Abstract
The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.
Highlights
The Sentimental Analysis is a widely known and used technique in the natural language processing realm
In social media analytics, which is the focus on the present study, studies have demonstrated the possibility of using sentiment analysis (SA) through platforms like Microsoft Azure Machine learning; Amazon SageMaker and Amazon Machine Learning; and Google Cloud Machine Learning to analyse social media analytics
This study demonstrated that Microsoft Azure Machine Learning (ML) based on two Machine Learning (ML) algorithms: Logistic Regression, and Support Vector Machine (SVM) can be used to build sentiment analysis (SA) models used to perform data analytics
Summary
The Sentimental Analysis is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It encompasses studying peoples’ attitudes, feelings and opinions towards a product, an event or organization computationally (Kasture & Bhilare, 2017; Li & Wu, 2010; Thomas, et al, 2011). Sentiment analysis involves assessing a piece of writing intending to determine whether is neutral, negative or positive. It is often applied in several areas namely plagiarism checking; intellectual property, social media analytics, product reviews, and document/case classification. In social media analytics, which is the focus on the present study, studies have demonstrated the possibility of using sentiment analysis (SA) through platforms like Microsoft Azure Machine learning; Amazon SageMaker and Amazon Machine Learning; and Google Cloud Machine Learning to analyse social media analytics
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