How to Build a Text Classifier with Machine Learning?

Text classification is one of the most used machine learning technologies today by modern businesses and corporations. Text classifiers can analyze and categorize text faster and more correctly than humans using natural language processing (NLP).

Let’s be honest, with valuable data gushing from numerous sources such as emails, chats, reviews, forums, social media, support tickets, and more, it’s hard for humans to keep up.

However, building a text classifier can make things much easier, and help you extract insights from large volumes of data. But it can be a challenging process. You will need to define tags you need to gather data for training your classifier.

In this post, we will discuss the necessary steps to successfully train your text classifier. Let’s get started.

Define your Tags

What tags do you wish to use to categorize your content? It’s the first thing you need to figure out when you get started. Here’s how to get started.

The first tag usually is the one that defines your brand or product

When you have the necessary data, you can dive into further classification based on the issue. For example Shipping, billing, product availability, discounts, etc.

Now that you have the data related to the key issue, you can start further analyzing it with sentiment analysis.

How to define your tags?

Keep your tags clear of one other

By utilizing disjoint tags, you can avoid specifying tags that are confusing or overlapping; there should be no uncertainty about where a sentence belongs. If your tags overlap, your model will be confused, and your predictions will be incorrect.

Do not mix your classification criteria

Each model should be classified using a single classification criterion. Consider a situation in which you want to categorize firms based on the information provided about them. What you add in your tags is entirely up to you: B2B/B2C, Enterprise/Finance, Media/Construction, and so on.

One model should be used to classify businesses based on their clients (B2C, B2B, and Enterprise), while the other should be used to classify businesses based on the industry vertical in which they operate (Finance, Media, Construction). A precise set of criteria and a specific aim are developed for each model.

Structure your tags

It is critical to categorize your tags based on their semantic links. Basketball and baseball, for example, have suitable subtags because they are distinct sports. Similarly, tags for clothing and electronics in retail should be segregated.

The tags we’ll need for a classification process based on these tags comprise three classifiers: one that distinguishes between Sports and Retail, another that distinguishes between Sports subtags (basketball and baseball), and a third that distinguishes between Retail subtags (clothing and shoes) (Clothing and Electronics). A classification procedure with a clear structure is required to create reliable predictions using your classifiers.

Begin small and work your way up

If this is your first time training a text classifier, we recommend starting with a simple model. To provide accurate forecasts, complex models may need more time and effort. Initially, limit yourself to no more than a dozen tags.

Try adding a few more tags to your model and tweaking it until the new tags operate as expected. You’ll be able to keep iterating and adding tags as needed after a while.

Gathering the Data

After you’ve specified your tags, the following step is to collect text data, i.e., the texts you wish to utilize as training samples that represent future texts that your model should automatically classify.

We recommend that you collect data from the following potential sources:

The internal data

You can make use of a variety of different types of internal data, such as files, documents, spreadsheets, emails, help desk tickets, and more. It’s possible that you already have this information in the databases or programs you use on a daily basis:

• CRMs: Salesforce, Hubspot CRM, etc
• Customer Support/Interaction: Zendesk, Desk, Intercom
• Chat: Slack, Hipchat, Messenger
• Data Analytics: Segment, Mixpanel
• NPS: Delighted, Satismeter, etc
• Databases: Postgres, MySQL, Redis
• The external data

It’s easy to acquire data from the web utilizing web scraping tools, APIs, and freely available open data sets.

Frameworks for web scraping

Using a web scraping framework is an option if you have coding skills and want to construct your own scraper to collect data from the web. For web scraping, below are some of the most used tools:

• Python: Scrapy, Cola, Pyspider
• PHP: Goutte
• Javascript: Node Crawler, Simple Crawler
• Ruby: Upton, Wombat

Web scraping tools

If you lack coding experience, it is possible to generate a web scraper with just a few mouse clicks using some of these visual tools, such as Portia, ParseHub, etc

APIs

To collect the data you need for your machine learning classifier, you can use APIs to interact with some websites or social media networks. For instance, the following APIs can be used to retrieve text data: AngelList, eBay, Facebook, GitHub, New York Times, Twitter, etc

Open data

Sites like Kaggle, Quandl, and Data.gov all have available data that you may use to your advantage in your project.

Other tools

If you’re not familiar with coding, use Zapier or IFTTT to automate the process of obtaining your text data. In order to use them, you don’t have to write any code to connect to the tools you use every day.

If you’re familiar with a particular tool or technique, you may want to consider utilizing it instead of these examples.

Building your Text Classifier

Here’s how to build a text classifier with BytesView.

• Choose a text classification model (topic labeling, sentiment analysis, intent detection, etc)
• Import text data using CSV/Excel to train the text classifier
• Create the tags for the text classifier
• Tag the data to create samples for the classifier
• Once you have trained your classifier with relevant data, just upload the data in Excel/CSV file and select the columns you want to analyze.
• You can further improve the accuracy of the model by training with additional samples at regular time intervals.

Wrapping Up

Text analytics is a powerful tool if used the right way. Although, to build an effective and accurate text classifier you need to follow the above-mentioned steps. And before you start using the text classifier, make sure that you test it with data samples and evaluate its accuracy.

You can’t train an algorithm with faulty samples, because the model will make a lot of mistakes if you do so. A high-quality dataset, on the other hand, will allow for accurate modeling and the automation of the analysis of text data by machines. Get started today with BytesView.