Sentiment analysis of electronic word of mouth (E-WoM) on e-learning.

The proliferation of social media and the internet has given people many opportunities to air their views and to be at liberty to say what they feel without hindrance. This is beneficial to commercial organizations and the general well-being of the populace. However, the cost of this freedom is that spamming is practiced with little or no control. This chapter focuses on the electronic word of mouth (eWOM) of opinion holders and the sentiments expressed in eWOM. One of the areas of life impacted by sentiment is electronic learning because it has become a prevalent mode of learning. The study aims to analyze eWOM on e-learning which can help in identifying learners' sentiments. Findings from three thousand tweets show more neutral sentiments, followed by positive sentiments. Suggestions and recommendations as well as the future directions for sentiment analysis of eWOM on e-learning are also discussed in this chapter

Open, Flexible and Distributed e-Learning Environments

What generate an effective e-learning environment to course writers may differ to a developer but the major consideration is to the prospects because they are the receiver.  An e-learning system is meaningful to learners when it is easily accessible,well-designed, learnercentred, affordable and efficient; flexible and has a facilitated learning environment. When learners display a high level of participation, involvement and tremendous output in meeting a course’sgoals and objectives, this can makee-learningmeaningful to instructors.Therefore, when learners enjoy all availablesupportservices provided in the course without any interruptions, it makes supportservices staff happyas they strive to provide easy-to-use, reliable and accessible services.This paper examines components and features that can make e-learning as competitive as face-to-face learning whereby students derive equal benefits as when undergoing face-to-face learning.

Neural Network Models: A Basic Statistical Tool for Data analysis and Forecasting

Neural network model is a model of brain’s cognitive process. Neural network originated as a model of how the brain works and has its beginnings in psychology. Today, neural network models are used to solve numerous problems associated with forecasting, pattern recognition, classification, manufacturing, medical diagnostic, signal processing, system control, checking, and modelling among others. This paper reveals the processes involved in using a neural network model in forecasting future events by effectively and efficiently utilizing extracted rules from trained neural networks and domain knowledge of the applied area. The structural method of operations and rules of extraction of neural networks were also discussed.

Model-based Information Retrieval System

Information retrieval System (IRS) is often defined as the location and delivery of documents to a user to satisfy their information needs. IR is the area of study concerned with searching for documents, for information within documents, and for metadata about documents, as well as that of searching structured storage, relational databases, and the worldwide web. There is an overlap in the usage of the terms data retrieval, document retrieval, information retrieval, and text retrieval. IR is interdisciplinary; based on computer science, mathematics, library science, information science, information architecture, cognitive psychology, linguistics, statistics and law. Automated information retrieval systems are used to reduce what has been called information overload. Many universities and public libraries use IR system to provide access to books, journals and other documents. Web search engines are the most visible IR applications. This article is the design and implementation of information retrieval system that deals with this aspect of the institution’s administration by providing an easy to use computerized application to assist in retrieving information