Text analysis, also known as text mining is a process that enables you to extract valuable insights from unstructured text. It uses natural language processing (NLP) to break down the data and machine learning to extract machine-readable insights from textual data.
But you don’t have to build a text analysis solution of your from scratch. You can choose a text analysis API (Application Programming Interface) instead. Integrating an API of an existing tool is the most popular way of getting started with text analysis. You can harness the power of a perfected tool that does not have bugs. Also, you can easily get support services for the software. You can choose between open-source or SaaS software based on your requirements.
Even if you have various options, choosing the right API can be difficult. To help you make it easier, this article lists some of the most effective text analysis APIs that you can use.
Top 10 Text Analysis APIs
Here are some of the widely sought-after paid and free text analysis APIs that you can use:
Best Open-source or Free Text Analysis APIs
This Python module provides industrial-strength text mining capabilities. SpaCy is incredibly fast, can handle large-scale datasets, and excels at prepping text for deep learning. It contains pre-trained models for a range of tasks, including text categorization, named entity recognition, tagging, and dependency parsing, among others. The API is rather straightforward to use and can be effortlessly deployed using their well-documented guidelines.
TensorFlow is a machine learning open-source library developed by Google that supports a variety of languages. It is used for deep learning and advanced text categorization, summarization, tagging, and speech recognition applications. Large organizations use TensorFlow to construct models because it allows them to analyze data on a massive scale.
TensorFlow APIs are available in a variety of programming languages, with Python being the most user friendly. TensorFlow is not the best choice for novices because of its steep learning curve.
NLTK (Natural Language ToolKit)
NLTK is the most well-known Python text mining library. NLTK demonstrates all potential approaches to addressing specific text mining difficulties, such as subject categorization or named entity recognition. It enables you to choose the data analysis technique that yields the most suitable output. However, it is not ideal for working with demanding tasks or vast volumes of data. NLTK contains several APIs that may be used to assist with activities such as sentiment analysis, keyword extraction, tagging, and more.
Another popular Python machine learning library is Scikit-learn. Using text mining techniques such as text classification, regression, and clustering, as well as more complex text mining techniques, simple text analysis functions such as keyword identification, content characterization, and clustering, are provided to aid the end-users. Simple, diverse, and connected: This program is quite simple to use, and has a variety of uses. It also has a thriving community for support.
It is an open API that provides documented and consistent functionality, which you can take advantage of. While Scikit-learn can serve as a valuable first course in text analysis, it is not compatible with more advanced deep learning methods.
PyTorch is a machine learning library developed as an open-source project by Facebook. This framework is extremely useful for computer vision, machine learning, content-based filtering and categorization, and other applications. This library is completely integrated with Python, making it usable by programmers of various skill levels. Furthermore, it has been trained on a range of models. Academics and researchers often use PyTorch’s API because it is simple to use, versatile, and efficient.
Best Paid or SaaS Text Analysis APIs
BytesView is a versatile text analysis API that offers a wide range of features. It provides pre-trained models so even a novice that has no programming experience can use them with ease. It can process and analyze large volumes of textual data. The API also comes with various plugins for easy integration of data, such as analyzing customer queries (Voice of customers). You can use the API to perform:
- Sentiment Analysis
- Keyword Extraction
- Named-entity Recognition
- Topic Labeling
- Emotion Analysis
- Intent Detection
- Feature Extraction
- Semantic Similarities
Also, you can use the pre-trained models or you can build custom models by training them with data related to your organization or industry.
The MonkeyLearn cloud-based machine learning system analyses and evaluates textual information. MonkeyLearn provides a straightforward and comprehensive text mining API, as well as a plethora of pre-built text mining examples that demonstrate how to execute tasks such as sentiment analysis, entity extraction, topic labeling, and other activities.
Google Cloud NLP
Google Cloud Natural Language Processing analyses unstructured text using a range of AI and machine learning capabilities. A developer can use the Cloud NLP API to comprehend dialogues and distinguish emotions and syntax (such as tokens, dependence, and part-of-speech) to study the topic and identify entities within documents.
Even if you lack machine learning knowledge, you can build a custom machine learning model using one of the powerful pre-trained models or the AutoML Natural Language tool.
The Microsoft Azure Text Analytics API is a suite of services built with Microsoft’s robust machine learning algorithms. You may use the API to do four core tasks: sentiment analysis, language identification, named entity recognition, and keyword extraction.
There is no requirement for training data or customization to use these models. You may get started even if you are completely new to programming. There are tutorials available that show how to use the API based on your programming experience, which you can find here.
Lexalytics is another cloud-based text analysis platform. It offers pre-trained data analysis models that individuals can easily implement without programming skills. It also enables you to tailor custom solutions with ease. You can use the API to perform sentiment analysis, topic labeling, keyword extraction, entity recognition, and more. You can use the pre-trained models or build custom models with data related to your organization.
Textual data can offer an organization useful information that it can use to improve customer experiences, employee efficiency, and other aspects. You can start by experimenting with different text analysis APIs. Select the ideal one for your project based on its size, budget, and technological capabilities. For a comprehensive but easy-to-use solution, give BytesView a try.
Until next time, Adios!