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Sentiment Analysis Using Python: A Hands-on Guide

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18th Nov, 2019
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Sentiment Analysis Using Python: A Hands-on Guide

Imagine you run a multinational company, and you have lakhs of customers. 

You’ve recently launched a product, and you want to see what people think of it. What would you do?

You’d check your product reviews, but when the number of reviews is in thousands, it can get pretty hectic. 

That’s where you’d implement sentiment analysis. 

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What is sentiment analysis & why does it matter? And how is it used? We’ll answer these questions in this detailed article. 

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Read on to find out. 

What is the Sentiment Analysis?

Sentiment Analysis refers to the automated techniques which extract the opinions from a specific piece of text written in natural language.

In other words, sentiment analysis finds out whether the particular piece of text is positive, negative, or neutral. 

It’s part of artificial intelligence and machine learning, and it finds uses in many industries.  It is one of the interesting NLP applications for businesses. 

For example, suppose a tweet says ‘This man is garbage’ you’d want the machine to figure out that the tweet is negative. 

While you can quickly figure out whether a particular text is positive or not by reading it, when the number of contents to read is humongous, the task becomes challenging.

That’s why sentiment analysis is popular.

Read: Check out other interesting machine learning project ideas.

How does sentiment analysis work?

How would you figure out the sentiment of the following two sentences:

  • That tree is ugly
  • That flower smells nice

You’d do so by focusing on the keywords: ugly and nice. You know the first sentence is negative because it mentions ‘ugly.’ The same goes for the second sentence.

A machine similarly does sentiment analysis. You teach it how to spot positive and negative keywords, and it gets rid of the other words.

This might seem like a lot, but once you’ve created a sentiment analysis model, it won’t be difficult for you. 

Applications of Sentiment Analysis

As we mentioned earlier, sentiment analysis is prevalent in multiple industries. Here are some examples of its uses:

Political Campaigns

Having a grasp on public opinion is crucial for political parties. If a political party doesn’t know what the public thinks about a particular topic, it can end up making a colossal blunder.

Political parties must be aware of the general sentiment on different topics related to their constituencies.  

Political parties and campaign managers use sentiment analysis to find out the opinion of the general public on specific topics.

They use Twitter sentiment analysis for this purpose. They take the data from people’s tweets on a specific topic and analyze it to see whether the response was great or not. 

Twitter sentiment analysis can help political parties in planning out their campaigns and future strategies as well. It lets them understand the opinion of the general public efficiently. 

Customer Experience

Companies use sentiment analysis to check their customer reviews, as well. Many people don’t give a review directly and post their opinions on social media.

Through sentiment analysis, companies can check the reviews of a particular product as well as the opinion of their customers online to see whether they like it or not. 

Giving customers a great experience is vital for any company. That’s why enterprises employ different strategies to see how their customers perceive them and what their customers think of their products or services. 

After finding out the opinion of the customers, the organization can also figure out whether it needs to improve its product or not. 

If a product isn’t getting a positive response, the organization might stop selling it or improve it. All of this leads to enhancing the customer experience. 

Check out all trending Python tutorial concepts in 2024.

Competitive Analysis

Apart from finding out the customer’s perspective on your products and services, you can also find out their opinion on your competitor’s products through sentiment analysis. 

It helps companies in understanding what their competition is doing right and where their competition is making mistakes. This way, they can adapt themselves accordingly.

For example, if you find out that a specific product of your competitor is getting bad reviews because of a particular drawback, you can release a similar product without that drawback. 

Sentiment analysis is a powerful tool in this regard. 

Organizations of different industries, including automotive, manufacturing, hospitality, food, and many others, are using (or can use) this technology for this purpose. 

How to do Sentiment Analysis?

There are multiple ways of doing sentiment analysis python-based:

  • Using open-source libraries
  • Using an API

Both of them have their advantages. 

Using Open-Source Libraries

With open-source libraries, you have the independence of using whatever techniques you want to implement. However, they require a lot of resources because you might have to install some hardware too.

And they can get very complicated because you’ll need a lot of sentiment analysis python code. As you start from scratch, you will need a lot of data for training your model as well. 

You will also need to rely heavily on testing because you might come across a lot of errors. There’s a steep learning curve with open-source libraries as well. 

Using an API

Using a Saas API can seem a better option for those who don’t have many resources (a team of data scientists, hardware, etc.). Moreover, if you’re learning about machine learning and Python, then you should start with an API first.

Creating a sentiment analysis model with a Saas API is simple too. These APIs are made to simplify the task of creating and implementing a sentiment analysis model. 

So you won’t face much difficulty in starting with these products. There are many APIs you can use for this purpose. 

How to do Sentiment Analysis with APIs?

When you’re using a sentiment analysis API, you don’t have to write a lot of sentiment analysis python code. Some APIs let you perform sentiment analysis without any code, as well. 

Here are the steps you’ll need to follow with most APIs to perform sentiment analysis:

  • Create an account
  • Install the Python SDK (Make sure it JSON integration is enabled)
  • Write a specific set of code (code differs among APIs)
  • Copy/paste the text you need to analyze
  • See the results

Each API requires a different set of python code you might need to write, so you should check the API and its documentation thoroughly for this purpose. You can perform Twitter sentiment analysis with the help of APIs, as well. 

How to Create a Sentiment Analysis Model?

Now you know how to do sentiment analysis, but what if you want to automate it? 

Suppose you only want to perform sentiment analysis for product reviews, wouldn’t it be more efficient to automate the analysis?

For this purpose, you will need to create a sentiment analysis model. 

A sentiment analysis model can analyze similar texts and improve their performance regularly. It’s a great example of machine learning in real life. 

Once you’ve taught a model how to perform sentiment analysis properly, you won’t need to put in much effort later on. 

If you’re using an API, you’ll get some models to work with. However, you can also develop data models yourself for checking a specific kind of group of text.

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For example, if your sentiment analysis model can check hotel reviews, it won’t be able to analyze news articles effectively. 

Ready to do some Analysis?

Sentiment analysis is one of the many ways you can use Python and machine learning in the data world. From major corporations to small hotels, many are already using this powerful technology. If learning about Machine learning and AI excites you, check out our Machine learning certification course from IIIT-B and enjoy practical hands-on workshops, case studies, projects and more. 

Also, If you’re interested to learn more about Machine learning, check out IIIT-B & upGrad’s Executive PG Programme in Machine Learning & AI which is designed for working professionals and offers 450+ hours of rigorous training, 30+ case studies & assignments, IIIT-B Alumni status, 5+ practical hands-on capstone projects & job assistance with top firms.



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