How to Use No-Code AI for Twitter Sentiment Analysis — and Why You Need It

How to Use No-Code AI for Twitter Sentiment Analysis — and Why You Need It

Patricia Orza

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Brands frequently use social media as a tool to reach out to new customers and build brand awareness. You can gain a lot from positive comments that inspire new customers to purchase your products.

However, there’s another side to that coin—negative posts and comments can cost you a lot. What’s more, they can bring enormous PR crises that are difficult to recover from. In fact, 86% of customers are hesitant to buy from a company that has received unfavorable feedback.

To avoid crises and prevent problems from escalating, try sentiment analysis. With sentiment analysis, you can analyze the emotions of a particular text, such as social media posts. This can help you discover what the brand image of your company really looks like.

In this article, we’re focusing on Twitter sentiment analysis.

We’ll explain how to do Twitter sentiment analysis—both the hard way and the easy way. We'll also take a closer look at why do TSA at all.

We’ll also include a real-world sentiment analysis example to help ensure you get a full understanding of how to do TSA. Let’s get to it.

What is sentiment analysis?

Sentiment analysis is the process of determining if a piece of data reveals a favorable, negative, or neutral attitude toward a subject. Put simply, sentiment analysis reveals the emotions behind a piece of text. User experience, survey replies, and product evaluations are all frequent applications for it.

The power of algorithms to analyze text has greatly increased as a result of the developing nature of deep learning. Advanced artificial intelligence algorithms—when used effectively—are a valuable tool for conducting detailed research.

Sentiment analysis can help you develop a better understanding of how your brand is perceived online. Sentiment analysis translates the language people use—with a combination of Natural Language Processing (NLP) and machine learning—to produce key insights automatically.

Sentiment analysis for social media
Sentiment analysis for social media

Why do Twitter sentiment analysis?

Sentiment analysis can help your business:

  • Recognize segments among your customers to improve marketing targets. You can create segments based on the words and phrases they use and create more targeted campaigns.
  • Find out potential problems users might have with your product. The use of words like “bug,” “problem,” “difficult,” and similar can indicate that there is a problem with your product you might not be aware of.
  • Plan to create new features/products or improve existing ones. Sometimes, people tweet something like: “This app is great, but I wish it had an integration with Slack.” You can see this as an opportunity and actually create that Slack integration they’re wishing.
  • Identify gaps in customer service and take action to enhance processes. People also write about their customer service experiences on Twitter, especially when they’re negative. You can find out their biggest pain points and see if you can do something to eliminate them.
  • Constantly follow the public’s opinion of your brand. You’ll know what’s the brand perception for your company/product and find out what improvements you need to make.

This technique is frequently used in the social media realm to learn how individuals feel about specific issues. In the case of Twitter, you’ll be analyzing tweets to find out the emotions and meaning of what people are saying about your brand.

The challenge with sentiment analysis

There are a variety of approaches and algorithms for implementing sentiment analysis, which can be divided into a number of categories:

  • Rule-based systems: where you set rules yourself. For example, if you have a healthcare diagnostic product, you can set the symptoms that refer to a specific illness.
  • Machine learning systems: used by automated systems to learn from data. Image recognition is a popular example where algorithms process thousands of images, learning to identify products more precisely.
  • Combination systems: rule-based and automated methods. This is a combination where you set the rules at the beginning and then the system learns from big quantities of data.

The ‘traditional’ way of doing sentiment analysis is with Python. How do you do Twitter sentiment analysis in Python—we hear you ask. It’s tricky—take a look.

You need to have:

  • Python and API knowledge
  • Installed, up-to-date Python and additional libraries that don’t come with the Python interpreter
  • Knowledge of training and test datasets

The goal is to create an application that uses the Twitter API to perform Twitter sentiment analysis in Python for tweets of your choice. Although it can have many variations and subactions, and requires different tools, a typical process looks a little like this:

  1. Set up a test set: where you’ll get authentication credentials, authenticate your Python script, and create the function to build the test set.
  2. Set up a training set: where you’ll define what’s positive, negative, and neutral when it comes to the tweets you want to process.
  3. Pre-process tweets in the datasets: or clean up the data that doesn’t contribute to your analysis like videos, URLs, usernames, emojis, etc.
  4. Create a classification algorithm: based on the Naive Bayes Classifier, a classification algorithm that relies on Bayes’ Theorem and requires some math knowledge.
  5. Test your model: by running the classifier on a number of pre-downloaded tweets.

But, if the process is this complex—why do Twitter sentiment analysis at all?

Doing TSA in R/Python is no small task.

First, the setup and download of sets and algorithms can take up many hours. The process consists of various steps that include a lot of data and libraries, which can be hard to follow.

It’s also a highly technical and time-consuming process—not all companies have the time or resources to perform sentiment analysis with Python. The good news is—they don’t have to.

There’s a simpler way that requires no coding knowledge. Plus, it’s quick and easy to set up and maintain. Let’s take a look.

How to do Twitter sentiment analysis without coding?

Using a no-code AI tool eliminates all the manual steps we mentioned in the previous section. There’s no coding, no confusion, and no worrying if you’ve done it right.

Here’s what the process with a no-code AI tool looks like.

Step 1: Define your tags

It’s time to build the AI model that you’ll train to classify your data. You can do this by defining tags that you want to add to the Tweets you’ll be analyzing. When doing sentiment analysis, you’ll normally be looking for data to fall into one of three categories:

  • Positive
  • Negative
  • Neutral

However, your model can go way beyond this classification. You can also add emotions to get a deeper understanding of what exactly your audience is feeling in regards to your brand. Here are some examples:

  • Happy
  • Sad
  • Worried
  • Disappointed

You can also consider tagging Tweets by intention:

  • Interested
  • Not interested

Let’s take this Tweet about Zapier as a real-world sentiment analysis example:

Tweet appreciating Zapier
Tweet about Zapier, Source

If we have the tags ‘positive’ and ‘happy’ in our model, they would detect the word appreciation.

Here’s another real-world sentiment analysis example—this time, about Airtable:

Tweet about airtable
Tweet about Airtable, Source

In this tweet, our model would detect the word love and tag it as a positive one.

To add a tag to a tweet, you need to set up a trigger. The trigger, in this case, would be when someone mentions your brand name or a specific hashtag connected to your company. For example, Nike could follow #JustDoIt.

You can also use the same model to track what people are saying about your competition.

Step 2: Organize and upload your data

A no-code AI tool gives you several options to organize your data. A good AI tool should allow you to upload:

  • PDFs
  • Images
  • Free text

To perform a no-code Twitter sentiment analysis, you want to use free text. For example, you can set up a trigger that dictates that when someone tweets about your company, your categorized tweets go into a Google Sheet—which contains all the labels you defined in your previous step.

Once you have your datasets ready, the next step is to map the content using the labels you defined in the previous step. This is how you’ll teach your AI model to classify the content according to your needs.

You should create at least two labels and add a minimum of 20 data points to train your model effectively for more accurate results.

You can build and customize your classifications to match your sentiment analysis objectives—based on how you wish to analyze tweets about your brand.

Finally, let’s consider how you’ll handle the data collected by your sentiment analysis.

Step 3: Add human review & evaluate results

Adding a human review or human-in-the-loop improves the model’s preciseness. The knowledge of the algorithm is usually based on data, so they can’t be as accurate as humans. That’s why some AI systems allow people to engage with them directly to adjust for this inherent unpredictability.

In this step, you want to consider whether you’re happy to leave the AI to generate its own results or if you want some input on the final output. The human review stage allows you to reduce the machine's margin of error.

There are three options that you can select for your categorized tweets:

  • No human review: where the prediction with the highest confidence will be accepted and susceptible to error.
  • Standard human review: where you define an acceptable error margin for predictions and agree to review the amount of data necessary to remain within your desired error margin. You can assign this block to a certain member of your team.
  • Advanced settings: where you set up your own error minimization settings and define all details.

With human review, you can analyze and evaluate the results in the Google Sheets you connected in the previous step. This helps ensure that all your data is being correctly categorized, and helps your AI model to improve over time.

How to take action based on your Twitter sentiment analysis

Once you’ve gathered your information, you need to know how to use it.

Let’s see how you can take action once the no-code AI tool determines whether a tweet contains positive, negative, or neutral opinions about your brand.

Positive feedback

Based on your categorized tweets, you can identify the products or features your users like the most and use that information to make better business decisions. Let’s consider an example.

A Twitter user could say they like your gamification feature—which wasn't initially a feature you highlighted about your product. Prioritizing it might bring you closer to a previously undiscovered user group.

The products and features that individuals praise in their favorable comments indicate your winners. You might want to highlight these features during the sales and marketing process.

Negative feedback

If a person has decided to post bad comments about you on social media, they’re likely not very happy with your product. This is an opportunity for you to address the issues, and prevent any further negative reviews.

Make sure a real person contacts them and offers to help and make up for the mistake. Listen to what this customer has to say and provide an adequate solution to the problem to turn this negative experience into a positive one.

Neutral feedback

Neutral feelings are turned into positive opinions with the right approach. Offer new features, special deals, or other perks that the user could be interested in. Additional benefits or rewards might convert a skeptic into an advocate—improving your brand image.

Make certain you thank them for the positive aspects of the review while also acknowledging the negative aspects. Apologize for the inconvenience and provide them real-time support to boost your brand's reputation in their eyes.

In addition to deciding what kind of sentiment a tweet contains, you can also use sentiment analysis to evaluate and improve your brand health. Let’s explore this further.

Brand monitoring for brand health

Users often talk about different experiences and provide suggestions. Ideas and remarks abound on social media platforms, product evaluations, blog articles, and discussion forums.

In fact, 51% of people have called out a company on social media. Twitter is the second most popular platform for this purpose, with 30% of consumers using it to mention companies. These callouts can seriously affect your brand health and tarnish your reputation. It’s important to practice active social listening to avoid such negative impacts.

Thanks to sentiment analysis, you can maintain and improve your brand health. It enables you to assess the online and social media noise regarding your products, services, and campaigns—allowing you to make any necessary adjustments.

Companies can swiftly recognize disgruntled consumers, classify problems by urgency, and prioritize answers using sentiment analysis technologies. Genuine sentiment analysis can help prevent possible PR issues, allowing you to intervene before a customer's negative experience becomes widely publicized.

Brand monitoring is a crucial area of work for businesses, and sentiment analysis gives real insights through in-depth analysis. To convert unpleasant events into positive ones, you must respond promptly and effectively.

No-code AI tools for Twitter sentiment analysis

You’ve likely heard the saying a man is only as good as his tools—this applies to Twitter sentiment analysis, too. Here are some no-code tools you could use for Twitter sentiment analysis:

  • Lexalytics: is a tool for analyzing text taken from a number of places, including Twitter.
  • Brandwatch: is a digital consumer intelligence company that offers users the ability to analyze text.

For a comprehensive approach to no-code machine learning that does much more than Twitter sentiment analysis, Levity is your best option. The platform enables you to not only analyze short-form text but also longer documents and image-based data.

Whether you’re in customer service, operations, or the product team—Levity can help you speed up your day-to-day workflow.

Wrapping up on no-code AI for Twitter sentiment analysis

All modern brands engage in meaningful social media interactions with consumers, prospects, and even competitors. Social media can provide insights you wouldn’t find anywhere else—those insights can then be developed and used to inform key business decisions.

With no-code AI, you can automate the sentiment analysis process and increase productivity.

Thanks to data classification, you can identify the approach you need to take with each tweet. As customer opinion often changes, an automated process will ensure you’ve always got your finger on the pulse when it comes to customer sentiments.

Get in touch today to start using Levity’s no-code AI solution for your sentiment analysis and much, much more. Get set up with a demo, and start benefiting from the many ways that AI can manage mundane tasks and improve your business processes.

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Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.

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