Experimental investigation of automated system for twitter sentiment analysis to predict the public emotions using machine learning algorithms

2020 ◽  
Author(s):  
Priti Sharma ◽  
A.K. Sharma
Keyword(s):  
Machine Learning ◽  
Experimental Investigation ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Automated System ◽  
The Public

scholarly journals Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 1612-1618
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
The Public ◽  
Social Media Platforms ◽  
Day By Day

Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

A Surveillance on Machine Learning Algorithms and Its Applications

2020 ◽  
Vol 17 (9) ◽  
pp. 4294-4298
Author(s):  
B. R. Sunil Kumar ◽  
B. S. Siddhartha ◽  
S. N. Shwetha ◽  
K. Arpitha
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Sentiment Analysis ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Pros And Cons ◽  
Primary Advantage

This paper intends to use distinct machine learning algorithms and exploring its multi-features. The primary advantage of machine learning is, a machine learning algorithm can predict its work automatically by learning what to do with information. This paper reveals the concept of machine learning and its algorithms which can be used for different applications such as health care, sentiment analysis and many more. Sometimes the programmers will get confused which algorithm to apply for their applications. This paper provides an idea related to the algorithm used on the basis of how accurately it fits. Based on the collected data, one of the algorithms can be selected based upon its pros and cons. By considering the data set, the base model is developed, trained and tested. Then the trained model is ready for prediction and can be deployed on the basis of feasibility.

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