Sentiment Classification: A Beginner's Guide

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.

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.

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!

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!