How Do You Create A Sentiment Analysis Process? And Why Is It Essential In Business?

These days, companies compete to create the most innovative solutions, products, and offers possible, hoping to win new customers.

But are they based on ideas alone? Or actual user needs? As it turns out, it looks like the former because research shows that 56% of consumers believe businesses need a deeper understanding of what they want. This is where sentiment analysis can help. 

Sentiment analysis enables businesses to better understand the needs, desires, and emotions of current and future customers. And in this article, we’ll walk you through how to use it in business, what benefits it can bring, and how to create it. 

Ready? Let’s go.

What is sentiment analysis?

Sentiment analysis (also known as opinion mining) is a popular NLP (Natural Language Processing) solution where one identifies feelings and emotions expressed in words. The emotions are usually divided into three categories: negative, neutral, or positive.

From a business perspective, companies use it to identify how customers feel about a brand, product, or service based on an analysis of the words they use when they mention it (be it a comment on social media, a community forum, or in emails). 

And if you want to learn about technical issues, we invite you to read this article.

How do you create a sentiment analysis process in machine learning?

The design of sentiment analysis processing systems will vary depending on the needs and capabilities of a given company. In the simplest, most classic use case, sentiment analysis consists of seven concrete steps:

1. Choose your content

First, you have to decide what kind of content you want to analyze. People express emotions differently in a movie review than in email correspondence, and the context influences the process design.

2. Gather your dataset

You need to gather as many labelled data points relevant to your particular type of document as possible. The dataset must contain the document content and a human-assigned label (`positive`, `neutral` or `negative`).

3. Split your dataset

Now you split your dataset into a training set and a hold-out set. A popular strategy is a random split, with about 20% of samples in the hold-out set.

4. Train a machine learning model

Here’s where you use your training dataset to train a machine learning model to classify your content as positive, neutral or negative (supervised learning, binary classification model). 

The model architecture is up to you, but we recommend training a proven, context-aware NLP model (like BERT). We also recommend not training a model from scratch but instead using a transfer learning technique. 

If you can start with a model that already understands text in your selected languages (because it was trained on a vast corpus of human language to develop associations and understanding of words and phrases), all the better.

You can fine-tune such a model for sentiment analysis tasks, which will provide much better results than if you try to train a model from scratch. Not sure where to start? 

Try our course on how to create and train a sentiment analysis model.

5. Validate your model

Now it’s time to validate your trained machine learning model on your hold-out dataset: do this by evaluating the values of chosen model analysis metrics and decide whether the output is good enough for your application.

6. Deploy your model

If you need real-time predictions, deploy the model as an endpoint. We recommend code-less serving platforms like `Tensorflow Serving` (preferably on a cluster of machines in the cloud for scalability, for example, Vertex AI Endpoints.)

You can learn how to do this in our free sentiment analysis course. And you can integrate external applications with the model over the endpoint’s HTTP API. If you don’t need live predictions, you can just use your trained model in batch prediction mode. 

Here, you can leverage Vertex AI Batch Predictions for the job. And once the process ends, you can import the batch prediction results into your other applications.

7. Monitor your model’s performance

Finally, don’t forget to monitor your model’s performance on real data! 

It might turn out that your actual documents are so different from the training set that the model’s performance isn’t ideal. In such a case, it might help to extend your training set with additional sources of good examples, ultimately re-training the model.

And if you want a step-by-step guide on how to apply BERT to sentiment analysis using TFX and Vertex AI pipelines, check out this free course.

6 ways sentiment analysis will help your business

Sentiment analysis offers several significant business benefits. Here are six reasons we recommend every business considers using it.

Get feedback about products and services

As we mentioned at the start of this article, 56% of consumers think businesses need to develop a deeper understanding of their needs. What’s the issue here? A lack of customer understanding will severely impact any business.

But you can avoid such problems using sentiment analysis, drawing valuable insights by learning about your customers’ responses to your products and services. You’ll see what they like and what annoys them, enabling you to adjust accordingly.

Save time and reduce your overheads

Sentiment analysis is a fully-automated process that offers time and cost optimizations. An algorithm analyzes the data, saving employees from reading thousands of customer reviews, categorizing them, and sharing the insights.

Reduce the risk of unsuccessful products

Companies launch over 30,000 new products every year, and 95% of them fail. With sentiment analysis, you can predict your customers’ response to new products or services  before they hit the market. 

Analyzing this data will help you decide whether it’s worth investing money in a particular solution or whether it’s better to focus on something else.

Improve the overall user experience

Algorithms can detect a customer’s emotional state in emails, live chat, and more. This enables businesses to prioritize specific messages and notify customer support when a disgruntled customer needs an immediate solution.

Such a process not only improves the user experience. It also helps businesses retain customers and limit negative reviews. That’s crucial because as many as 95% of new customers read reviews before making a purchase.

Measure the impact of marketing and sales campaigns

Marketers and sales professionals use many tools to measure the effectiveness of their campaigns by analyzing metrics and KPIs. 

They monitor things like leads and conversions. But in the midst of all this, do they remember to check how the user feels? Because as it turns out, this is crucial, with 95% of purchasing decisions influenced by factors like emotions.

With sentiment analysis, you can see how many clicks or leads a campaign generates alongside the emotions it evokes. How is it possible? For example, if you send a direct message campaign on Linkedin and get responses from your target audience, sentiment analysis will allow you to analyze their language in terms of emotion. In doing so, you can constantly optimize your messaging and create campaigns that bring about the best results.

React to complaints in real-time

Did you know that just 1-in-25 unsatisfied customers complain to your company directly? But not to worry. 

With sentiment analysis, you can check for complaints, no matter where they’re published online. Sentiment analysis can gather information from any online source by tracking keywords, allowing you to categorize mentions as positive, neutral, and negative. 

Setting up notifications for specific keywords could help you respond to complaints in real-time. And given that 53% of people expect companies to respond to negative reviews within a week, this would be an invaluable capability.

How can you use sentiment analysis in your organization?

You already know the benefits of sentiment analysis. And you know how to build it. But have you decided if it’s something you want to use? 

If you’re still on the fence, here are a few ways you could deploy it in your organization. Bear in mind that sentiment analysis has near-limitless applications. Whether you run a business, NGO, political party, or otherwise, we bet you can find a use for it.

Here’s a handful of the most popular ways to use this AI solution:

  • Customer Support Analysis: Sentiment analysis combined with Speech-to-Text can convert spoken language into text, allowing you to analyze customer service conversations in real-time.
    If the system detects an issue, it can inform a company representative, suggest solutions, or connect the customer with someone who can solve the problem. The system could even offer a discount by way of an apology.
  • Social Media Monitoring: This is one of the most popular applications of sentiment analysis, enabling businesses to surface the mentions of their company and categorize statements as positive, neutral, or negative.
    The options not only allow you to find reviews of your company but also manage your company’s reputation online.

social media monitoring

Source: Brand24

  • Market Research: Sentiment analysis is a must if you’re releasing a new product or just coming to market. It will show how your target audience perceives your product in the early days.
  • Customer Feedback Analysis: Sentiment analysis can analyze customer ratings on and off your site, helping you see what customers appreciate and dislike about your product, services, or other aspects of your company.
  • Competitor Analysis: Sentiment analysis isn’t just about your company. You can use it to track the competition, gaining insights into which campaigns or solutions from other companies earn the best response.
    It can also be a source of inspiration, showing you what your competitor’s customers are complaining about and helping you find the perfect solution.
  • Processing Employee Feedback: If you run a large organization, sentiment analysis can help you monitor how your employees feel. Use it to analyze surveys and get an overview of your teams’ needs and concerns.
  • Political Sentiment Analysis: Political parties are increasingly open to using AI (although it can sometimes pose a threat) to understand public sentiment. And as early as 2012, a real-time sentiment analysis system tracked public opinion of U.S. presidential candidates, all by analyzing Tweets.

Know your customers’ emotions and achieve better business results with advanced AI solutions

As you can see, sentiment analysis has many applications. And it can bring significant benefits to a business like yours.

So if you want to implement an advanced ML solution tailored to your organizational needs, feel free to contact one of our AI experts and tell us what you’re looking for.

Or if you’re more technically minded and want to build sentiment analysis from scratch, see what you make of our step-by-step guide with ready-made components.

Katarzyna Rojewska

Content Manager at DLabs.AI and copywriter who loves AI and new technology. She helps companies to be more creative and employee-friendly. Remote working allows her to travel around the world, therefore, she currently lives in Iceland.

BERT SENTIMENT ANALYSIS ON VERTEX AI USING TFX

Learn how to apply BERT to sentiment analysis using TFX and Vertex AI pipelines.

Read now

Read more on our blog