Our main goal with MonkeyLearn is to make text analysis simple.
We want to enable developers, marketers, salespeople, customer support representatives and entrepreneurs without any knowledge or experience in natural language processing or machine learning to take advantage of this technology.
MonkeyLearn does this through offering models that you can use to analyze your text. Models are very powerful, they can be used for categorizing text or extracting pieces of information, and they can be combined for complex processes. Plus, they improve in accuracy over time.
With these things in mind, users have two basic ways to use MonkeyLearn:
- Public Models: pre-trained and ready to use models for general tasks, created by the MonkeyLearn team. These models do not require any more training, are great at top-level analysis, but they are not as reliable as a Custom Model for more specific situations.
- Custom Models: created by users for their specific needs and based on their own context and criteria. Custom models take into account a client's unique conditions and processes, and can be very accurate with proper training. See getting started with a custom model for more information.
By using public models, users can have an easy and fast way to incorporate text analysis capabilities to their apps or workflows. Public models are models that have been already trained with training data and resolve particular text analysis tasks. They are ready to be integrated via a web API, Zapier or our Google Sheets integration.
These models are specialists in common tasks. Click on the link to test the model out:
General Use Models
- Language detection: detect language in text. New languages were added for a total of 48 different languages arranged in language families.
- Topic classification: classify text according to a broad and generic topic tree.
- Retail classification: classify retail products using their descriptions.
- News categorizer: classify news by topic.
- Business classifier: classify professional profiles or companies.
- Affinity profiler: classify users into affinity groups.
- Profanity & abuse detection: identify profanity or abuse in comments.
- E-commerce Customer Support Ticket Classifier: classify customer service tickets into categories like fraud, missing item, shipping problem, etc.
- NPS SaaS Product Classifier: classify NPS responses into categories like usability, features, pricing and customer Service.
- Outbound Sales Response Classifier: classify outbound sales email responses based on subject and body.
- Urgency Detection: classify customer support messages into "urgent" or "not urgent".
- Tweets Sentiment (English): classify tweets in English according to their sentiment: positive, neutral or negative.
- Tweets Sentiment (Spanish): classify tweets in Spanish between positive, neutral and negative sentiment.
- Product sentiment: classify product reviews and opinions in English as positive or negative according to the sentiment.
- English tweets products sentiment analysis: sentiment analysis for tweets about products and brand reviews.
- English tweets Apple products sentiment analysis: sentiment analysis for tweets about Apple products comments.
- Hotels sentiment: this sentiment analysis classifier was trained with data from different hotel review sites.
- Movies sentiment: this sentiment analysis classifier was trained with data from movie review sites.
- Restaurant sentiment: this sentiment analysis classifier was trained with data from different restaurant review sites.
- Telcos – Sentiment analysis (Twitter): sentiment analysis for tweets about phone carriers comments.
- Airlines Sentiment: sentiment analysis for tweets about airline reviews.
- Insight extractor (English): extract the most important insights from text in English.
- Keywords extractor (English): extract relevant keywords from texts in English.
- Keywords extractor (Spanish): extract relevant keywords from texts in Spanish.
- Entity extractors (English): extract entities (like people, companies and locations) from texts in English.
- Entity extractors (Spanish): extract entities from texts in Spanish.
- US Address extractor: use this model to extract US addresses from text.
- Useful data extractor: extract useful data (like dates, prices, phones, links and more) from text.
- Boilerplate Extractor: extract relevant text from HTML, it also removes boilerplate.
- Email Cleaner & Last Reply Extractor: Extract the last reply from an email thread. A cleaning model that removes signatures, confidentiality clauses and other replies from email threads. It relies on statistical algorithms and natural language processing technology to analyze your emails and generate a cleaned version that captures the actual message. The targeted language is English.
Check out and explore the public models here. New public models will be continuously added and improved by the MonkeyLearn team.