该系列资源是Python疫情大数据分析，涉及网络爬虫、可视化分析、GIS地图、情感分析、舆情分析、主题挖掘、威胁情报溯源、知识图谱、预测预警及AI和NLP应用等。推荐大家结合作者CSDN博客阅读，武汉必胜、湖北必胜、中国必胜！

This repository do mainly 3 things: twitter data scrapping , data analysis, sentiment analysis and generation

Twitter Analysis on Top countries - Health Department regarding COVID19 virus update since the first case detected as on 31/12/19 in China. The Study includes, World Health Organisation twitter data, plus, UK, India, USA, and Australia's health department twitter account analysis.

Analysis of the COVID19 outbreak in Brazil mainly through epidemic and hospitalization models, by the Health Analytics and Prospera consulting business units of Funcional Heatlh Tech.

Soli1065/

This project aims to use the Hadoop framework to analyze unstructured data that we obtain from Twitter and perform sentiment and trend analysis using Hive on MapReduce and Spark on keyword “COVID19”. We then compare the Hive and Spark approaches to determine the best performance.

Coronavirus-covid19-stocks-analysis

No description available

The covid-19 Pandemic was one of a kind that took many lives. I embarked on a journey to get insights on Covid's impact on the

COVID-10 Analysis

🦠 Covid19 Visualization & Analysis is a community-driven Python project for exploring, visualizing, and analyzing global Covid-19 data. It features interactive notebooks, scripts, and guides to help users uncover trends, create impactful visualizations, and share insights for research, education, and public awareness.

Covid19 Data Analysis of year 2020-21 through excel

COVID19 Data Analysis Using Python -- Coursera Project

🌍 This repository hosts an in-depth analysis of COVID-19's impact across five key countries from Jan 2020 to Dec 2021. Through advanced data analysis and visualization, we aim to provide insights into how the pandemic evolved differently across these nations, shedding light on the effectiveness of various health measures and vaccination campaigns.

Asking important and interesting questions regarding Coronavirus outbreak and finding the answers using data.

Covid19 and Iowa Liquor Sales analysis at BigQuery using dbt, Airflow, Marquez, Google Cloud and other modern data stack tools

A data science analysis of the Covid-19 pandemic, caused by the novel coronavirus.

No description available

A visualization dashboard made for sentiment analysis of tweets of indians public in covid19 pandemic.

No description available

Sentiment Analysis of COVID-19 Vaccine-related Twitter Data

Map trajectory for new vs total cases for trend and inflection analysis

Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP (NLP-COVID19, EMNLP)

A dashboard for Real-time analysis of Covid-19 in India and a resource tracker eg. Oxygen, Ventilators, Remdesivir along with Live Vaccine Availablity Tracker.

Analysing the sentiment of people during each phase of COVID-19 Pandemic in India

This is a project that help us to cluster the emotions of different humans using the data of covid19 tweet. This dataset contain data of different peoples tweets and their emotion

ECCV 2022 Workshop: AI-enabled Medical Image Analysis – Digital Pathology & Radiology/COVID19 : An easy-to-understand and lightweight Transfer Learning-based solutions for COVID-19 diagnosis

I qt worked on corona virus tweet streams mam With hashtags #covid19,#indiafightscorona,#lockdown I did generate the dastset from the stream and procesed according to the working of deep learning algorithms work flow. I reframed my datset with 2 parameters-- tweets full text and sentiment score and worked on 4 algorithms mam. SET 1- DEEP LEARNING ALGOTITHMS: 1.CNN -(used 1csv with train_test_split method ) Accuracy-0.73368 2.LSTM- (used 2csv file seperate for trainingand testing) Training accuracy-0.9457,loss-0.1605 Testing accuracy-0.6557,loss-0.3442 3.FFNN-( used 2csv file seperate for trainingand testing) Training accuracy-0.28,loss-622.3 Testing accuracy-0.14893,loss-141.82 4.ANN with TFIDF Vectorizer(used 1 csv wth train_test_split) The different Ann epoches and models with different learning rate and different drop out value ,Training accuracy ranged btween 0.4752 to 0.6270 and the Validation accuracy ranged 0.2353 constantly On comparing the above 4 algorithms I came to a conclusiom with my understanding Sentiment analysis in tweets can be done efficiently in this order. CNN > LSTM > ANN > FFNN. SET 2-MACHINE LEARNING I did try with Linear Support vector Classifier --1 csv train_test_split method Training accuracy - 0.6666 Testing accuracy(f1score)-0.59471 And with Naive bayes classifier--1 csv train_test_split method Training accuracy - 0.64 Test accuracy -0.5486 SET 3- MODEL CLASSIFICATIONS: I compared my datasets efficiency with 4 models . The accuracies of the model classificatiom are: 1.Baseline Model - 62.86% 2.Reduces Model-65.71% 3.Regularized Model-66.86% 4.Dropout Model-67.43% Efficient modeling order for tweet data-set Dropout model > Regularized model > Reduced model > Baseline model .

Using the Python programming language, we were able to research and analyze the Twitter dataset to figure out the effects COVID-19 has had globally. Our goal is to dive deep into the use of coronavirus related hashtags, including their frequency of use, and to analyze the impact of positive, negative, sentimental and neutral tweets on social media regarding COVID-19. We will also view the activity level of COVID-19 related tweets from the verified accounts on social media since they have the ability to utilize their platform to spread awareness of social distancing and ‘stay-at-home’ measures. Utilizing both quantitative and qualitative methodologies with our research will help to guide further in-depth exploration of how to take advantage of recent advancements.

No description available