What is sentiment analysis?
Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and analyze emotional states and subjective information.
More simply put, it is the process of identifying and categorizing the emotional tone of a piece of text. It can be used to determine whether a writer's attitude is positive, negative, or neutral.
Why do developers need a sentiment analysis API?
Developers need to know about the best sentiment analysis APIs because they help developers understand the mood of their users. Developers can use these APIs to build analysis tools and reports for better insights and better decisions.
This article will cover the top sentiment analysis APIs. Developers should care about these APIs because they help developers understand the mood of their users. According to a study by Zendesk, 49% of users who are unhappy with a brand are likely to tell others about their bad experiences through social media. This is just one of many reasons why it's important for developers to understand the mood of their users.
The Top Sentiment Analysis APIs
We have found these APIs to be the very best at sentiment analysis:
- MeaningCloud Sentiment Analysis API
- Twinword Sentiment Analysis API
- Google Cloud Natural Language API
- IBM Watson Natural Language Understanding
- Aylien API
MeaningCloud Sentiment Analysis API
The MeaningCloud Sentiment Analysis API is a powerful tool for developers to understand the mood of their users. It provides detailed multilingual sentiment analysis that identifies the positive, negative, and neutral polarity in any text. This helps developers better understand what people are saying about their product or service and make better decisions based on this information.
The MeaningCloud Sentiment Analysis API also extracts sentiment at the document or aspect-based level. This means that it can distinguish between facts and opinions, as well as to detect irony and polarity disagreement. Developers can also define their own dictionaries and sentiment models to adapt the analysis to their specific needs. This makes the MeaningCloud Sentiment Analysis API an extremely versatile tool that is perfect for any developer who wants to better understand the mood of their users.
Check out their documentation here.
The MeaningCloud API charges by buckets of 500 words. Each bucket corresponds to one or two credits depending on the text language. You will be charged one credit per bucket for documents in standard languages. You will be charged two credits per bucket for documents in translated languages.
MeaningCloud's API is important to developers because it helps them understand the mood of their users. This is important because it can help developers make better decisions based on what people are saying about their product or service. The API also extracts sentiment at the document or aspect-based level, which can help developers distinguish between facts and opinions.
Twinword Sentiment Analysis API
Twinword's Sentiment Analysis is an important tool for developers to use in order to determine a text's sentiment. This API can be useful if you have a large number of user responses or reviews and want to quickly find the negative comments to see what your customers don't like and vice versa. The score indicates how positive, negative, or neutral the overall text analyzed is. Anything below a score of -0.05 we tag as negative and anything above 0.05 is tagged as positive.
Any text analyzed with this API can be interpreted in different ways, so developers can create their own negative and positive minimum scores. Using the Twinword API can help developers make better products by understanding how customers feel about them.
Other features of their APIs include:
- text classification
- emotion analysis
- topic tagging
- text similarity
- word associations
- and much more
This API has a pay-as-you-go pricing model, with different monthly request limits for each plan. The Basic Plan allows for 500 requests per month, while the Pro Plan allows for 125,000 requests per month. The Ultra Plan allows for 750,000 requests per month, and the Mega Plan allows for 2,000,000 requests per month.
Google Cloud Natural Language API
Google's Natural Language API is a powerful tool for developers that allows them to analyze and understand the data behind text with ease. The API can identify the sentiment of text, as well as the various entities within it. This information can be used by developers to create more intelligent applications that can better understand human language. Additionally, the API is part of Google's larger Cloud Machine Learning API family, which provides a wide range of machine learning tools and services. This makes the Natural Language API a valuable addition to any developer's toolkit.
The Cloud Natural Language API can also analyze sentiment directly on a file located in Cloud Storage. This eliminates the need to send the contents of the file in the body of your request. This makes it easier and faster for developers to get started using the API.
Google Cloud's natural language processing API can also be used for other tasks such as entity analysis and content classification. Entity analysis identifies specific entities within a text, such as people, organizations, or locations. This information can be useful for understanding the text as a whole. For example, if you were analyzing customer feedback, entity analysis could help you determine which aspects of the product people are most satisfied and dissatisfied with. Content classification assigns a category to a piece of text, such as news, sports, or entertainment. This information can be used to help organize and understand data behind text.
Google Cloud's Natural Language API is priced based on the number of units analyzed. For every 1,000 Unicode characters in a document (including whitespace characters and any markup characters such as HTML or XML tags), one unit is charged. Prices for the usage of the API are computed monthly and vary depending on which feature of the API is used.
For sentiment analysis, the first 5000 units are free, then it goes up from there based on the pay-as-you-go pricing.
Microsoft Text Analytics API
The Microsoft Azure Text Analytics API is a cloud-based service that allows developers to analyze unstructured text using natural language processing (NLP). This API offers a variety of features for understanding customer opinions, identifying key phrases and entities, and classifying medical terminology. Additionally, the sentiment analysis feature allows developers to examine positive or negative sentiments around specific topics. The Text Analytics API is important for developers because it provides a way to easily analyze large amounts of unstructured text data with little or no machine learning expertise. The API also has language support, allowing it to be used in applications that need to handle text in multiple languages.
The broad entity extraction feature of the Microsoft Azure Text Analytics API allows developers to identify important concepts in any text, including key phrases and named entity recognition. The power of Microsoft Azure Text Analytics API's key phrase extraction feature is that it allows developers to quickly evaluate and identify the most important points in unstructured text.
Microsoft has a few examples using various programming languages here.
The Microsoft Text Analytics API is priced based on the number of text records analyzed. For every 1,000 characters in a document (including whitespace characters and any markup characters such as HTML or XML tags), one text record is charged. Prices for the usage of the API are computed monthly and vary depending on which feature of the API is used.
For sentiment analysis, the first 5000 text records are free, then it goes up from there based on the pay-as-you-go pricing.
IBM Watson Natural Language Understanding
IBM Watson Natural Language Understanding is a service that uses deep learning to extract meaning and metadata from unstructured text data. This allows developers to get underneath their data using text analytics to extract categories, classification, entities, keywords, sentiment, emotion, relations, and syntax. Additionally, the sentiment towards a specific target phrase or the document as a whole can be analyzed. This is an important tool for developers as it allows them to better understand their data and make informed decisions based on that information.
As a developer, understanding your data is essential to making good decisions about what to do with it. With IBM Watson Natural Language Understanding, you can extract meaning and insights from unstructured text data, giving you a better understanding of what your data is telling you. This can help you make more informed decisions about how to proceed with your project. Additionally, the sentiment analysis functionality can be helpful in determining whether the target audience for your product is likely to have a positive or negative reaction.
Check out their docs here.
IBM Watson has 2 plans: Lite and Standard. The Lite model offers the first 30k items free. The Standard model is a paid plan that starts at $0.003/item, but rates get discounted as you increase your volume. You can find more information on pricing here.
The Aylien API is a powerful tool for developers who want to build intelligent applications and models with real-time access to enriched, tagged, and structured news feeds. The API provides industry-leading sentiment analysis at the document and entity level, as well as other features that make it easy for developers to get the most out of news content. With the Aylien API, developers can quickly and easily get the data they need to create innovative applications that make use of news content.
The Aylien API pricing starts at $49.00/month, but they do offer a free trial so that developers can try out the API before deciding to purchase it. The pricing is reasonable, and it's a great option for developers who want to make use of news content in their applications.
Repustate is a text analytics API that uses natural language processing to help developers extract insights from unstructured data. This can be helpful in understanding customers and employees on a deeper level, as well as getting insights from social media, YouTube, and TikTok. Use the API to get real-time reporting with actionable, visual data that anyone can understand. Additionally, the API supports 23 languages, so it's great for developers who want to target a global audience. (However, the API does not offer language detection and you'll have to specify a specific language).
The Repustate API is a powerful tool for developers who want to build intelligent applications with real-time access to enriched, tagged, and structured news feeds. The API provides industry-leading sentiment analysis at the document and entity level, as well as other features that make it easy for developers to get the most out of content.
The sentiment analysis algorithm determines if a chunk of text is positive, neutral, or negative. It uses natural language processing (NLP) techniques such as part-of-speech tagging, named entity recognition, lemmatization, prior polarity, negations, and semantic clustering.
Check out their documentation.
Conclusion: What is the best sentiment analysis API?
Sentiment APIs are a great way for developers to extract insights from unstructured data. By using natural language processing, these APIs can help developers understand customers and employees on a deeper level, as well as get insights from social media.
All of the sentiment analysis APIs on this list are great options for developers who want to make use of text analysis and analytics in their applications. They all offer a variety of features that make it easy for developers to get insights from unstructured data. Additionally, most APIs support multiple languages, so they're great for global audiences.
If you're looking to add some extra functionality to your web or mobile app, be sure to check out the APIs available on AbstractAPI.com. Some of our best APIs include IP Geolocation, Email Validation, and Phone Validation, so you can be sure you're getting the most accurate data possible.
Which model is best for sentiment analysis?
The most recent, efficient, and popular technique for sentiment analysis is hybrid sentiment, analysis models. You can actually obtain the advantages of both automatic and rule-based systems if you build well-designed hybrid solutions. Hybrid algorithms may combine the power of machine learning with the freedom to customize.
Is there a difference between natural language processing and sentiment analysis?
Yes, there is a difference between natural language processing and sentiment analysis. Natural language processing helps developers understand the structure and meaning of the text, while sentiment analysis determines the feeling or opinion of the text. This can be helpful for understanding customers and employees on a deeper level, as well as getting insights from social media.
What are some use cases for sentiment analysis APIs?
Some use cases for sentiment analysis APIs include understanding customer feedback, analyzing employee sentiment, and understanding public opinion on a topic. Additionally, some of these APIs can be used to get insights from social media, YouTube, and TikTok.