Let’s be honest, no matter what we do, we are controlled by our feelings somehow and it affects our relations, decisions, life and our future.
So feelings matter.
And it’s interesting to know that Sentiment analysis in twitter is one of the most important and yet challenging topics in data since, tweets from elections to financial markets or customer reviews of products and almost everything else are in twitter, and that makes it special!
But, how can Sentiment analysis be done in twitter? Where would we get the data?
And many more questions come to mind.
But don’t worry I’m here to tell you all in the most simplest way, so let’s go!
In this video of twitter analytics I’m gonna explain what sentiment analysis is, why is Twitter the best choice to start and how to run a sentiment analysis project in twitter, so let’s start.
What is twitter sentiment analysis?
- Opinion extraction
- Opinion mining
- Sentiment mining
- Subjective analysis or
- Sentiment analysis
As it is quite obvious from its words itself, it is the process of analyzing texts and trying to find out the emotions behind the words.
In sentiment analysis we will analyze a lot of words and we will try to create a model or use existing models to find out the sentiments behind words, sentences and so on.
Normally we have 3 categories of emotions:
- Positive (as +1)
- Negative (as -1)
- Neutral (as 0)
In sentiment analytics we classify each word in one of these categories and using artificial intelligence and machine learning algorithms we extract the general sentiment of each sentence or group of words.
Why sentiment analysis in twitter?!
Since sentiment analysis is actually analyzing and discovering the emotions hidden in words and Twitter’s main content is text, that’s why sentiment analysis projects in social media are focused more on twitter data.
Beside that , twitter is a popular social media and you can find a lot of tweets about almost anything.
10 Steps to execute sentiment analysis using python
- Choose an IDE and learn how to work with it
- Learn Python key concepts, at least just to be familiar with it
- Learn how to work with Pandas, Numpy and Matplotlib libraries a bit
- Learn about the theoretical process of machine learning algorithms especially neural networks
- Learn the theoretical process of sentiment analysis
- Find a labeled dataset
- Choose a machine learning algorithm
- Find a sample code
- Work with the sample code until you understand every little details
- Analize another dataset with same sample code