Case Study

Recruitment process takes place based on needed data while certain limiting factors are ignored. The objective of the chapter is to recruit best employees while taking care of limiting factors from the cluster for resource management and scheduling. Various parameters of the recruits have been selected to find the maximum score achieved by them. Recruitment process makes a database as cluster in the software environment perform the information retrieval on the database and then perform data mining using genetic algorithm while taking care of the positive values in contrast to limiting values received from the database. A bigger level recruitment process finds required values of a person, so negative points are ignored earlier in the recruitment process because there is no direct way to compare them. Genetic algorithm will create output in the form of chromosomal form. Again, apply information retrieval to get actual output. Major application of this process is that it will improve the selection process of candidates to a higher level of perfection in less time.

Sentimental Analysis Tools

Opinion mining, also known as sentimental analysis, is the analysis of sentiment (emotion, affection, experience) towards the target object. In the present era, everyone is interested to know the opinions of others before making a decision or performing a task. Hence, it is necessary to collect the information (features) from relatives, friends, or web. These opinions or feedbacks help them to decide their action. With the advent of social media and use of digital technologies, web is a huge resource for data. However, it is time-consuming to read the data collected from the web and analyze it to arrive at informed decisions. This chapter provides complete overview of tools to simplify the operations of opinion mining like data collection, data cleaning, and visualization of predicted sentiment.

Ontology-Based Opinion Mining for Online Product Reviews

In this chapter, the authors work at the feature level opinion mining and make a user-centric selection of each feature. Then they preprocess the data using techniques like sentence splitting, stemming, and many more. Ontology plays an important role in annotating documents with metadata, improving the performance of information extraction and reasoning, and making data interoperable between different applications. In order to build ontology in the method, the authors use (product) domain ontology, ConceptNet, and word net databases. They discuss the current approaches being used for the same by an extensive literature survey. In addition, an approach used for ontology-based mining is proposed and exploited using a product as a case study. This is supported by implementation. The chapter concludes with results and discussion.

Ontologies, Repository, and Information Mining in Component-Based Software Engineering Environment

This chapter reviews some ontologies, tools, and editors used in building and maintaining the ontology from those reported in the literature, and the main focus is on the interoperability between them. The essential thing while developing an ontology or using an ontology from world web are tools. Through tools, ontology can either be developed or aligned in a manner that the researcher wants and given direction in term of opinion from the source files as meta data. This chapter presents various editors for building the ontology and various tools for matching between the two ontologies and conclusion based on the repository extracted as from the data in term of mining results. Comparison of various ontologies, tools, and editors are also there in order for the ease of user to access a particular ontology tool for selection of data in term of repository or components from the enormous data.

Opinion Mining

The machine learning is the emerging research domain, from which number of emerging trends are available, among them opinion mining is the one technology attraction through which the we could get analysis of the interested domain or we can say about the review from the customer towards any product or we can say any upcoming trending information. These two are the emerging words and we can say it's the buzz word in the information technology. As you will see that its widely use by the corporate sector to uplift the business next level. Before two decade you will not read any words e.g., Opinion mining or Sentiment analysis, but in the last two decade these words have given a new life to information technology domain as well as to the business. The important question which runs in the mind is why use sentiment analysis or opinion mining. The information technology has given number of new programming languages, new innovation and within that the data mining has given this trends to the users. The chapter is covering the three major concept's which comes under the machine learning e.g., Decision tree, Bayesian network and Support vector machine. The chapter is describing the basic inputs, and how it helps in supporting stakeholders by adopting these technologies.

Ontology-Based Opinion Mining

E-commerce business is very popular as a large amount of data is available on the internet in the form of unstructured data. To find new market trends and insight, it is very important for an organization to track the customers' opinions/reviews on a regular basis. Reviews available on the internet are very scattered and heterogeneous (i.e., structured as well as unstructured form of data). A good decision is always based on the quality of information within a specified period of time. Ontology is an explicit detailed study of concepts. The word ontology is borrowed from philosophy. It can also be defined as systematic maintenance of information about the things which already exist. In computer science, it could be said that it is a formal representation of knowledge with the help of a fixed set of believed concepts and the relationship between those concepts.

Feature Based Opinion Mining

Opinion mining is the estimated learning of user's beliefs, evaluation and sentiments about units, actions and its features. This method has several features matched with data mining techniques, language processing methods and feature oriented data abstraction. This seems to be extremely difficult to mine opinions from analysis those exist in common human used language. Views are very essentials when one desires to construct a judgment. Data abstraction is an important characteristic for decision making applicable to individuals and organization of different nature. While selecting and purchasing a particular product, it is always beneficial for an individual to collect other views for correct decision making. One association wants to conduct surveys and gather opinions to develop their product excellence. Internet as a source of information, having a number of websites available with the customer reviews as a number of products, it is easy to extract the features from these opinions, sentiments and view, is a task comes under feature-based opinion mining.

Open Issues in Opinion Mining

Opinions are found everywhere. In web forums like social networking websites, e-commerce sites, etc., rich user-generated content is available in large volume. Web 2.0 has made rich information easily accessible. Manual insight extraction of information from these platforms is a cumbersome task. Deriving insight from such available information is known as opinion mining (OM). Opinion mining is not a single-stage process. Text mining and natural language processing (NLP) is used to obtain information from such data. In NLP, content from the text corpus is pre-processed, opinion word is extracted, and analysis of those words is done to get the opinion. The volume of web content is increasing every day. There is a demand for more ingenious techniques, which remains a challenge in opinion mining. The efficiency of opinion mining systems has not reached the satisfactory level because of the issues in various stages of opinion mining. This chapter will explain the various research issues and challenges present in each stage of opinion mining.

Applications of Ontology-Based Opinion Mining

Ontology provides a technique to formulate and present queries to databases either stand-alone or web-based. Ontology has been conceived to produce reusable queries to extract rules matching them, and hence, it saves time and effort in creating new ontology-based queries. Ontology can be incorporated in the machine learning process, which hierarchically defines the relationship between concepts, axioms, and terms in the domain. Ontology rule mining has been found to be efficient as compared to other well-known rule mining methods like taxonomy and decision trees. In this chapter, the authors carry out a detailed survey about ontology-related information comprising classification, creation, learning, reuse, and application. The authors also discuss the reusability and the tools used for reusing ontology. Ontology has a life cycle of its own similar to the software development life cycle. The classification-supervised machine learning technique and clustering and the unsupervised machine learning are supported by the ontology. The authors also discuss some of the open issues in creation and application of ontology.

Fundamentals of Opinion Mining

Mining techniques in computer science have been evolving for the last two decades. Opinion mining is the latest buzzword in this evolution and goes to a deeper level to understand the drive behind people's behavior. Due to the richness of social media opinions, emotions, and sentiments, opinion mining examines that how people feel about a given situation, be it positive or negative. This chapter primarily focuses on explaining the fundamentals of opinion mining along with sentiment analysis. It covers the brief evolution in mining techniques in the last decade. The chapter elaborates on the significance of opinion mining in today's scenario and its features. It also includes a section to discuss the applications, challenges and research scope in opinion mining.