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In this article, we are going to see how we can easily integrate a Sentiment Analysis engine in your project and how to choose and access the right engine according to your data.
Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment 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, sentiment analysis is widely used to mine subjective information from content on the Internet, including texts, tweets, blogs, social media, news articles, reviews, and comments.
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
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ParallelDots provides Komprehend AI APIs that are a comprehensive set of document classification and NLP APIs for software developers. Their NLP models are trained on more than a billion documents and provide state-of-the-art accuracy on most common NLP use-cases such as named entity recognition, sentiment analysis and emotion detection.
The Cloud Natural Language API provides natural language understanding technologies to developers, including sentiment analysis, entity analysis, entity sentiment analysis, content classification, and syntax analysis. This API is part of the larger Cloud Machine Learning API family. Each API call also detects and returns the language, if a language is not specified by the caller in the initial request.
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Meaning Cloud provides text analytics products to extract the most accurate insights from any multimedia content in many languages. And they do it SaaS and On-prem. They work for different industries (pharma, finance, media, retail, hospitality, telco, etc.) developing personalized and industry-oriented solutions. Meaning Cloud sentiment analysis API performs a detailed, multilingual sentiment analysis on information from different sources.
At Lettria, they are building a new NLP paradigm, so that previously unstructured data becomes relevant and valuable. Lettria empowers data scientists and developers with ready-to-use and ultra-performant NLP/NLU API. They also enable business managers to structure and leverage their databases, integrating NLP & knowledge graphing technologies directly into the softwares they use everyday. Lettria provides many features such as sentiment analysis, keyword extraction, NER, POS tagger, synthesis, language detection, etc.
Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents. Amazon Comprehend 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.
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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.
IBM Natural Language Understanding is a collection of APIs that offer text analysis through natural language processing. This set of APIs can analyze text to help you understand its concepts, entities, keywords, sentiment, and more. Additionally, you can create a custom model for some APIs to get specific results that are tailored to your domain.
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MonkeyLearn is a Text Analysis platform with Machine Learning to automate business workflows and save hours of manual data processing. They provide pre-built NLP APIs adapted to use cases such as entity extraction, sentiment analysis, text classification, etc. With MonkeyLearn you can also train custom machine learning models to get topic, sentiment, intent, keywords and more.
Intellexer is a linguistic platform which incorporates powerful linguistic tools for analyzing text in natural language and provides effective capabilities for ...
Intellexer Sentiment Analyzer is a powerful and efficient solution that automatically extracts sentiments (positivity/negativity), opinion objects and emotions (liking, anger, disgust, etc.) from unstructured text information. Besides, Intellexer Sentiment Analyzer can be successfully used for document sentiment classification and review rating prediction tasks.
Allganize provides Natural Language Understanding API and conversational AI for enterprises. It also helps businesses automate workflows by natural language understanding AI. It provides insight into what your teammates are working on, as well as overarching work patterns/trends in your team. Allganize leverages deep learning-based, high-performance NLU technology to enable companies of all sizes to apply AI technology to develop their own AI systems and services. The company provides real-time customer and project related information.
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:
When you need a Sentiment Analysis engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Sentiment Analysis. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Sentiment Analysis engines with a provider as a simple parameter. With only a few lines, you can set up your project in production:
Test and API:
Here is the code in Python (GitHub repo) that allows to test Eden AI for sentiment analysis:
Eden AI also allows you to compare these engines directly on the web interface without having to code:
There are numerous Sentiment Analysis engines available on the market: it is impossible to know all of them, to know those who provide good performance. The best way you have to integrate Sentiment Analysis technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
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