Social media is a goldmine of insight. Customers love to leave their opinions, whether negative or positive, which is a great source of feedback for businesses. But as social mentions grow, it becomes tedious, time-consuming, and costly to analyze and process all this data manually.
Fortunately, there are automated sentiment analysis tools that make it possible for you to extract insights from social media data in a scalable, reliable, and accurate way. With very little effort, you’ll be able to get an idea of what your customers like and dislike about your product or service, helping you make data-driven decisions.
In this post, we’ll explain why social media monitoring using sentiment analysis tools is important, how businesses are already using it, and how easy it is to get started with sentiment analysis.
Why Is It Important to Analyze Sentiment of Social Media?
Manually analyzing customer interactions is time-consuming, expensive, inaccurate, and prone to human error.
By automatically categorizing social media conversations as positive, negative, and neutral, businesses will not only gain valuable insights from their data, they will also improve their processes and overall performance. Some of the overall benefits include:
Data Analysis at Scale
Humans can only do so much. They grow tired when faced with repetitive and time-consuming tasks, which leads to inaccuracies and delays.
As your social interactions grow, you’ll need an automated solution that can process limitless amounts of data in a cost-effective, reliable, and timely manner. Automated sentiment analysis tools allow you to scale up – or down – easily. For example, businesses that experience high volumes of data at different times of year will be able to meet customer demands without having to worry about the cost of hiring new employees.
Social media interactions happen every second, and businesses need to keep on top of customer comments, 24/7 – especially when it comes to unhappy customers. Sentiment analysis tools can alert you to spikes in negative mentions in real time, every day of the year, so you can take action before negative sentiment escalates into a larger problem.
Consistent Analysis and Accurate Insights
Manually sorting text by sentiment can lead to inconsistencies. You see, humans are prone to error, especially when carrying out repetitive tasks. Not only that, they’re 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 sentiments, you can ensure that your model will deliver accurate insights.
Use Cases: Analyzing Sentiment of Social Media
Social listening tools, like sentiment analysis, are essential for your teams, to learn how customers feel about your brand. Businesses are already implementing sentiment analysis tools to analyze social media mentions and help them improve their overall customer experience. For example, they’re able to:
Monitor Brand Reputation Effectively
Analyzing social media interactions with sentiment analysis can help you understand how your customers view your brand. If customers feel negatively or positively about your brand, you can create action plans and set them in motion to address the situation, as necessary.
Respond to Customer Feedback Quickly
Social media is full of customer opinions. Recent statistics show that customers are more likely to post about a positive experience, but nearly 74% of customers will terminate a business relationship if they have a bad experience. Social listening, in the form of sentiment analysis, helps you learn how customers feel about your business, and impels you to take action.
Customer feedback might reveal problems in your customer service, for example, or collective discontent with your brand. Responding to ‘urgent’ feedback as soon you receive it could turn a bad situation into a positive outcome for both your business and customers.
Prioritize Negative Mentions
You can prioritize which of your customers’ comments to address first by detecting negative mentions.
For example, you might perform sentiment analysis on a set of tweets following marketing campaigns or product launches, to identify problem areas or unhappy customers. If you notice that negative comments often mention poor performance or slow interface, you can pass this feedback onto product teams to handle these issues as quickly as possible.
Prevent A Social Media Crisis
Customers love to voice their opinions on Twitter, Facebook, Reddit, etc. – and sometimes these opinions spread like wildfire, putting businesses in the spotlight. If you don’t pay attention to the negative emotions behind these opinions and address them quickly, you may end up with a PR nightmare on your hands.
Discover Customer Insights
Your customers speak directly to you on social media, telling you what they like and dislike about your product. 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.
Perform competitive 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 and discover if there’s an area, feature, or product you should improve. You can discover opportunities and see if you’re performing better than your competitors.
Start Using Sentiment Analysis in Social Media
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:
- Gather your data
- Prepare your data
- 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.
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.
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.
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