key influencers of our behaviors.

Sentiments an individual has with objects in context are how they formulate an opinion. Sentiments are defined as “an attitude toward s...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• List of contributors
• Preface
• Chapter 1. Application and techniques of opinion mining
• Chapter 2. Influence of big data in smart tourism
• Chapter 3. Deep learning and its applications for content-based video retrieval
• Chapter 4. A computationally intelligent agent for detecting fake news using generative adversarial networks
• Chapter 5. Hybrid computational intelligence for healthcare and disease diagnosis
• Chapter 6. Application of hybrid computational intelligence in health care
• Chapter 7. Utility system for premature plant disease detection using machine learning
• Chapter 8. Artificial intelligence-based computational fluid dynamics approaches
• Chapter 9. Real-time video segmentation using a vague adaptive threshold
• Index