Question (on Quora) Is the R data science course from datacamp worth the money?
Answer:
It depends on your learning style.
If you like watching videos then coursera/udacity might be better.
If you like reading then a book/e-book might be better.
If you like hands-on then something like Data Camp is a great choice. I think they have monthly plans so it’s much cheaper to try them out. When I subscribed to it, it was like 30$/Month or so. I found it was worth it. Also, if you want to see if “hands-on” is how you learn best. Try this: swirl: Learn R, in R. — it’s free! Also, Data Camp has a free course on R too so you could try that as well.
Apart from this, I’ve used other Tools per project requirements and It’s been fun designing and developing projects on “Sentiment Analysis” primarily using Social Media Monitoring. Having worked with clients on projects that use “Sentiment Analysis” – I reflected about the role of Sentiment Analysis in Social Media Monitoring. And in this blog post, I am sharing these reflections:
What is Social Media Monitoring?
Social Media Monitoring is a process of “monitoring” conversations happening on social media channels about your brand/company.
Is it NEW? Not really. The idea of monitoring or gathering data about what is being talked about the brand/company is not new. Earlier, it was newspapers and magazine-articles and now, it’s the social media channels including online news, forums and blogs and thus the name given to this process is “Social Media Monitoring”
What is Sentiment Analysis?
Analyzing data to categorize it under a “sentiment” (emotion).
Example. Is this review saying positive, negative or neutral thing about our product.
side-note: Sentiment analysis is often categorized under “Big Data Analytics”.
What’s the Role of Sentiment Analysis in Social Media Monitoring?
We’ve seen that in social media monitoring, we gather all online conversations about a brand/product/company. Now wouldn’t it be great to take the data that we have and bucket it under “Positive”, “Negative” or “Neutral” categories for further analysis?
So few questions that can be answered after we have results from sentiment analysis:
1) Are people happy or sad about our product?
2) What do they like about our product?
3) What do they hate about our service?
4) Is there a trend or seasonality in sentiment data?
Among other business insights that may be not be easily answerable with just plain text data.
Thus sentiment analysis is one of the step in social media monitoring that assists in analyzing sentiment of all the conversations happening on the social web about a brand/product.
This is beginner’s guide to sentiment analysis using Python NLTK on windows. We’ll start w/ installing Python and NLTK and then see how to perform sentiment analysis.
Using the code I was able to run the Naive Bayes Classifier to categorize text:
Conclusion:
In this post, we learned how to perform sentiment analysis using Python on windwos platform. NLTK supports classifiers other than Naive Bayes, and also there are resources that will help you increase the accuracy of the classifier. And I hope that this post acts as a starting guide for you!
3. Create an Application on Google and get the APP ID.
Now, let’s sign up for a Google App Engine Account with a Google account you may have. Part of the process includes verification via a code sent to your mobile device – so be ready to provide your mobile number. And every Google Account gets to deploy 10 applications with a Google App Engine admin account.
Now, To sign up for Google APP engine. Go to: https://appengine.google.com/ and if you have a Google Apps account go to: https://appengine.google.com/a/<DOMAIN.COM>
Now, sign in with your Google account and you would be asked to verify your account:
Now, once you successfully verify your account, you would see something like:
And click on create application and fill in the following details. For now, fill in the App ID and the App Title. Leave other options as default for now. And yes, please check the availability of your app id and this will also be your URL. The URL will take the form.appspot.com
Scroll down and you will find a “create application”. please click on it. You will also see a message: “Application registered successfully”. Now, let’s do some coding!
4. Let’s write the Hello World Python app!
create a directory and name it anything you like. I named it “parasdoshipyapp”. But remember the location of the directory – you need it later.
Now,
create a helloworld.py file inside the directory you just created. (you can edit it via notepad or pythonwin). And here is the demo code:
In the first line, you add the app id (remember, we had created an application id in step 3)
And the last line is the file that has the code. In this case, the code is meant to print just couple of lines.
5. Test and upload on Google App Engine via Google App engine Launcher.
open Google App Engine Launcher:
Go to File –> create new application, and you will see:
Add the app id in the application name and the directory of your app. In my case, it is “parasdoshipyapp” and click on create.
Now let’s test it locally.
Click on “Run” to test it locally. Note the port no. In my case it is 8080 so I am going to go and open “http://localhost:8080/” on my browser:
It runs and so let’s deploy it on cloud (Google App Engine)
click on “deploy” and enter your Google Account credentials. Note the projects field is your “App ID”
Now we you will see the progress of the deploy. Let it do it’s processing and when it has successfully deployed your app, go to the URL and check out your site! Also explore the application dashboard.
