Sentiment Classification: A Beginner's Guide

Your customers have emotions and feelings.

Recognizing, addressing, and appreciating that your customers associate emotional value with your brand, product, or service helps you address those feelings—be they negative or positive.

Understanding what customers say or feel about your brand is critical to creating a positive customer experience and brand image.

Let's say you want to monitor your content for different tones—such as sad, happy, frustrated, and joyful—flag inappropriate or negative comments, or analyze search results. You’re eager to learn how customers interact with different facets of your business and their thoughts and opinions around them.

But…

You don't have the time and resources to analyze all of this data manually, or just don't want to spend your valuable time—that could be better spent elsewhere—manually classifying sentiment.

How do you ensure you’re able to manage the many moving parts of your business while successfully understanding your customers’ thoughts and feelings? Is what you’re doing worth it, or should you try something else?

The sweet and short answer: sentiment classification.

Sentiment classification helps you answer questions like:

• What are customers saying about your brand?

• Are they satisfied or dissatisfied with your services?

• What do they like or dislike about the user experience?

• What can you do better to address their concerns and issues?

When you make an effort to know what your customers feel, you stand a better chance of attracting and retaining them. Sentiment classification is a powerful technique to help you do just that.

Let’s start with the basics.

What is sentiment classification?

Sentiment classification is the automated process of identifying and classifying emotions in text as positive sentiment, negative sentiment, or neutral sentiment based on the opinions expressed within. It helps determine the nature and extent of feelings conveyed using Natural Language Processing (NLP) to understand what customers say or feel about your brand, products, and services.

Manually reading, classifying, and sorting text data can take a long time. You can lose the essence of what's important when you get stuck manually analyzing and classifying positive and negative sentiments.

Sentiment classification has seen tremendous growth in recent years. Many businesses use it to better understand their customers' feelings, behavior, preferences, and needs.

Automated classification methods enable you to analyze reviews, comments, survey responses, and other public opinions faster. They analyze data to determine the overall tone toward your brand and products, as well as the broader industry and market trends and insights.

Sentiment classification applications

Sentiment classification can be used in many areas, from marketing and advertising to customer service and product research. Here are some of its top applications.

Customer service

Customer service teams can use sentiment classification to understand customers better. Using NLP and sentiment analysis-based tools, they can identify, prioritize, and resolve customer issues faster.

When analyzing customer feedback, you can use sentiment classification to spot different trends. By monitoring feedback sentiment, you can uncover patterns that indicate if your team meets or exceeds expectations and identify areas for improvement. This allows you to focus on the specific areas that matter most to your customers.

The easiest way to do sentiment classification is with Artificial Intelligence (AI) tools. Automation accelerates your processes and makes them more efficient.

A no-code AI tool like Levity simplifies classifying sentiment in text data. You also get multiple integrations with platforms like Slack and Zapier to pull your data and automatically notify relevant people based on the sentiments classified from customer data.

Classify customer insights through Sentiment Classification with Levity

Social media monitoring

Social media is crucial for gaining insights into what people think about a given topic—in this case, your brand.

However, when it comes to social media monitoring and social listening, it's easy to get overwhelmed by the sheer quantity of feedback and data to monitor. Automated sentiment classification is a winner when it comes to monitoring, understanding, and responding to social media conversations and addressing complaints and concerns.

With sentiment classification, you can easily monitor social media platforms and see which topics are getting the most attention—whether that’s positive or negative. Social media sentiment classification is a great way to stay ahead of emerging trends and capture negative reviews early on.

This allows your team to proactively work toward a solution and nurture a positive customer experience in the future.

Practicing social listening is the first step in helping you understand the sentiment behind a social review, post, or comment. With an integrated AI platform like Levity, you can easily hear what people talk about you, track keyword engagement, cut through the noise, and prioritize insights—all without lifting a finger.

Listen to your customers and track essential keywords on the go with Levity

Market and competitive research

Since most business is done online, the competition has reached another level. Market and competitive research are both essential for monitoring sales growth and product success.

Keeping track of your competitors or key market trends can be hectic and time-consuming. Sentiment classification tools save the day when it comes to understanding the market, identifying potential competitors, and discovering customer trends and purchasing habits.

The ability to quickly gauge sentiment is key to any market research exercise. Therefore, it’s essential to use the right tool to classify sentiment correctly and get quick insights. By closely checking sentiment around your brand, you can prioritize customer issues, segment customers, and improve customer satisfaction with appropriate solutions.

Put your data to work and analyze sentiments with Levity

Sentiment classification models

Sentiment classification models use NLP to combine linguistics—the study of language—with computer science, computer vision, data science, and Machine Learning (ML) to extract, classify, and understand human sentiment.

Let’s take a look at some common sentiment classification models.

Rule or Lexicon-based approach

In a rules-based sentiment classification system, you can create specific rules to classify text data into different categories. A popular approach is to create a rule or lexicon-based sentiment classifier inspired by how human experts classify documents.

Rule-based systems use rules to determine a sentence’s sentiment. Lexicon-based systems are based on a dictionary of words with associated sentiment values.

For example, positive and negative are two words used to classify text in many Natural Language Processing tools. In this approach, we first try to map every word in the text to a category using a lexicon. The lexicon is then used by the AI model as a lookup table for predicting the category with which the next word should be classified.

This model can greatly simplify the classification task by eliminating the need to manually classify sentiment. Lexicons are useful in situations where it's not possible to collect data manually and completely remove the need for human involvement.

However, rule or lexicon-based models cannot handle complex sentences with multiple intertwined concepts. You also have to train the model before it can accurately classify sentiments.

AutoML systems

AutoML systems are automated systems that use Machine Learning-based text classifiers to analyze and classify sentiment based on past understanding.

To determine the type of sentiments text data contains, AutoML systems automatically detect and categorize emotions using a variety of Machine Learning models.

These systems are trained to extract the relevant information from a given piece of content and determine its sentiment. The extracted data may be a single word or phrase.

For example, AutoML systems can extract information such as buying advice, product reviews, recommended accessories, product comparisons, and buying guides from a wide variety of customer data. In this case, words or phrases commonly found in the content, let’s say reviews, are automatically flagged as possible data points for further classification.

The AI model uses the extracted data to train itself, build a model, and classify new datasets. Once the model is trained and tested, the system can easily determine a content’s sentiment. AutoML systems often analyze news articles—an area fraught with controversy and dispute.

The accuracy of AutoML models varies depending on the training dataset used to train them. An inaccurate training dataset has disastrous effects on the AI model, and hinders its effectiveness.

Hybrid systems

Hybrid systems combine rule-based methods with Machine Learning algorithms to provide a more accurate and dynamic understanding of sentiment. Hybrid systems can identify sentiment in social media posts, online reviews, or blog posts to determine whether the content is positive, negative, or neutral.

They’re also becoming more common because they enable quick and easy integration of multiple tools. You can use a hybrid model with different data sources, including text, social media, and image data.

Using a hybrid sentiment classification system offers many advantages, including that it can provide a more comprehensive view of sentiment.

However, hybrid systems are more complex and require more resources than other approaches.

They take more time and data to train than simpler models. The more data available for training, the better the model's overall performance. This means that high-quality, human-annotated datasets are important components of a hybrid sentiment classification project.

How to build a sentiment classification model

Want to build a robust sentiment classification model that can take the stress out of manual sentiment classification? Levity is here to help.

Levity is a no-code sentiment analysis library. It works by analyzing text with regular expressions to extract keywords and sentiment.

Building a sentiment classification model with Levity takes just a few clicks.

Train a model using a training dataset or use an existing model. Once you have trained your model, you can use it to classify text and predict the sentiment of new documents.

Here's how you can use Levity to create a Twitter sentiment classification model and use sentiment analysis for Twitter without coding.

Upload data and define labels

Uploading data is the first step in training AI blocks with Levity. Levity offers flexibility in data formats: PDF, images, or free text.

To get started, create a new AI block and name it. You’ll come across two options to upload your data:

• Labeled data: each piece of data is tagged with the relevant sentiment—you’re good to go!

• Unlabeled data: you’ll need to assign labels so that the AI block can identify the characteristics of the data associated with each label. Time to get to work labeling it!

When it comes to sentiment classification, you’ll likely be looking to categorize data as positive, negative, or neutral. You can also go further than this by specifying exact feelings, such as happy, frustrated, confused, and more.

Set up an AI Block - Label your data on Levity

Train your model and analyze the results

The next step is training your AI model to classify tweets based on your labels. This shouldn’t take long—five to 15 minutes—and provides an accuracy score for you to consider. This score indicates how good your AI model is at making accurate predictions.

You can test your AI Block's performance by typing a sentence in the 'Test' tab. The model will predict the sentiment of your sentence, and and however this performs will reflect the accuracy of your AI model.

Test your AI Block's performance on Levity

Build your AI workflow

Once you’ve trained your AI block, you need to integrate it into an AI workflow. This workflow enables you to automate the sentiment classification process from start to finish.

You’ll need to define your input—where data comes from—and your output—actions following the machine’s predictions. For twitter sentiment analysis, you could pull data from Twitter, analyze it, and segment negative, positive, and neutral statements into three different Google Sheets.

Once you’ve created your AI workflow, you’re ready to start analyzing new tweets!

Connect your AI Block to a workflow

Train a custom sentiment classification model with Levity

When analyzing content, you may come across many types of feelings and ways that people express themselves. Classifying content into relevant sentiments is a huge challenge for businesses.

That being said, it’s worth the hassle.

Sentiment analysis gives you key insights that can help you improve your product and service, identify issues before it’s too late, and understand how customers view your brand overall.

Levity equips you with the right features and capabilities to start working on your sentiment classification model without any coding knowledge or expertise. It’s quick and simple, and streamlines your sentiment classification process for increased efficiency.

Join a demo to learn more about using Levity's no-code AI solution for your sentiment classification needs, and sign up to start automating your daily tasks!