Sentiment Analysis Through Machine Learning for the Support on Decision-Making in Job Interviews

2019 ◽  
pp. 202-213
Author(s):  
Julio Martínez Zárate ◽  
Sandra Mateus Santiago
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sentiment Analysis ◽  
Job Interviews

scholarly journals A Novel Deep Learning Based Sentiment Analysis of Movie using Hybrid CNN_SVM Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 12391-12394
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Business Strategy ◽  
Performance Metrics ◽  
Classification Models ◽  
Learning Models ◽  
Meaningful Information

Data flow in web is becoming high and vast, extracting useful and meaningful information from the same is especially significant. The extracted information can be utilized for enhanced decision making. The information provided by the end-users is normally in the form of comments with respect to different products and services. Sentiment analysis is effectively carried out in these kinds of compact review to give away the people’s opinion of any products. This analyzed data will be efficient to improve the business strategy. In our work the collected online movie reviews are analyzed by using machine learning sentiment classification models like Random Forest, Naive Bayes, KNN and SVM. The work has been extended with CNN and hybrid CNN-SVM deep learning models to achieve higher performance. Comparing the workings of all the above classification models for sentiment analysis based upon various performance metrics is the main objective of the paper.

scholarly journals Application of Twin Objective Function SVM in Sentiment Analysis

2020 ◽  
Author(s):  
Qiaoman Yang ◽  
Chunyu Liu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Objective Function ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Key Issues ◽  
Single Objective ◽  
Accuracy And Stability

Classification modeling is one of the key issues in sentiment analysis. Support vector machine (SVM) has been widely used in classification as an effective machine learning method. Generally, a common SVM is only for decision-making that sacrifices the distribution of data. In practice, sentiment data are big and mazy, which results in the deficiency of accuracy and stability when common SVM is used. The study investigates sentiment analysis by applying the twin objective function SVM, including nonparallel SVM(NPSVM) and twin SVM (TWSVM). From the experiments, we concluded that twin objective function SVMs are superior to NB and single objective function SVM in accuracy and stability.

Sentiment Analysis for Decision-Making Using Machine Learning Algorithms

Data Science ◽  
2019 ◽  
pp. 285-304
Author(s):  
Mohamed Alloghani ◽  
Thar Baker ◽  
Abir Hussain ◽  
Mohammed Al-Khafajiy ◽  
Mohammed Khalaf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Sentiment Analysis for Social Networks Using Machine Learning Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 473
Author(s):  
Dorababu Sudarsa ◽  
Siva Kumar.P ◽  
L Jagajeevan Rao
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Vast Amount ◽  
Learning Techniques ◽  
Precision And Accuracy

The tremendous of the overall enormous net has conveyed a present day way of communicating the feelings of individuals. It's additionally a medium with a vast amount of data in which clients can see the assessment of different clients which can be ordered into exceptional entailment summons and are progressively more boom as a key component in decision making. This paper adds to the supposition assessment for customers assessment class that is utilized to analyze the records inside the type of the assortment of tweets wherein investigates are very unstructured and are both high fine or terrible, or somewhere in the middle of these . For this we first pre-prepared the dataset, after that extract the adjective from the dataset that has a couple of significance this is alluded to as capacity vector, at that point decided on the component vector posting and from that point accomplished device examining based write calculations particularly navie bayes, most entropy and svm along the edge of the semantic introduction based absolutely based on word net which extracts synonyms and similarity for the content characteristic. In the end, we measured the performance of the classifier in terms of considering, precision and accuracy. 

scholarly journals Word Embedding and Feature Reduction for Sentiment Analysis Using GA

2020 ◽  
pp. 111-117
Author(s):  
Prajakta P. Shelke ◽  
Ankita N. Korde
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Sentiment Analysis ◽  
Recommendation System ◽  
Opinion Mining ◽  
Hybrid Approach ◽  
Feature Reduction ◽  
Word Embedding ◽  
Support Vector ◽  
Svm Classifier

Sentiment analysis (SA), also called as opinion mining is the technique for the removal of opinions of a specific entity or feature from reviews dataset. The opinions of other users help in decision making process of people. This paper studies different methods that are aimed at SA. These approaches vary from semantic based methods, machine learning, neural networks, syntactical methods with each having its own strength. Although hybrid approach also exists where the idea is to combine strengths of two or more methods to increase the accuracy. A framework in which sentiment analysis is done by using word embedding and feature reduction techniques is also proposed. Word embedding is a technique in which low-dimensional vector representation of words is provided. Feature reduction method is used with Support Vector Machine (SVM) classifier. The framework will perform sentiment analysis of user opinions by using a machine learning approach and provides a recommendation system for the ease of decision making for users. The proposed system in this paper has solved the scalability problem and improved the accuracy.

scholarly journals DevOps Project Management Tools for Sprint Planning, Estimation and Execution Maturity

2020 ◽  
Vol 20 (2) ◽  
pp. 79-92
Author(s):  
Jayasri Angara ◽  
Srinivas Prasad ◽  
Gutta Sridevi
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Project Management ◽  
Sentiment Analysis ◽  
Real Time ◽  
Rapid Development ◽  
Estimation Technique ◽  
Management Tools ◽  
Project Culture ◽  
Project Level

AbstractThe goal of DevOps is to cut down the project timelines, increase the productivity, and manage rapid development-deployment cycles without impacting business and quality. It requires efficient sprint management. The objective of this paper is to develop different sprint level project management tools for quick project level Go/No-Go decision making (using real-time projects data and machine learning), sprint estimation technique (gamified-consensus based), statistical understanding of overall project management maturity, project sentiment & perception. An attempt is made to device a model to calibrate the perception or the tone of a project culture using sentiment analysis.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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