Social Media Sentiment Analysis: A Social Listening Tool

Customers often leave their opinions on a whim, on social media platforms like Twitter, Facebook, and Instagram, providing businesses with a wealth of immediate customer feedback. 

Obviously, the more positive feedback you receive, the betterHowever, negative feedback is just as important for businesses. Recognizing customers’ pain points, and making decisions based on their feedback lets your customers know that you’re listening to them. 

But as social mentions grow, it’s hard for brands to constantly tune into how their customers are feeling. Manually sorting social media comments and analyzing them is tedious, time-consuming, and costly for businesses. Fortunately, there’s a powerful AI solution: sentiment analysis. 

In this post, we’ll explain what sentiment analysis is, why it’s important, and how businesses are already using it to analyze social media data. We’ll also show you how easy it is to get started with sentiment analysis tools like MonkeyLearn.

What Is Sentiment Analysis?

Sentiment analysis is a text classification task that automatically sorts data into negative, positive, and neutral.

Using natural language processing (NLP) and machine learning algorithms, sentiment analysis models are able to understand ambiguity in natural language and sort text in a similar way to humans – only a lot faster and using one set of criteria!

By plugging your social media data into sentiment analyzers, you’ll be able to perform social listening in a scalable, reliable, and accurate way. Helping you understand how customers feel about your brand, products, and services.

Why Is Social Sentiment Analysis Important?

Social interactions show no sign of slowing down, and you’ll need to turn towards AI solutions, like MonkeyLearn to keep on top of all your data. To demonstrate how important social media sentiment analysis is for businesses, let’s take a look at the ways in which it can shape your business for the better.

Customer Service That’s Always On

Brands can monitor their mentions in real time, so they’re able to address both negative and positive comments at the drop of a hat. Not only does this show your customers that you are paying close attention to their needs, but it also improves the customer experience. Nearly 74% of customers will terminate a business relationship if they have a bad experience

Responding to ‘urgent’ feedback or detecting spikes in negative comments on social media could help you turn a bad situation into a positive outcome for both your business and customers. 

Scaling Social Data Analysis

Sentiment analysis tools can process huge amounts of social data in a matter of seconds, allowing you to scale up – or down – easily. Even if you’re experiencing high volumes of data, due to seasonal fluctuations or a recent product release, you’ll be able to meet customer demands without having to worry about the cost of hiring new employees. 

Check out this tutorial on how to do sentiment analysis on Facebook data.

Real-Time Product Feedback 

Your customers speak directly to you on social media, telling you what they like and dislike about your product. When you launch a new product feature, campaign, or service, you can immediately identify any pitfalls. 

You might notice negative comments often mention poor performance or a slow interface. If you pass this feedback onto product teams, they can handle these issues as quickly as possible. On the other hand, customers might be praising your new feature. 

Either way, sentiment analysis can help you gain insights about features that customers love, areas of your business that need improving (customer support, pricing, user experience, etc.), or alert you to bugs/defects you need to fix.

Consistent Analysis and Accurate Insights

Manually sorting social data by sentiment can lead to inconsistencies. You see, humans are prone to error, especially when carrying out repetitive tasks. Not only that, but they’re also subjective. In contrast, automated, machine learning-based models are trained with one set of rules, which means that they apply the same set of criteria to each text. 

If you train your model to correctly understand the difference between negative and positive mentions in social media data, you can ensure that your model will deliver accurate insights.

Monitoring Competitors for Market Research

It’s always a good measure to keep an eye on what your competitors are doing – right and wrong. With sentiment analysis, you can compare how customers mention topics on social media platforms and discover if there’s an area, feature, or product you should improve. 

Analyzing competitors’ social media conversations might also lead to new opportunities. Could you provide customers with a better product or service that better suits customer needs, or is there a feature that customers love that you could implement in your product?

Social Media Sentiment Analysis Tools

Getting started with sentiment analysis is easy with tools like MonkeyLearn. Specializing in text analysis, MonkeyLearn offers various pre-trained machine learning models for sentiment analysis, intent detection, topic labeling, feature extraction, and more. 

You also have the option to train your own model using your data, via an intuitive user interface. Let’s take a look at how easy it is to analyze your social media conversations with a sentiment analysis tool, in just three simple steps:

  1. Gather your data
  2. Prepare your data
  3. Use a pre-trained sentiment analysis model or build your own

1. Data Gathering: Collecting Social Media Data

To collect data from social media platforms like Facebook, Twitter, Reddit, and YouTube , you can use web scraping tools, public data sets, and APIs. Let’s take a closer look:


This platform lets you connect your apps through an automated flow. Simply put, you can extract data from one app and send it to another by creating a zap (a rule system). For example, you might ‘instruct’ a zap to extract all tweets mentioning your company, then add a ‘rule’ that sends these tweets directly to MonkeyLearn for analysis. Check out this guide to using Zapier with MonkeyLearn.


This web automation software, or browser-based web crawler, offers an extraction automation tool that you can integrate with MonkeyLearn. For example, you can export data from Facebook to .CSV files, simplifying the upload of data to a sentiment analysis model.

Content Grabber

This web scraping tool extracts content from the website of your choice and saves it as structured data. With Content Grabber, you could extract data from Twitter and export it to a .CSV or Excel file, making it readily available for analysis.


Pattern is a GitHub web mining module for Python that includes tools for scraping, natural language processing, machine learning, network analysis, and visualization. It offers datasets for Facebook, Twitter, YouTube, and more.


Most social media platforms have readily available APIs to extract data. 

  • The Graph API is the primary way to get data into and out of Facebook
  • Twitter’s API enables users to access and interact with public Twitter data
  • The Python Reddit API Wrapper allows users to scrape data from subreddits, get comments from a specific post, and much more.

2. Data Preparing: Preprocessing and Cleaning

Once you have your social media data, you’ll need to prepare it. This involves transforming unstructured data into information that machines can understand so that they can yield accurate results.

Social media is unstructured and is often riddled with abbreviations, incorrect grammar, emojis, special characters, unidentifiable words, and so on. So, you’ll need to clean data to get rid of irrelevant words, duplicate text, blank spaces, and any other form of text that could skew your analysis. 

Take a look at our guide on how to prepare your data.

3. Using a Pre-trained Sentiment Analysis Model

With MonkeyLearn, it’s easy to analyze your data with a pre-trained sentiment analysis model or build your own.

Simply paste or upload text in a .CSV file and notice how sentiments are automatically assigned to your text, along with the level of confidence.

This powerful sentiment analysis model is a great way to start classifying your social media texts.

4. Building a Sentiment Analysis Model

For more accurate and relevant insights, we strongly recommend you build a tailor-made sentiment analysis model. By creating your own model, you can apply your own negative and positive sentiment criteria.

Before you begin, sign up to MonkeyLearn for free.

1. Choose Your Model

Go to MonkeyLearn’s dashboard, click on ‘Create Model’ and choose to build a Classifier.

2. Select Sentiment Analysis

From the list of three available classifiers, choose the Sentiment Analysis option. 

3. Upload Your Social Media Data

Once you’ve gathered and pre-processed/cleaned your data, use it to train your sentiment analysis model. MonkeyLearn gives you the option to upload Excel or CSV files, and import data from other sources. 

4. Train Your Sentiment Analysis Model

You can train your model by assigning sentiments to at least 12 texts, which is the minimum number of sample texts the model requires before making its own predictions. Keep in mind that the more samples you use to train your model, the more accurate it will be.

5. Test Your Sentiment Analysis Model

Test your model by entering a text and asking the model to classify it. This will show you how confident your model is at classifying texts on its own.

Remember, you can always enter more sample texts to improve the accuracy levels of your model, and boost its confidence when making predictions on its own. 

Get Started with Sentiment Analysis Today

Social media interactions are a valuable source of information. They let you know what your customers are thinking and how you can improve your business. 

By using sentiment analysis tools to make sense of unstructured data, you can turn comments, tweets, and posts into actionable insights that help you make important decisions. 

Performing sentiment analysis on social media data is straightforward with MonkeyLearn, whether you choose to use one of our pre-trained models or build your custom model. And if you get stuck on the way, we’re always here to help. 

Sign up to MonkeyLearn for free, and start analyzing your social media data in a matter of seconds.

Inés Roldós

Inés Roldós

Marketing @MonkeyLearn. Business Administration student.


Have something to say?

Text Analysis with Machine Learning

Turn tweets, emails, documents, webpages and more into actionable data. Automate
business processes and save hours of manual data processing.