There is a lot of written text out there that pertains to your business and needs to be cataloged or analyzed. It might be emails, reviews, support tickets, comments on social media, feedback, news articles, etc.
Working through a high volume of text data can be challenging for a team, requiring a lot of time for manual and repetitive tasks, and with a high degree of errors.
MonkeyLearn is built to allow you and your team to analyze this overflow of data automatically. This translates into responding to clients more quickly, detecting problems before they snowball, and prioritizing your team's time to work on higher impact items.
Many of our clients are using MonkeyLearn in the following areas:
- Automatically routing new tickets to team members
- Detecting urgency and emotion in tickets, tweets, etc.
- Tagging tickets by category based on content
- Analyzing sentiment (positive / negative) in reviews or tweets
- Breaking down complex reviews into "opinion units" to categorize feedback by product or service
- Comparing product reviews with competitors to inform strategy
Sales & Marketing
- Analyzing news articles for mentions, topics or keywords
- Extracting data from documents or lists
- Detecting client interest in sales emails
How does text analysis with MonkeyLearn work?
MonkeyLearn offers access to machine learning models that analyze incoming text and make predictions. Models can be trained to analyze sentiment, emotion, urgency, topics, or to look for pieces of information inside texts such as keywords, entities, etc.
There are two types of models for text analysis: Classifiers and Extractors. Users can choose to build their own classifiers and extractors, or use a series of pre-trained models to analyze their text. For more on models, please see the "What is a model?" section in our FAQ.
To analyze your text, you can batch process files by uploading them directly to the model in question. You can also use our extension to process text in Google Sheets, as well as integration options through Zapier and our API.
See Integrations for more information.