Entity Sentiment refers to the sentiment or emotional tone associated with a specific entity, such as a person, place, product, or concept, within a piece of text or conversation. It is a way of analyzing and understanding the sentiment expressed towards individual entities mentioned in a larger context.
Entity Sentiment analysis is often used in natural language processing (NLP) and sentiment analysis tasks to provide a more fine-grained understanding of opinions and emotions expressed in text. It can be valuable for businesses to identify which specific aspects of their products or services are being praised or criticized by customers, helping them make targeted improvements.
In some NLP models and tools, entity sentiment analysis may involve assigning sentiment scores or labels to each entity mentioned in a text, indicating whether the sentiment is positive, negative, or neutral.
Entity Sentiment APIs Use Cases
You can use Entity Sentiment in numerous fields, here are some examples of common use cases:
Customer Feedback Analysis: Businesses can use entity sentiment analysis to analyze customer reviews, survey responses, or social media comments to understand the sentiment associated with different aspects of their products or services. This helps in identifying areas for improvement and gauging overall customer satisfaction.
Product and Service Reviews: E-commerce platforms and review websites can use entity sentiment analysis to provide more detailed and informative product or service reviews. This allows customers to quickly assess how various features or aspects of a product are perceived by others.
Brand Monitoring: Companies can monitor online discussions and social media mentions of their brand to gauge public sentiment. This can help in reputation management and understanding how the brand is perceived by the public.
News and Media Analysis: News agencies and media companies can use entity sentiment analysis to understand public sentiment toward specific news topics, political figures, or events. This can help in shaping editorial content and assessing public opinion.
Market Research: Researchers can use entity sentiment analysis to analyze textual data from surveys, interviews, or focus groups. This can provide insights into public perception of various products, services, or trends.
Best Entity Sentiment APIs on the market
While comparing Entity Sentiment APIs, it is crucial to consider different aspects, among others, cost security and privacy. v experts at Eden AI tested, compared, and used many Entity sentiment APIs of the market. Here are some actors that perform well (in alphabetical order):
Amazon Comprehend can extract important words, phrases, locations, names, businesses, events, sentiments, and more from unstructured text.
Custom entity recognition enables you to recognize novel entity types that are not compatible with the pre-established generic entity types. This enables you to isolate entities that are particular to your firm to meet your needs.
Aylien's Entity Level Sentiment Analysis (ELSA) reliably predicts the emotion conveyed about each specific entity in the text, even though the sentiment is different for each.
When an entity is referenced more than once, the emotion of each mention will be evaluated, and an average will be determined for the title or body. Additionally, a confidence score is given to demonstrate the degree of the model's confidence in the prediction.
Entity Sentiment from Google Cloud makes it simple and quick to conclude a lot of open-text data. It may assist you in swiftly and simply gleaning insights from big amounts of open text data.
Each time an entity is mentioned, its sentiment is assessed and expressed as a numerical score and magnitude value. These results are then combined to provide an entity's overall emotion score and magnitude.
Performance variations of Entity Sentiment API
Entity Sentiment API performance can vary depending on several variables, including the technology used by the provider, the underlying algorithms, the amount of the dataset, the server architecture, and network latency. Listed below are a few typical performance discrepancies between several Entity Sentiment APIs:
Quality of Training Data: The accuracy of an Entity Sentiment API depends heavily on the quality and diversity of the training data used to develop the underlying model. APIs that have been trained on large, diverse datasets are likely to perform better than those with limited or biased training data.
Language Support: Some Entity Sentiment APIs may perform better in certain languages than others. The quality of entity recognition can vary significantly depending on the language, with some languages having better support and more accurate results than others.
Contextual Understanding: The ability of an API to understand the context of a text can impact its performance. APIs that can consider the surrounding words and phrases when identifying entities tend to perform better in complex sentences or documents.
Why choose Eden AI to manage your Entity Sentiment APIs
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 Entity Sentiment tasks in their cloud-based applications, without having to build their solutions.
Eden AI offers multiple AI APIs on its platform among several technologies: Text-to-Speech, Language Detection, Sentiment Analysis, Face Recognition, Question Answering, Data Anonymization, Speech Recognition, and so forth.
We want our users to have access to multiple Entity Sentiment 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 :
Fallback provider is the ABCs: You need to set up a provider API that is requested if and only if the main Entity Sentiment API does not perform well (or is down). You can use the confidence score returned or other methods to check provider accuracy.
Performance optimization: After the testing phase, you will be able to build a mapping of providers to optimize performance by selecting the right provider for each field (one provider for the payer, one for dates, one for amount, etc.) Each data that you need to process will then be sent to the Best Entity Sentiment API.
Cost - Performance ratio optimization: You can choose the cheapest Entity Sentiment provider that performs well for your data.
Combine multiple AI APIs: 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 Entity Sentiment APIs will validate and invalidate each other for each piece of data.
How Eden AI can help you?
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies.
Centralized and fully monitored billing on Eden AI for all Entity Sentiment APIs.
Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider.
Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines).
Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.