12 Top Text Analysis APIs for 2022

2. IBM Watson

IBM Watson uses Deep Learning to find meaning in unstructured text data. Businesses across industries leverage this platform to extract text metadata such as entities, keywords, sentiment, relations, categories, and syntax.

IBM Watson features:

IBM Watson offers the following features to help you eliminate data silos and find meaningful insights from big data, texts, and documents.

• Domain customization: helps Watson learn the language your industry speaks.
• Text analytics: pulls metadata, patterns, and insights in real time.
• Entities: identifies different entity types across the unstructured text.
• Categories: segment data using a five-level classification hierarchy.
• Classifications: automate workflows with custom text labels.
• Concepts: recognize high-level ideas even in absence of direct references in texts.
• Emotions: detect and analyze emotions around target phrases.
• Sentiment: detects text or phrase sentiments for different entities.

IBM Watson pros:

• In-depth sentiment results
• Supports multiple languages
• Easy no-code data manipulation

IBM Watson cons:

• Steep learning curve
• Comparatively expensive
• Lacks diagnostics and error identification

3. BytesView

BytesView is a data analysis tool that uses NLP to extract personalized insights from unstructured data. This platform features sentiment analysis, text clustering, data auto-tagging, intent detection, and topic extraction functionalities.

BytesView also offers an analytical dashboard for you to visualize insights and develop strategies.

BytesView features:

BytesView offers text classification and extraction solutions along with the following features.

• Topic labeling: labels and categorizes data from multiple sources.
• Intent detection: classifies intent behind interactions and text data.
• Gender detection: reveals audience demographics for personalized messaging.
• Semantic similarities: identify if two text pieces bear the same meaning.
• Sentiment and emotion analysis: interpret complex text data to find opinions and discover the meaning behind your data.
• Feature, entity, and keyword extraction: identifies industry-related features, targeted keywords, and key entities.

BytesView pros:

• Responsive customer support
• Quick data visualization capability
• Easy personalized insights extraction

BytesView cons:

• Takes time to learn the process
• Time-consuming model training process

4. Amazon Comprehend

Amazon Comprehend uses NLP to simplify text processing workflows, uncover insights, and protect personally identifiable information (PII). Amazon Comprehend APIs use JavaScript Object Notation (JSON) format to provide key phrase, sentiment, language, and entity outputs.

Amazon Comprehend features:

Amazon Comprehend offers the following features to help businesses find meaningful insights in texts.

• Custom classification: creates custom models using business-specific labels.
• Customer entity recognition: uses AutoML to identify domain-specific terms and categorize named entities.
• Sentiment analysis: depicts overall user sentiment.
• Keyphrase extraction: shows key phrases with a confidence score.
• Events and language detection: answers who-what-when-where questions from large datasets and recognizes 100+languages to find dominant text languages.
• Syntax analysis: uses tokenization and Parts of Speech (PoS) to show word boundaries and labels.
• Topic modeling: discovers common topics from text groups.

Amazon Comprehend pros:

• Easy to use
• High native language accuracy
• Superior pre-trained NLP text analysis AI engine

Amazon Comprehend cons:

• Can be expensive
• Lengthy implementation
• Lacks use case specific models

5. Google Cloud NLP

Google Cloud NLP API uses AutoML to train custom models and extract insights from text. This platform also allows you to apply Natural Language Understanding (NLU) to business applications.

Google Cloud NLP features:

Google Cloud NLP offers three text analysis solutions.

• AutoML: uses Vertex AI to extract data and detect sentiments.
• Natural Language API: offers pre-trained models for you to apply NLP techniques—such as sentiment analysis, text classification, and more—to apps.
• Healthcare Natural Language AI: helps you gather machine-readable insights from medical text and documents.

Google Cloud NLP pros:

• Efficient NLP engine
• Accurate text classification
• Offers REST API-based access

Google Cloud NLP cons:

• Can be expensive
• Time-consuming integration process
• Limited support for multiple languages

‍6. Aylien API

Aylien API is an easy-to-use, AI-powered news API that helps you analyze and extract insights from textual content like documents, social comments, and reviews. This platform excels in delivering ingestible structured data feeds that enable you to manipulate, query, and interpret data effortlessly.

Aylien API features:

Ayline API helps businesses find hidden news data insights with the following features.

• Public and licensed sources: offer data from 80,000 trusted sources.
• Multilingual content: supports content ingestion and translation in five languages.
• NLP enrichment: ensures efficient entity, category, and topical tagging.
• Event clustering: detects events and deduplicates articles.
• Sentiment analysis: captures overall document and entity sentiment.
• Multi-language SDK: supports four languages to help you get started instantly.
• Source filtering: sorts attributes such as author, popularity, language, and location.
• Entity-based search: discover organization, people, location, and brand mentions.
• Category and industry research: supports 3,000 topics and 1500 industry tags.
• Search by event: uses SOTA clustering and deduplication models to find events related to your search.

Aylien API pros:

• Excellent documentation
• Responsive customer support

Aylien API cons:

• Often doesn’t show relevant results
• Shows articles from paid sources

7. Apache OpenNLP

Apache OpenNLP is an open-source NLP Java library for processing texts. This Machine Learning-based toolkit is ideal for use cases like POS tagging, tokenization, sentence detection, and named entity recognition.

Apache OpenNLP features:

Below are some of the key features of this platform.

• Named entity recognition: extracts people, things, and location insights.
• Summarize: sums up documents, articles, and paragraphs.
• Searching: discovers synonyms of search strings, even with misspelled or changed input.
• Tagging: segments text into different elements for analysis.
• Translation: converts one language into another.
• Information grouping: clubs together relevant textual information from documents.
• Natural language generation: automates information reports based on data generated from databases.
• Speech recognition: analyzes human speech with built-in features.
• Feedback analysis: extracts insights from customer testimonials.

Apache OpenNLP pros:

• Easy to use
• Extensive documentation available
• Useful for documents with different languages

Apache OpenNLP cons:

• Lacks faster update releases
• Chunking and parsing could be better

8.SpaCy

SpaCy is a robust NLP API that helps you gather insights faster with its simple and productive API. This platform excels at using memory-managed Cython to extract information at scale.

SpaCy features:

SpaCy automates end-to-end text insight extraction workflows with the following features.

• Components: provide key solutions such as Named Entity Recognition, dependency parsing, lemmatization, entity linking, part-of-speech tagging, sentence segmentation, morphological analysis, and text classification.
• Custom models: support frameworks such as PyTorch and TensorFlow.
• Visualization: offers key insights with syntax and NER.
• Trained pipelines: support 22 languages.
• Pre-trained transformers: facilitate multi-task learning.

SpaCy pros:

• Support community
• Efficient information extraction
• Pre-trained model for smooth functioning

SpaCy cons:

• Slower sentence parser
• Documentation lacks examples
• Lacks intuitive user interface

9. TensorFlow

TensorFlow is an end-to-end open-source API that uses Machine Learning to help developers build powerful applications. The platform features a high-level Keras API for building and training custom models.

TensorFlow features:

TensorFlow eases how you run Machine Learning algorithm experiments and visualize flow graphs with the following features.

• Model building: uses intuitive high-level APIs for model reiteration and debugging.
• ML production: ease model training and deployment for on-premise, cloud, and on-device deployments.
• Flexible architecture: aids faster model publishing.

TensorFlow pros:

• Responsive community support
• Intuitive Python-based API
• Easy Machine Learning model visualization

TensorFlow cons:

• Duplicate and redundant methods
• Lacks in-depth documentation
• Difficult to install new versions

10. Natural Language Toolkit (NLTK)

Natural Language Tooklit (NLTK) is another popular open-source text analysis API that uses the Python library to process NLP queries. It uses powerful query tools like SQL to extract valuable information from texts. NLTK API is a great tool if you want to work with computational linguistics or natural language.

Natural Language Toolkit (NLTK) features:

NLTK API offers the following feature to ease how developers work with human language data to build Python programs.

• Corpora and lexical resources: ensure easy classification, tagging, parsing, stemming, tokenization, and semantic reasoning.
• Active discussion forum: helps solve queries and connect with fellow developers.

Natural Language Toolkit (NLTK) pros:

• Easy-to-use libraries
• Easy to implement
• Superior text cleaning and pre-processing

Natural Language Toolkit (NLTK) cons:

• Lacks comprehensive functionalities
• Slower lemmatization
• Algorithms need fine-tuning

11. TextBlob

TextBlob API is an efficient text analysis API for processing text data and executing NLP tasks. You can use this API to perform noun phrase extraction, classification, translation, sentiment analysis, POS tagging, and more.

TextBlob features:

Below are some of the features that the TextBlob API offers.

• n-grams
• Parsing
• Tokenization
• Noun phrase extraction
• Part-of-speech tagging
• Sentiment analysis
• Classification using Naive Bayes and decision tree
• Word and phrase frequencies
• Word inflection
• Spelling correction
• WordNet integration

TextBlob API pros:

• Intuitive library structure
• Smaller learning curve for new users

TextBlob API cons:

• Lacks complex sentiment analysis ability

12. PyTorch

PyTorch is an open-source API that helps developers to research prototypes and deploy productions faster with Machine Learning frameworks. This is your go-to API when you want a user-friendly front-end for building an efficient Deep Learning project.

PyTorch features:

PyTorch offers an excellent end-to-end Machine Learning framework with the following features.

• TorchScript: ensures flexible graph transitions for speed, functionality, and optimization.
• TorchServe: scales PyTorch model deployment across environments.
• Distributed training: optimizes research and production performance.
• Mobile: supports end-to-end workflows on iOS and android.
• Ecosystem: features tools and libraries for supporting diverse research areas.
• C++ front end: ensures high performance and low latency during research.
• Cloud support: eases frictionless development on major cloud platforms.

PyTorch pros:

• Developer-friendly
• Many trained functions and models
• Integrates various layers seamlessly

PyTorch cons:

• Issues with scalability
• Doesn’t work well with small data amount
• Smaller user community

Choosing the best text analysis API

Text analysis APIs can be a game-changer for deriving insights, crafting customer-centric experiences, and boosting productivity. Consider your company’s budget, technical expertise, and project scope before evaluating text analysis tools that fit your needs.

Levity helps you create custom Machine Learning models for extracting valuable insights from texts with minimal lift. The platform enables you to use no-code custom models to analyze data and prioritize business improvements.

Sign up to Levity to start harnessing the power of AI text analysis in your organization.

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Text Analysis FAQs

1. What is text analysis API?

A text analysis API uses Machine Learning and Natural Language Processing to analyze and extract relevant insights from textual data. These APIs enable companies to understand customers’ preferences and make strategic decisions.

2. What is text analysis used for?

Text analysis is used to extract actionable insights from unstructured data sources, such as:

• Chats
• Emails
• Surveys
• Documents
• Social media
• Customer testimonials
• Blogs, forums, and online media

Text analysis APIs parse these texts to discover easy-to-interpret data pieces and machine-readable facts for insights.

3. What is a sentiment analysis API?

Sentiment analysis APIs identify positive, negative, or neutral polarity in text strings. Sentiment analysis tools use natural language processing, computational linguistics, and text analysis to find subjective information in texts.