Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices

Abstract

Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of reviews of Amazon Alexa to predict customer sentiment. The proposed model can be directly applied to any company with an online presence to detect customer sentiment from their reviews automatically. This research aims to present a suitable method for analyzing the users’ reviews of Amazon Echo and categorizing them into positive or negative thoughts. A dataset containing reviews of 3150 users has been used in this research work. Initially, a word cloud of positive and negative reviews was plotted, which gave a lot of insight from the text data. After that, a deep learning model using a multinomial naive Bayesian classifier was built and trained using 80% of the dataset. Then the remaining 20% of the dataset was used to test the model. The proposed model gives 93% accuracy. The proposed model has also been compared with four models used in the same domain, outperforming three.

Revolutionizing Healthcare by Unleashing the Power of Machine Learning in Diagnosis and Treatment

“…Unlabeled data is easily accessible as compared to labeled data. Unknown or hidden insights can be found that are not possible with supervised learning techniques [17]. Labeling of data might cause a manual error, but, in this case, the chances get lowered [7].…”

“…Multiple stakeholders, beginning from the manufacturing of equipment and drugs to insurance providers regulate the price of healthcare [17]. Rising costs demoralize people in different aspects, such as undergoing laboratory tests and www.ijacsa.thesai.org regular visits to health practitioners, which affects patient health.…”

“…InnerEye implements ML and computer vision to identify anatomy and tumors separately by harnessing radiological pictures that guide health practitioners in surgical and radiation therapy [17]. The aim of Microsoft is to deliver drugs that are customized according to the requirements of each individual.…”

“…Discrimination in the machine learning model occurs when data is imported from a particular source, and the output that is obtained can only be beneficial for that particular source [17]. Suppose that data imported from an academic health science center is fed to the ML system.…”

Machine-Learning Algorithms for Process Condition Data-Based Inclusion Prediction in Continuous-Casting Process: A Case Study

“…Also based on fuzzy theories, Kaminska et al [ 21 ] proposed an improved KNN using ordered weighted average operators and obtained optimal results in applied aspect-based sentiment analysis. Kaushik et al [ 22 ] used a multinomial naive Bayesian Classifier for text-classification-based sentiment analysis. Erkan [ 23 ] reported an eigenvalue-based algorithm, using the eigenvalues of each sample in the test data concerning the training data to make classifications.…”