In this article, we will introduce our top 10 Sentiment Analysis APIs and how to choose and access the right engine according to your data.
Sentiment Analysis (or Opinion Mining) is a Natural Language Processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment Analysis is often performed on textual data to help companies monitor brand and product perception in customer feedback and understand customer needs.
The origin of Sentiment Analysis can be traced to the 1950s, when Sentiment Analysis was primarily used on written paper documents.
Sentiment Analysis engines appeared in the early 2000s and became increasingly popular due to the abundance of data from social networks, especially those provided by Twitter.
Today, however, it is widely used to mine subjective information from content on the Internet, including texts, tweets, blogs, social media, news articles, reviews, and comments.
Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. The API processes any text file in UTF-8 format, and semi-structured documents, like PDF and Word documents. It develops insights by recognizing the entities, key phrases, language, sentiments, and other common elements in a document.
Connexun offers a Sentiment Analysis API which uses a combination of advanced text vectorization and machine learning classifiers to accurately evaluate the sentiment of text in multiple languages. Their API also provides the ability to analyze sentiment of entities based on their context. Models are trained on human-labeled datasets created by Connexun, ensuring high-quality results.
Dandelion API is a set of semantic APIs to extract meaning and insights from texts in several languages (Italian, English, French, German and Portuguese). It’s optimized to perform text mining and text analytics for short texts, such as tweets and other social media. Dandelion API extracts entities (such as persons, places and events), categorizes and classifies documents in user-defined categories, augments the text with tags and links to external knowledge graphs and more.
Emvista provides a robust solution for sentiment analysis, with a focus on detecting and explaining sentiment accurately. The company offers Text Radioscope, a web-based tool that allows users to visualize emotions and other information present in text from a variety of sources, such as Twitter feeds, support tickets and email boxes. Sentiments, keywords, concepts and opinions detected in text are presented in graphs, histograms and word clouds, allowing users to better understand the data.
Powered by Google's machine learning models, the API is trained on a large dataset of annotated text, enabling it to accurately identify sentiment even in complex sentences. Additionally, the API can analyze sentiment in multiple languages and identify entities and categories in the text, providing further insights.
The API enables users to train their own models to tailor the sentiment analysis to their specific needs and supports sentiment analysis in multiple languages. Additionally, the API offers contextual analysis, taking into account the tone, emotion, and writing style of the text to provide a more nuanced understanding of the sentiment.
Lettria provides an advanced sentiment analysis platform specifically designed to process textual data. Its sentiment analysis features deliver high accuracy and enable customization to meet specific business and industry needs. Lettria is able to address any use case where sentiment analysis is applied.
The API offers fine-grained sentiment analysis, which enables it to detect positive, negative, or neutral sentiment with greater accuracy. Additionally, Microsoft Azure's API enables sentiment analysis in multiple languages.
One AI's sentiment analysis API leverages the power of NLP to deliver highly accurate and insightful analysis of text-based data. This API is the perfect tool for businesses looking to gain a deeper understanding of their customers' opinions, sentiments, and emotions. With features such as customizable sentiment dictionaries, fast processing times, and seamless integration into existing systems, One AI's sentiment analysis API is the good choice for organizations that want to stay ahead of the curve.
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OpenAI's sentiment analysis API uses deep learning algorithms to provide accurate and insightful analysis of text-based data. With the ability to process large volumes of text, the API returns a sentiment score indicating the overall positivity, negativity, or neutrality of the text.
spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pre-trained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.
You can use Sentiment Analysis in numerous fields. Here are some examples of common use cases:
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Sentiment Analysis tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Summarization, Question Answering, Data Anonymization, Speech recognition, and so forth.
We want our users to have access to multiple Sentiment Analysis engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs:
You need to set up a provider API that is requested if and only if the main Sentiment Analysis API does not perform well (or is down). You can use confidence score returned or other methods to check provider accuracy.
After the testing phase, you will be able to build a mapping of providers performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best Sentiment Analysis API.
You can choose the cheapest Sentiment Analysis provider that performs well for your data.
This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because Sentiment Analysis APIs will validate and invalidate each other for each piece of data.
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
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You can see Eden AI documentation here.
The Eden AI team can help you with your Sentiment Analysis integration project. This can be done by :
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