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What is LSA

Building a Brand Image Through Electronic Customer Relationship Management
Latent semantic analysis.
Published in Chapter:
Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics
Salma Abdulaziz Alquhtani (King Khalid University, Saudi Arabia) and M. Anandhavalli Muniasamy (King Khalid University, Saudi Arabia)
DOI: 10.4018/978-1-6684-5386-5.ch003
Abstract
Analysis of customer relationships based on their satisfaction is a practical and motivating success factor for the growth of every company. Web intelligence describes the scientific development that uses information technology and artificial intelligence for new frameworks, services, and products provided by the web. This chapter aims to present the model of analyzing the users' sentiments from their online reviews on an e-commerce platform using machine-learning classifiers, namely naive bayes, logistic regression, support vector machine, and neural network. For data analysis, latent semantic analysis has been applied to examine the most frequent words used in online reviews. Finally, customers' interest in online shopping analysis has been performed to classify the customers' sentiments from their posted reviews on the e-commerce platform. In addition, the authors compared the performance results of these classifiers on the e-commerce dataset. The results reveal that the naive bayes classifier has performed better than all the other three classifiers.
Full Text Chapter Download: US $37.50 Add to Cart
More Results
Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine
LSA stands for Latent Semantic Analysis which is famous neural language processing techniques that is widely being used to find relationship between the words. It works on the fact that if words are similar in meaning it means they can be correlated.
Full Text Chapter Download: US $37.50 Add to Cart
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