Machine learning can perform some pretty amazing feats to automate processes and gather powerful insights from all manner of text data: from documents, surveys, emails, customer support tickets, social media, and all over the web.
But you first need to begin with proper training data to ensure that your machine learning models are set up for success.
Training data (or a training dataset) is the initial data used to train machine learning models.
Training datasets are fed to machine learning algorithms to teach them how to make predictions or perform a desired task.
If you’re training a sentiment analysis model (that analyzes text for opinion polarity: positive, negative, and neutral), training data examples could be:
Unsupervised learning uses unlabeled data. Models are tasked with finding patterns (or similarities and deviations) in the data to make inferences and reach conclusions.
With supervised learning, on the other hand, humans must tag, label, or annotate the data to their criteria, in order to train the model to reach the desired conclusion (output). Labeled data is shown in the examples above, where the desired outputs are predetermined.
There are also hybrid models that use a combination of supervised and unsupervised learning.
In topic analysis (text categorization), below, we are using a supervised machine learning model and training the model to automatically analyze and categorize customer support tickets into topics, like Shipping, Billing, Account, Login, etc.
Take a look at how we annotate the input data with a desired output tag to properly train the model.
Training data is the initial dataset you use to teach a machine learning application to recognize patterns or perform to your criteria, while testing or validation data is used to evaluate your model’s accuracy.
You’ll need a new dataset to validate the model because it already “knows” the training data. How it performs on new test data will let you know if it’s working accurately or if it requires more training data to perform to your specifications.
Let’s say you’re training a model to sentiment analyze tweets about your brand. You can search Twitter for brand mentions and download the data to a CSV file, then you would randomly split this data into a training set and a testing set. Splitting the data into 80% training and 20% testing is generally an accepted practice in data science.
MonkeyLearn offers a number of integrations to sync your data. By using similar data for training and testing, you can minimize the effects of data discrepancies and better understand the characteristics of the model.
Traditional programming algorithms follow a set of instructions to transform data into a desired output with no deviations.
Machine learning algorithms, on the other hand, enable machines to solve problems based on past observations. The great thing about machine learning models is that they improve over time, as they’re exposed to relevant training data.
Let’s break the data training process down into three steps:
1. Feed a machine learning model training input data
2. Tag training data with a desired output. The model transforms the training data into text vectors – numbers that represent data features.
3. Test your model by feeding it testing (or unseen) data. Algorithms are trained to associate feature vectors with tags based on manually tagged samples, then learn to make predictions when processing unseen data.
You will, of course, need data relevant to the task at hand or the problem you’re trying to solve. If your goal is to automate customer support processes, you’d use a dataset of your actual customer support data, or it would be skewed.
There are, however, three more factors to consider when training your machine learning models: people, processes, and tools.
The team that will be training your models will have a huge impact on their performance. So you need workers that are familiar with your business and your goals, all using the same criteria to train the models. Whether analyzing social media data for sentiment or categorizing support tickets by department or for degree of urgency, there is a level of subjectivity involved. Regular training and testing is important to maintain consistent data tagging.
Similarly, quality controls must be put in place to maintain consistency. Step-by-step guidelines are important to ensure that all models are trained with the same process, and clear communication is key to upholding training criteria.
The above are simply irrelevant if you don’t have the right tools. Flexibility and ease-of-use are crucial if you don’t want to put whole teams to work on building your own tools. SaaS text analysis tools, like MonkeyLearn allow you to train and implement models with little to no code at any scale.
MonkeyLearn offers simple integrations with tools you already use, like Zapier, Zendesk, SurveyMonkey, Google, Excel, and more, so you can get quality data right from the source. Check out training data best practices to see how easy it is to set up.
There isn’t a hard-and-fast-rule but tasks like training a model to analyze the sentiment of brand mentions will require far less data than something that needs an incredibly confident model, like self-driving cars.
With text analysis, it all depends on the use case and the number of tags you need. As a general rule of thumb for training MonkeyLearn models:
The more you train your model, the smarter it will become, so it’s always safe to err on the side of “a lot of training data.”
Now that you understand what training data is and why it’s important, you can put your own training dataset to work training your own MonkeyLearn text analysis model:
Or schedule a demo and we’ll show you how to get the most from your data.
November 2nd, 2020