Sentiment and Emotion Analysis API

What is Sentiment Analysis?

Sentiment analysis is the process of extracting a general sentiment from a block of text. Basically it's about determining whether the text is positive or negative.

Generative AI models like ChatGPT, GPT-3.5, GPT-4, LLaMA 3, Yi 34B, and Mixtral 8x7B, are very good at performing sentiment analysis and emotion analysis.

For example, let's imagine our program finds the following Twit:

Look what's just come on the market in #ValThorens! A recently renovated, charming 6 bed duplex apartment in the heart of the resort with superb views!

This is a commercial Twit that clearly shows a positive sentiment.

The Natural Language Processing model in charge of sentiment analysis would return the main sentiment and its likelihood. Here we would get a positive sentiment with a high likelihood.

What is Emotion Analysis?

Emotion analysis is about detecting one or several emotions from a block of text: sadness, joy, love, anger, fear, surprise...

The Natural Language Processing model in charge of emotion analysis would return each emotion together with its likelihood.

Sentiment analysis and emotion analysis can be achieved with generative AI models like GPT-4 or GPT-3.5 but also but open-source alternatives like LLaMA 3, Mixtral 8x7B, Yi 34B, and more. On NLP Cloud you can perform sentiment analysis and emotion analysis either with small and fast models like DistilBERT or with larger generative AI models like LLaMA 3, Mixtral 8x7B, or Yi 34B. tr%

Sentiment analysis

Why Use Sentiment/Emotion Analysis?

Sentiment and emotion analyses can be interesting in many situations. Let's give you a couple of examples.

Social Network Analysis

Imagine you're working in a marketing department that is regularly posting new content on social networks. You might want to automatically monitor the user reactions in order to quickly intervene in case of negative feedback.


Some support requests might be more urgent than others, depending on how angry users are. Detecting the user's sentiment automatically can help support address critical tickets more quickly.

Public Relations

Gauging the sentiment of a couple of persons on the internet is easy, but understanding the global sentiment of thousands of persons is another thing. Automated sentiment analysis is the key solution here.

Product Launch

Right after launching a new product, it can be critical to react quickly in case of poor reception by customers, bloggers, journalists... Sentiment analysis can help in such situations.

NLP Cloud's Sentiment/Emotion Analysis API

NLP Cloud proposes a sentiment analysis API that allows you to perform sentiment analysis and emotion analysis out of the box, based on DistilBERT Base Uncased Finetuned SST-2, Distilbert Base Uncased Emotion, Prosus AI's Finbert, LLaMA 3, Mixtral 8x7B, Yi 34B, and more. They are very good alternatives to ChatGPT, GPT-3.5, and GPT-4. The response time (latency) is very low for the DistilBERT and Finbert models. Accuracy is higher with generative models like LLaMA 3, Mixtral 8x7B and Yi 34B. You can either use the pre-trained model or train your own model, or upload your own custom models!

For more details, see our documentation about sentiment analysis here. For advanced usage, see the text generation API endpoint here. And easily test sentiment analysis on our playground.

Testing sentiment/emotion analysis locally is one thing, but using it reliably in production is another thing. With NLP Cloud you can just do both!

Frequently Asked Questions

What is sentiment analysis?

Sentiment analysis is the computational process of identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic or the overall contextual polarity of the text is positive, negative, or neutral. It is widely used in fields like marketing, social media, and customer service to analyze feedback and public opinion.

How does emotion analysis differ from sentiment analysis?

Emotion analysis focuses on identifying and analyzing the range of human emotions, such as happiness, sadness, anger, or fear, from textual data. In contrast, sentiment analysis primarily categorizes text into positive, negative, or neutral sentiments, often overlooking the specific emotions involved.

How are sarcasm and irony handled in sentiment analysis?

In sentiment analysis, sarcasm and irony are challenging to detect because they often involve saying something positive while meaning the opposite, or presenting a situation in an unexpected light that contrasts with literal interpretation. Advanced techniques such as context analysis, linguistic feature recognition, and machine learning models trained on large datasets incorporating sarcastic and ironic expressions are employed to identify and correctly interpret these nuances.

Can sentiment analysis detect neutral sentiments?


How does sentiment analysis impact customer service and support?

Sentiment analysis significantly enhances customer service and support by rapidly identifying and categorizing customer emotions and opinions from their feedback, allowing businesses to address concerns, improve services, and personalize responses. This drives better customer satisfaction and loyalty by ensuring timely and relevant engagement based on the sentiments customers express.

In what ways can businesses utilize sentiment analysis to make data-driven decisions?

Businesses can leverage sentiment analysis to understand customer opinions and emotions toward their products or services, allowing them to improve offerings, tailor marketing strategies, and enhance customer service. Additionally, sentiment analysis can provide insights into market trends and competitor performance, enabling strategic decisions to increase market share and profitability.

What role does sentiment analysis play in social media monitoring?

Sentiment analysis plays a crucial role in social media monitoring by helping businesses and organizations understand public opinion and emotional responses towards their brand, products, or services. It allows for the identification and assessment of positive, negative, and neutral sentiments in social media content, enabling more informed and strategic decision-making.

How can sentiment analysis improve marketing strategies?

Sentiment analysis can improve marketing strategies by enabling companies to understand consumer emotions and opinions toward their products or services in real-time, allowing for quick adjustments or targeted messages. This insight can help tailor marketing messages more effectively, enhancing customer engagement and loyalty.

Can sentiment analysis be used to predict market trends?

Yes, sentiment analysis can be used to predict market trends by analyzing the mood or opinions of the public towards specific products, services, or companies. By gauging overall sentiment, businesses and investors can make more informed decisions potentially predicting market movements.

How to evaluate the accuracy of sentiment analysis?

To evaluate the accuracy of sentiment analysis, one commonly uses a confusion matrix to calculate metrics such as precision, recall, and the F1 score, which offer insights into how well the AI model distinguishes between classes. Additionally, accuracy can be directly assessed by dividing the number of correct predictions by the total number of predictions made by the model.

What languages does your AI API support for sentiment/emotion analysis?

We support sentiment/emotion analysis in 200 languages

Can I try your sentiment/emotion analysis API for free?

Yes, like all the models on NLP Cloud, the sentiment/emotion analysis API endpoint can be tested for free

How does your AI API handle data privacy and security during the sentiment/emotion analysis process?

NLP Cloud is focused on data privacy by design: we do not log or store the content of the requests you make on our API. NLP Cloud is both HIPAA and GDPR compliant.