Vern  Greenholt

Vern Greenholt

1594448520

Twitter Sentiment Analysis on Novel Coronavirus (COVID-19)

Since the blow-up of conspiracy theories around coronavirus, social media platforms like Facebook, Twitter, and Instagram have been actively working on scrutinizing and fact-checking to fight against misinformation. As more reliable sources get amplified, Twitter becomes more supportive than it was during the early stage of the outbreak. I figured it would be more interesting to hear the real public voice and discover the true sentiment regarding the coronavirus.

Don’t get intimidated by the word “scraping.” If you can browse the web page, you are able to perform web scraping like a pro, even if you are a newbie. So bear with me.

The easiest way to find out the attitude is by collecting all tweets containing the word of coronavirus. I even narrow down the research scope by setting the language as English and Terrain within the United States. This will ensure the sample data sets stay consistent with the search topic and increase the accuracy of the prediction.

After the research scope is settled, we can now start scraping. I prefer using Octoparsewhen it comes to picking the best web scraping tool, it has auto-detecting features which saves me a lot of time on hand-picking and selecting the data.

Twitter is more dynamic as it has infinite scrolling, meaning tweets are showing up once we keep scrolling down the page. In order to get as many tweets as possible, I build a loop list to maintain the scrolling action while fetching the information. This ensures the scraping workflow stays consistent without interruption.

Next, I create an extraction action. Octoparse renders the web page as we input the search URLs. It will break down the web page structure into sub-component so I can click on the target element easily to set up a command and tell the robot — go get the information for me. As I click one of the tweets, the tips panel pops up suggesting to select the sub-elements.

Image for post

There it is! A corresponding event is added to the workflow automatically. It also finds other tweets. Follow the tips guide, and click the “Select All” command. The final workflow should be like this:

Image for post

Octoparse workflow

The logic is simple: the scraper will first visit the page. Then it starts extracting the tweets until it finishes all the tweets inside the loop. It will repeat the scrolling action to locate another set of tweets and continue the extraction again until all the information is extracted successfully.

#coronavirus #web-scraping #sentiment-analysis #data analysis

What is GEEK

Buddha Community

Twitter Sentiment Analysis on Novel Coronavirus (COVID-19)
Vern  Greenholt

Vern Greenholt

1594448520

Twitter Sentiment Analysis on Novel Coronavirus (COVID-19)

Since the blow-up of conspiracy theories around coronavirus, social media platforms like Facebook, Twitter, and Instagram have been actively working on scrutinizing and fact-checking to fight against misinformation. As more reliable sources get amplified, Twitter becomes more supportive than it was during the early stage of the outbreak. I figured it would be more interesting to hear the real public voice and discover the true sentiment regarding the coronavirus.

Don’t get intimidated by the word “scraping.” If you can browse the web page, you are able to perform web scraping like a pro, even if you are a newbie. So bear with me.

The easiest way to find out the attitude is by collecting all tweets containing the word of coronavirus. I even narrow down the research scope by setting the language as English and Terrain within the United States. This will ensure the sample data sets stay consistent with the search topic and increase the accuracy of the prediction.

After the research scope is settled, we can now start scraping. I prefer using Octoparsewhen it comes to picking the best web scraping tool, it has auto-detecting features which saves me a lot of time on hand-picking and selecting the data.

Twitter is more dynamic as it has infinite scrolling, meaning tweets are showing up once we keep scrolling down the page. In order to get as many tweets as possible, I build a loop list to maintain the scrolling action while fetching the information. This ensures the scraping workflow stays consistent without interruption.

Next, I create an extraction action. Octoparse renders the web page as we input the search URLs. It will break down the web page structure into sub-component so I can click on the target element easily to set up a command and tell the robot — go get the information for me. As I click one of the tweets, the tips panel pops up suggesting to select the sub-elements.

Image for post

There it is! A corresponding event is added to the workflow automatically. It also finds other tweets. Follow the tips guide, and click the “Select All” command. The final workflow should be like this:

Image for post

Octoparse workflow

The logic is simple: the scraper will first visit the page. Then it starts extracting the tweets until it finishes all the tweets inside the loop. It will repeat the scrolling action to locate another set of tweets and continue the extraction again until all the information is extracted successfully.

#coronavirus #web-scraping #sentiment-analysis #data analysis

Data Scientist Creates Python Script To Track Available Slots For Covid Vaccinations

Bhavesh Bhatt, Data Scientist from Fractal Analytics posted that he has created a Python script that checks the available slots for Covid-19 vaccination centres from CoWIN API in India. He has also shared the GitHub link to the script.

The YouTube content creator posted, “Tracking available slots for Covid-19 Vaccination Centers in India on the CoWIN website can be a bit strenuous.” “I have created a Python script which checks the available slots for Covid-19 vaccination centres from CoWIN API in India. I also plan to add features in this script of booking a slot using the API directly,” he added.

We asked Bhatt how did the idea come to fruition, he said, “Registration for Covid vaccines for those above 18 started on 28th of April. When I was going through the CoWIN website – https://www.cowin.gov.in/home, I found it hard to navigate and find empty slots across different pin codes near my residence. On the site itself, I discovered public APIs shared by the government [https://apisetu.gov.in/public/marketplace/api/cowin] so I decided to play around with it and that’s how I came up with the script.”

Talking about the Python script, Bhatt mentioned that he used just 2 simple python libraries to create the Python script, which is datetime and requests. The first part of the code helps the end-user to discover a unique district_id. “Once he has the district_id, he has to input the data range for which he wants to check availability which is where the 2nd part of the script comes in handy,” Bhatt added.

#news #covid centre #covid news #covid news india #covid python #covid tracing #covid tracker #covid vaccine #covid-19 news #data scientist #python #python script

Aketch  Rachel

Aketch Rachel

1618099140

How Is TCS Helping With COVID-19 Testing In India

COVID-19 cases have only been on the rise. With the non-availability of effective drugs and vaccines, one of the effective ways to control it is to detect it early in patients. However, the task is easier said than done. While a large number of test kits are being produced, they are not enough to conduct testing in large numbers.

Government-run body, C-CAMP or Centre for Cellular and Molecular Platform, has been a key enabler in driving COVID-19 testing as it has been aggressively building, managing and scaling the ecosystem of MSMEs to produce test kits indigenously. However, they might not be enough.

#opinions #c-camp #c-camp tcs #covid-19 #covid-19 testing #tcs #tcs covid-19

Sofia  Maggio

Sofia Maggio

1626077565

Sentiment Analysis in Python using Machine Learning

Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?

We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.

But why do we need sentiment analysis?

Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.

It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.

Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.

Lastly, they are also used for spam filtering and removing unwanted content.

How does sentiment analysis work?

NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.

NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.

Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.

Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are

  • Simple classification of text into positive, negative or neutral. It may also advance into fine grained answers like very positive or moderately positive.
  • Aspect-based sentiment analysis- where we figure out the sentiment along with a specific aspect it is related to. Like identifying sentiments regarding various aspects or parts of a car in user reviews, identifying what feature or aspect was appreciated or disliked.
  • The sentiment along with an action associated with it. Like mails written to customer support. Understanding if it is a query or complaint or suggestion etc

Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.

  • Matching rules based sentiment analysis: There is a predefined list of words for each type of sentiment needed and then the text or document is matched with the lists. The algorithm then determines which type of words or which sentiment is more prevalent in it.
  • This type of rule based sentiment analysis is easy to implement, but lacks flexibility and does not account for context.
  • Automatic sentiment analysis: They are mostly based on supervised machine learning algorithms and are actually very useful in understanding complicated texts. Algorithms in this category include support vector machine, linear regression, rnn, and its types. This is what we are gonna explore and learn more about.

In this machine learning project, we will use recurrent neural network for sentiment analysis in python.

#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis

Abigail  Cassin

Abigail Cassin

1596574500

How The New AI Model For Rapid COVID-19 Screening Works?

With the current pandemic spreading like wildfire, the requirement for a faster diagnosis can not be more critical than now. As a matter of fact, the traditional real-time polymerase chain reaction testing (RT-PCR) using the nose and throat swab has not only been termed to have limited sensitivity but also time-consuming for operational reasons. Thus, to expedite the process of COVID-19 diagnosis, researchers from the University of Oxford developed two early-detection AI models leveraging the routine data collected from clinical reports.

In a recent paper, the Oxford researchers revealed the two AI models and highlighted its effectiveness in screening the virus in patients coming for checkups to the hospital — for an emergency checkup or for admitting in the hospital. To validate these real-time prediction models, researchers used primary clinical data, including lab tests of the patients, their vital signs and their blood reports.

Led by a team of doctors — including Dr Andrew Soltan, an NIHR Academic Clinical Fellow at the John Radcliffe Hospital, Professor David Clifton from Oxford’s Institute of Biomedical Engineering, and Professor David Eyre from the Oxford Big Data Institute — the research initiated with developing ML algorithms trained on COVID-19 data and pre-COVID-19 controls to identify the differences. The study has been aimed to determine the level of risk a patient can have to have COVID-19.

#opinions #covid screening #covid-19 news #covid-19 screening test #detecting covid