How does Sprout determine sentiment?

This article explores Sprout Social's sentiment analysis feature, detailing how it automatically categorizes incoming messages (positive, negative, or neutral). It highlights the benefits of this feature for users, including prioritizing responses, assessing brand health, monitoring customer satisfaction, and proactively identifying potential crises.

Sentiment Analysis Explained

Sprout's sentiment analysis is a feature that helps you understand the emotion behind messages you receive. It automatically categorizes incoming messages as positive, negative, or neutral, enabling you to prioritize and respond to messages more effectively. This is particularly useful for gauging brand health, understanding customer satisfaction, and identifying potential crises.

How Sprout Determines Sentiment

Sprout uses a sophisticated machine learning model called a Deep Neural Network (DNN) to determine sentiment. 

Deep Neural Networks accurately determine which parts of text are more or less important in determining a classification like sentiment. For example, in the sentence "I really loved the pizza," the word "loved" is the most important word that determines sentiment.

When a Deep Neural Network (DNN) collects enough data and tags as positive, negative or neutral, the DNN can automatically figure out which words or phrases are the most relevant and ignore the rest. A DNN can learn a language’s nuance and structure with very little additional information from humans.

Here's how it works:

• Data Analysis: The DNN analyzes the text of a message, identifying key words and phrases that indicate sentiment.
• Probability Scoring: It then calculates a probability score for each of the three sentiment categories: positive, negative, and neutral.
• Sentiment Assignment: The message is assigned the sentiment with the highest probability score.
• Example: If a message says, "I was expecting to be disappointed, but the pizza was amazing!", the DNN  recognizes the positive sentiment in "amazing!" as more significant than the negative sentiment in "disappointed" and classify the message as positive.

Sentiment Categories

• Positive: Indicates a favorable or happy sentiment.
• Negative: Indicates an unfavorable or unhappy sentiment.
• Neutral: Indicates a sentiment that is neither positive nor negative, such as a question or a factual statement.
• Unclassified: This category is used when the sentiment of a message cannot be determined.

Handling Unclassified Sentiment

You may see some messages with an "unclassified" sentiment. This can happen for several reasons:

• Unsupported Language: The language of the message is not yet supported by Sprout's sentiment analysis.
• Media-Only Content: The message contains only an image, video, or link with no accompanying text.
• Ambiguous Language: The text is too short or ambiguous for the DNN to make an accurate determination.

What to Do with Unclassified Messages

If you encounter an unclassified message, you can manually reclassify it. This not only helps you categorize the message correctly but also helps to train Sprout's machine learning model to make more accurate predictions in the future.

For more detailed instructions on how to reclassify sentiment for single or multiple messages, refer to the article Reclassifying Listening Sentiment.

FAQs

What is sentiment analysis and how can it help me?

Sentiment analysis is a tool that automatically categorizes messages as positive, negative, or neutral. It helps you understand customer emotions, prioritize messages, track brand health, and identify potential crises.

How does Sprout Social determine the sentiment of a message?

Sprout uses a Deep Neural Network (DNN) to analyze text. The DNN calculates a probability score for positive, negative, and neutral labels and assigns the one with the highest probability.

What is a Deep Neural Network and how does it relate to sentiment analysis?

A Deep Neural Network (DNN) is a type of machine learning model that can learn the nuances of language. In sentiment analysis, it identifies which words and phrases in a message are most important for determining the overall sentiment.

What are the different sentiment categories in Sprout Social?

The sentiment categories are Positive, Negative, Neutral, and Unclassified.

Why are some of my messages showing as "unclassified" sentiment?

Messages can be "unclassified" if the language is not supported, the message only contains media (like an image or link), or the text is too ambiguous for the system to make an accurate determination.

What should I do if a message has an "unclassified" sentiment?

You can manually reclassify the sentiment of the message. This helps with categorization and also trains the machine learning model to be more accurate in the future.

Can I manually change the sentiment of a message?

Yes, you can manually change the sentiment of any message in Sprout.

How do I reclassify the sentiment for a single message?

To reclassify a single message, you can click on the sentiment icon or label associated with that message and select the correct sentiment from a dropdown menu.

Can I bulk-reclassify the sentiment for multiple messages at once?

Yes, Sprout enables you to select multiple messages and reclassify their sentiment in bulk.

Does reclassifying messages help improve the accuracy of Sprout's sentiment analysis?

Yes, when you reclassify messages, you are providing feedback to the machine learning model, which helps it learn and improve its accuracy over time.

Does Sprout's sentiment analysis support languages other than English?

Yes, Sprout's sentiment analysis supports multiple languages, though the list of supported languages may vary.

Does sentiment analysis work for messages that only contain images or videos?

No, if a message only contains media with no accompanying text, it will typically be marked as "unclassified."

How accurate is the sentiment analysis provided by Sprout?

Sprout's sentiment analysis is highly accurate due to its use of a Deep Neural Network. However, accuracy can be further improved by manually reclassifying messages and using Sentiment Rulesets.

How can I use sentiment analysis to track my brand's health?

By monitoring the overall sentiment of messages related to your brand over time, you can identify trends, gauge customer satisfaction, and get a general sense of your brand's public perception.

Can I filter my incoming messages based on their sentiment?

Yes, you can filter messages in the Smart Inbox and other views based on their sentiment to focus on specific types of feedback, such as only positive or only negative comments.

Does Sprout's sentiment analysis understand nuances like sarcasm or industry-specific jargon?

While no sentiment analysis is perfect, Sprout's Deep Neural Network is designed to understand language nuances. Sarcasm remains a challenge for all sentiment analysis tools, but the models are continually improving.