scholarly journals Sentiment Analysis on Myocardial Infarction Using Tweets Data

2019 ◽  
Vol 8 (S1) ◽  
pp. 10-14
Author(s):  
M. B. Monicka ◽  
A. Krishnaveni
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Disease Risk ◽  
Vital Role ◽  
Mining System ◽  
Machine Learning Approach ◽  
Rich Information ◽  
The Rich

In 2016, the survey reports that 1.7 Million people die of Myocardial Infarction (MI), due to less medication facilities, less prevention care and treatment planning is top most analysis of effective disease risk assessment, through this we have take prevention using sentiment analysis of recent advancements, the text analytics have opened up new potential of using the rich information of tweet analysis, to identify the relevant risk factors in MI. To tackle the MI risk factors tweet analysis gives more remedy and care factors by users, also this leads to decrease of MI in India. Our system plays a machine learning approach using sentiment analysis using tweet dataset. Nowadays people suffering from MI such as cardiac arrest, high blood pressure, congestive heart failure etc. Twitter is an excellent resource for the MI Patients since they connect people who have with similar conditions and experiences. It provides the knowledge sharing about MI, plays a vital role through Opinion Mining system.

scholarly journals Predictive Analysis using Convolution Network on Sentiment Analysis of Text Classification using Machine Learning

2021 ◽  
Vol 9 (2) ◽  
pp. 313-317
Author(s):  
Vanitha kakollu, Et. al.
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Test Accuracy ◽  
Data Sets ◽  
Negative Data ◽  
Term Extraction ◽  
Positive Data ◽  
Machine Learning Approach

Today we have large amounts of textual data to be processed and the procedure involved in classifying text is called natural language processing. The basic goal is to identify whether the text is positive or negative. This process is also called as opinion mining. In this paper, we consider three different data sets and perform sentiment analysis to find the test accuracy. We have three different cases- 1. If the text contains more positive data than negative data then the overall result leans towards positive. 2. If the text contains more negative data than positive data then the overall result leans towards negative. 3. In the final case the number or positive and negative data is nearly equal then we have a neutral output. For sentiment analysis we have several steps like term extraction, feature selection, sentiment classification etc. In this paper the key point of focus is on sentiment analysis by comparing the machine learning approach and lexicon-based approach and their respective accuracy loss graphs.

scholarly journals Sentiment Analysis for Twitter Data

2020 ◽  
Vol 9 (7S) ◽  
pp. 98-101
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Daily Basis ◽  
Support Vector ◽  
Twitter Data ◽  
Internet Users ◽  
Political Views ◽  
Large Numbers ◽  
Machine Learning Approach

With the advancements in web technology and its growth, there's an incredible volume of information present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on various topics, and can discussion with different groups and communities, or post messages across the world. In the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for sentiment analysis, that's helpful to research the info within the tweets,where opinions are heterogeneous, highly unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive Bayes & Support Vector Machine, we provide research on twitter data.

scholarly journals Public knowledge of cardiovascular disease and response to acute cardiac events in three municipalities in Brazil

Open Heart ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. e001322
Author(s):  
Emily S Bartlett ◽  
Luisa S Flor ◽  
Danielle Souto Medeiros ◽  
Danny V Colombara ◽  
Casey K Johanns ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Cardiovascular Disease ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cardiovascular Disease Risk Factors ◽  
Public Knowledge ◽  
Significant Heterogeneity ◽  
Coronary Syndrome ◽  
Eastern Brazil

ObjectiveTo conduct a landscape assessment of public knowledge of cardiovascular disease risk factors and acute myocardial infarction symptoms, cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) awareness and training in three underserved communities in Brazil.MethodsA cross-sectional, population-based survey of non-institutionalised adults age 30 or greater was conducted in three municipalities in Eastern Brazil. Data were analysed as survey-weighted percentages of the sampled populations.Results3035 surveys were completed. Overall, one-third of respondents was unable to identify at least one cardiovascular disease risk factor and 25% unable to identify at least one myocardial infarction symptom. A minority of respondents had received training in CPR or were able to identify an AED. Low levels of education and low socioeconomic status were consistent predictors of lower knowledge levels of cardiovascular disease risk factors, acute coronary syndrome symptoms and CPR and AED use.ConclusionsIn three municipalities in Eastern Brazil, overall public knowledge of cardiovascular disease risk factors and symptoms, as well as knowledge of appropriate CPR and AED use was low. Our findings indicate the need for interventions to improve public knowledge and response to acute cardiovascular events in Brazil as a first step towards improving health outcomes in this population. Significant heterogeneity in knowledge seen across sites and socioeconomic strata indicates a need to appropriately target such interventions.

scholarly journals COVID-19 and acute ST-elevation myocardial infarction (STEMI) and ventricular thrombus in a young ethiopian man without known cardiac risk factors: A case report

2021 ◽  
Vol 0 ◽  
pp. 1-3
Author(s):  
Dawit Kebede Huluka ◽  
Yidnekachew Asrat Birhan ◽  
Adane Petros Sikamo ◽  
Nebiyu Getachew ◽  
Amha Meshesha ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Distress Syndrome ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Cardiac Risk ◽  
Left Ventricular ◽  
St Elevation Myocardial Infarction ◽  
Ventricular Thrombus ◽  
St Elevation

Patients with coronavirus disease 2019 (COVID-19) can present with pneumonia and acute respiratory distress syndrome but rarely with acute myocardial infarction, especially in the absence of known cardiovascular disease risk factors. We present the case of a previously healthy young Ethiopian man with COVID-19 and no known cardiovascular risk factors who was diagnosed with acute ST-elevation myocardial infarction and left ventricular thrombus.

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