Chatbot Data: Picking the Right Sources to Train Your Chatbot

If your own resource is WhatsApp conversation data, then you can use these steps directly. If your data comes from elsewhere, then you can adapt the steps to fit your specific text format. To train your chatbot to respond to industry-relevant questions, you’ll probably need to work with custom data, for example from existing support requests or chat logs from your company. The ChatterBot library combines language corpora, text processing, machine learning algorithms, and data storage and retrieval to allow you to build flexible chatbots. Now that you have created your chat bot and sent it out into the world, perhaps

you are looking for a way to share what it has learned with other chat bots? ChatterBot’s training module provides methods that allow you to export the [newline]content of your chat bot’s database as a training corpus that can be used to

train other chat bots.

Congratulations, you’ve built a Python chatbot using the ChatterBot library! Your chatbot isn’t a smarty plant just yet, but everyone has to start somewhere. You already helped it grow by training the chatbot with preprocessed conversation data from a WhatsApp chat export. In this section, you put everything back together and trained your chatbot with the cleaned corpus from your WhatsApp conversation chat export.

The importance of training and good data

You can also specify file paths to corpus files or directories of corpus files when calling the train method. Pick a ready to use chatbot template and customise it as per your needs. It doesn’t matter if you are a startup or a long-established company. This includes transcriptions from telephone calls, transactions, documents, and anything else you and your team can dig up.

Because the industry-specific chat data in the provided WhatsApp chat export focused on houseplants, Chatpot now has some opinions on houseplant care. It’ll readily share them with you if you ask about it—or really, when you ask about anything. You can run more than one training session, so in lines 13 to 16, you add another statement and another reply to your chatbot’s database. If you’re comfortable with these concepts, then you’ll probably be comfortable writing the code for this tutorial.

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Your custom trainer should inherit chatterbot.trainers.Trainer class. Your trainer will need to have a method named train, that can take any

parameters you choose. This training class will handle the process of downloading the compressed corpus

file and extracting it.

There are two main options businesses have for collecting chatbot data. Chatbots have evolved to become one of the current trends for eCommerce. But it’s the data you “feed” your chatbot that will make or break your virtual customer-facing representation. When it comes to any modern AI technology, data is always the key. Having the right kind of data is most important for tech like machine learning. Chatbots have been around in some form since their creation in 1994.

NLTK will automatically create the directory during the first run of your chatbot. The call to .get_response() in the final line of the short script is the only interaction with your chatbot. And yet—you have a functioning command-line chatbot that you can take for a spin. A fork might also come with additional installation instructions. Neural networks calculate the output from the input using weighted connections. They are computed from reputed iterations while training the data.

Some publicly available sources are The WikiQA Corpus, Yahoo Language Data, and Twitter Support (yes, all social media interactions have more value than you may have thought). A great next step for your chatbot to become better at handling inputs is to include more and better training data. If you do that, and utilize all the features for customization that ChatterBot offers, then you can create a chatbot that responds a little more on point than 🪴 Chatpot here. Your chatbot has increased its range of responses based on the training data that you fed to it.

You can’t just launch a chatbot with no data and expect customers to start using it. A chatbot with little or no training is bound to deliver a poor conversational experience. Knowing how to train and actual training isn’t something that happens overnight. Building a data set is complex, requires a lot of business knowledge, time, and effort. Often, it forms the IP of the team that is building the chatbot. An effective chatbot requires a massive amount of training data in order to quickly solve user inquiries without human intervention.

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