We spend a lot of time having conversations and engaging with others via chat, email, websites, social media… But we don’t always stop to think about the massive amounts of text data we generate every second.
For businesses, customer behavior and feedback are invaluable sources of insights that indicate what customers like or dislike about products or services, and what they expect from a company.
However, most companies are still struggling to find the best way to analyze all this information. It’s mostly unstructured, so hard for computers to understand and overwhelming for humans to sort manually. As a business grows, manually processing large amounts of information is time-consuming, repetitive, and it simply doesn’t scale.
Fortunately, Natural Language Processing tools can help you discover valuable insights in unstructured text, and solve a variety of text analysis problems, like sentiment analysis, topic classification, and more.
Natural Language Processing (NLP) is a discipline within artificial intelligence that leverages linguistics and computer science to make human language intelligible to machines. By allowing computers to automatically analyze massive sets of data, NLP can help you find meaningful information in just seconds.
Let’s say you work at a SaaS and want to perform data analysis on customer support tickets to identify the most common issues raised by your clients. Considering that companies handle an average of 777 customer support tickets per month, manual processing goes out the window!
Enter NLP tools. They can help you easily classify support tickets by topic, to speed up your processes and deliver powerful insights.
So, how can you get started with NLP? There are plenty of online tools that can help.
Basically, you can start using NLP tools through SaaS (software as a service) tools or open-source libraries.
SaaS tools are ready-to-use and powerful cloud-based solutions that can be implemented with low or no code. SaaS platforms often offer pre-trained NLP models that can be used code-free, and APIs that are geared more towards those who want a more flexible, low-code, option, e.g. professional developers, or those learning to code, who want to simplify their work. If you are looking to implement NLP in a way that’s cost-effective and quick, SaaS tools are the way to go!
Open-source libraries, on the other hand, are free, flexible, and allow you to fully customize your NLP tools. They are aimed at developers, however, so they’re fairly complex to grasp and you will need experience in machine learning to build open-source NLP tools. Luckily, though, most of them are community-driven frameworks, so you can count on plenty of support.
To build your own NLP models with open-source libraries, you’ll need time to build infrastructures from scratch, and you’ll need money to invest in devs if you don’t already have an in-house team of experts.
Now that you have an idea of what’s available, tune into our list of top SaaS tools and NLP libraries.
MonkeyLearn is a user-friendly, NLP-powered platform that helps you gain valuable insights from your text data.
To get started, you can try one of the pre-trained models, to perform text analysis tasks such as sentiment analysis, topic classification, or keyword extraction. For more accurate insights, you can build a customized machine learning model tailored to your business.
Once you’ve trained your models to deliver accurate insights, you can connect your text analysis models to your favorite apps (like Google Sheets, Zendesk, Excel or Zapier) using our integrations (no coding skills needed!), or through MonkeyLearn’s APIs, available in all major programming languages.
Aylien is a SaaS API that uses deep learning and NLP to analyze large volumes of text-based data, such as academic publications, real-time content from news outlets and social media data. You can use it for NLP tasks like text summarization, article extraction, entity extraction, and sentiment analysis, among others.
IBM Watson is a suite of AI services stored in the IBM Cloud. One of its key features is Natural Language Understanding, which allows you to identify and extract keywords, categories, emotions, entities, and more.
It’s versatile, in that it can be tailored to different industries, from healthcare to finance, and has a trove of documents to help you get started.
The Google Cloud Natural Language API provides several pre-trained models for sentiment analysis, content classification, and entity extraction, among others. Also, it offers AutoML Natural Language, which allows you to build customized machine learning models.
As part of the Google Cloud infrastructure, it uses Google question-answering and language understanding technology.
Amazon Comprehend is an NLP service, integrated with the Amazon Web Services infrastructure. You can use this API for NLP tasks such as sentiment analysis, topic modeling, entity recognition, and more.
For those that work in healthcare, there’s a specialized variant: the Amazon Comprehend Medical, which allows you to perform advanced analysis of medical data using Machine Learning.
The Natural Language Toolkit (NLTK) with Python is one of the leading tools in NLP model building. Focused on research and education in the NLP field, NLTK is bolstered by an active community, as well as a range of tutorials for language processing, sample datasets, and resources that include a comprehensive Language Processing and Python handbook.
Although it takes a while to master this library, it’s considered an amazing playground to get hands-on NLP experience. With a modular structure, NLTK provides plenty of components for NLP tasks, like tokenization, tagging, stemming, parsing, and classification, among others.
Stanford Core NLP is a popular library built and maintained by the NLP community at Stanford University. It’s written in Java ‒ so you’ll need to install JDK on your computer ‒ but it has APIs in most programming languages.
The Core NLP toolkit allows you to perform a variety of NLP tasks, such as part-of-speech tagging, tokenization, or named entity recognition. Some of its main advantages include scalability and optimization for speed, making it a good choice for complex tasks.
TextBlob is a Python library that works as an extension of NLTK, allowing you to perform the same NLP tasks in a much more intuitive and user-friendly interface. Its learning curve is more simple than with other open-source libraries, so it’s an excellent choice for beginners, who want to tackle NLP tasks like sentiment analysis, text classification, part-of-speech tagging, and more.
One of the newest open-source Natural Language Processing with Python libraries on our list is SpaCy. It’s lightning-fast, easy to use, well-documented, and designed to support large volumes of data, not to mention, boasts a series of pretrained NLP models that make your job even easier. Unlike NLTK or CoreNLP, which display a number of algorithms for each task, SpaCy keeps its menu short and serves up the best available option for each task at hand.
This library is a great option if you want to prepare text for deep learning, and excels at extraction tasks. For the moment, it’s only available in English.
Gensim is a highly specialized Python library that largely deals with topic modeling tasks using algorithms like Latent Dirichlet Allocation (LDA). It’s also excellent at recognizing text similarities, indexing texts, and navigating different documents.
This library is fast, scalable, and good at handling large volumes of data. Here are some tutorials to get started.
Natural Language Processing tools are helping companies get insights from unstructured text data like emails, online reviews, social media posts, and more.
There are many online tools that make NLP accessible to your business, like open-source and SaaS. Open-source libraries are free, flexible, and allow developers to fully customize them. However, they’re not cost-effective and you’ll need to spend time building and training open-source tools before you can reap the benefits.
SaaS tools,on the other hand, are a great alternative if you don’t want to invest a lot of time building complex infrastructures or spend money on extra resources. MonkeyLearn, for example, offers tools that are ready to use right away – requiring low code or no code, and no installation needed. Most importantly, you can easily integrate MonkeyLearn’s models and APIs with your favorite apps.
Ready to get started with NLP? Discover MonkeyLearn and create your own custom tools for text analysis!
March 11th, 2020