News-based sentiment analysis in real estate: A supervised machine learning approach with support vector networks

2018 ◽  
Author(s):  
Marcel Lang ◽  
Jessica Ruscheinsky ◽  
Jochen Hausler
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Sentiment Analysis ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

2020 ◽  
Vol 10 (16) ◽  
pp. 5673 ◽  
Author(s):  
Daniela Cardone ◽  
David Perpetuini ◽  
Chiara Filippini ◽  
Edoardo Spadolini ◽  
Lorenza Mancini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stress State ◽  
Thermal Imaging ◽  
Driving Simulator ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Thermal Features ◽  
Driver Stress

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

scholarly journals Sentiment Analysis on Social Media using Machine Learning Approach

2021 ◽  
Author(s):  
Erick Omuya ◽  
George Okeyo ◽  
Michael Kimwele
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

scholarly journals SENTIMENT ANALYSIS OF COVID-19 TWEETS

2021 ◽  
Vol 5 (1) ◽  
pp. 566-576
Author(s):  
Azeez A. Nureni ◽  
Victor E. Ogunlusi ◽  
Emmanuel Junior Uloko
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
Learning Approach ◽  
K Nearest Neighbors ◽  
Machine Learning Classification ◽  
Global Pandemic ◽  
Machine Learning Approach

Sentiment analysis involves techniques used in analyzing texts in order to identify the sentiment and emotion dominant in such texts and classify them accordingly. Techniques involved include but not limited to preprocessing of texts and the use a machine learning or lexical based approach in classifying these texts. In this research, attempt was made to adopt a machine learning approach to classify tweets on Covid-19 which is considered a global pandemic. To achieve this noble objective, a cross-dataset approach was applied to train four machine learning classification algorithms: Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB), as well as K-Nearest Neighbors algorithm (KNN). The final result will not only assist us in knowing the best performing algorithm, it will also assist in creating awareness on Covid-19 with the final objective of destigmatizing the patients through the analysis of sentiments and emotions on Covid-19  and finally use the same result for containing the spread of the pandemic

News-based sentiment analysis in real estate: a machine learning approach

2018 ◽  
Vol 35 (4) ◽  
pp. 344-371 ◽  
Author(s):  
Jochen Hausler ◽  
Jessica Ruscheinsky ◽  
Marcel Lang
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Sentiment Analysis ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Twitter Sentiment Analysis Using Different Machine Learning and Feature Extraction Techniques

2021 ◽  
Vol 24 (3) ◽  
pp. 50-54
Author(s):  
Mohammad W.Habib ◽  
◽  
Zainab N. Sultani ◽  
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
Extraction Methods ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Extraction Techniques ◽  
Machine Learning Approach ◽  
Negative Sentiment

Twitter is considered a significant source of exchanging information and opinion in today's business. Analysis of this data is critical and complex due to the size of the dataset. Sentiment Analysis is adopted to understand and analyze the sentiment of such data. In this paper, a Machine learning approach is employed for analyzing the data into positive or negative sentiment (opinion). Different arrangements of preprocessing techniques are applied to clean the tweets, and various feature extraction methods are used to extract and reduce the dimension of the tweets' feature vector. Sentiment140 dataset is used, and it consists of sentiment labels and tweets, so supervised machine learning models are used, specifically Logistic Regression, Naive Bayes, and Support Vector Machine. According to the experimental results, Logistic Regression was the best amongst other models with all feature extraction techniques.

A minimally-occlusive cuff method utilizing ultrasound vascular imaging for stress-free blood pressure measurement

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
K Uemura ◽  
T Nishikawa ◽  
T Kawada ◽  
M Sugimachi
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approach ◽  
Funding Source ◽  
Cuff Inflation ◽  
Time Calibration ◽  
Machine Learning Approach ◽  
Occlusive Cuff

Abstract Objective Occlusive cuff inflation in ambulatory blood pressure (BP) monitoring disturbs the daily life of the user, and affects efficacy of monitoring. To overcome this limitation, we have developed a novel minimally-occlusive cuff method for stress-free measurement of BP. This study aimed to experimentally evaluate the reliability of this method, and improve the precision of this method by implementing a machine learning algorithm. Methods In this method, a thin-plate-type ultrasound probe (Size: 5.6mm-thickness × 28mm × 26mm; weight: 10g) is placed between the cuff and the skin, and used to measure the ultrasonic dimension of the artery (Figure 1). The cuff pressure (Pc), arterial dimension at systole (Ds) and diastole (Dd), systolic BP (SBP) and diastolic BP (DBP) during cuff inflation are theoretically related by the following equations, SBP-Pc = P0·Exp[α·Ds] DBP-Pc = P0·Exp[α·Dd] Where P0 and α are constants, and α indicates arterial stiffness. Since multiple sets of the two equations can be defined over multiple cardiac beats while measuring Pc, Ds and Dd during mild cuff inflation (Pc is controlled less than 50 mmHg, Figure 1), it is possible to estimate SBP (SBPe) and DBP (DBPe) as solutions of the equations. In 6 anesthetized dogs, we attached the cuff and the probe to the right thigh to get SBPe and DBPe, which were one-time calibrated in each animal against reference SBP and DBP measured by using an intra-arterial catheter. We also determined the pulse arrival time (PAT), which is a commonly employed parameter in cuff-less BP monitoring. In all the dogs, BP was changed extensively by infusing noradrenaline or sodium nitroprusside. Results DBPe correlated tightly with DBP with a coefficient of determination (R2) of 0.85±0.08, and predicted DBP with error of 3.9±7.9 mmHg after one-time calibration (Figure 2). PAT correlated poorly with DBP (R2=0.49±0.17), and predicted DBP less accurately than this method. SBPe correlated well with SBP (R2=0.78±0.08) (Figure 3). However, even after one-time calibration, difference between SBPe and SBP was 2.6±18.9 mmHg, which was not acceptable. To improve the precision in SBP prediction, we used supervised machine learning approach with use of a support vector algorithm (Python, Scikit-learn), which regressed feature variables (SBPe, DBPe, Ds, Dd heart rate, and PAT) against teacher signal (reference SBP). The support vector algorithm, once trained, predicted SBP with acceptable accuracy with error of 0.7±6.9 mmHg (Figure 3). Conclusions This method reliably tracks BP changes without occlusive cuff inflation. Once calibrated, this method measures DBP accurately. With the aid of machine learning, precision in SBP prediction was greatly improved to an acceptable level. This method with machine learning approach has potential for stress-free BP measurement in ambulatory BP monitoring. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Japan Society for the Promotion of Science

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