Detect the sentiment present in customer messages accurately
Updated
Use Sprinklr’s Sentiment Analysis model and identify the sentiment present in messages accurately, extracting key information that drives critical business decisions.
Sprinklr Sentiments Analysis model reads messages in context and extracts opinions/sentiments from the text it reads and categorizes them as positive, neutral, or negative. The model is trained using huge volumes of pre-labeled datasets which enables the model to predict accurately almost any kind of text.
Our Sentiment Analysis model is powered by Sprinklr’s in‑house Large Language Model (LLM), trained on a combination of synthetic and open‑source data to generalize effectively without requiring domain‑specific retraining. The model can be further refined through prompt optimization to improve accuracy and relevance. Sprinklr makes 10 billion predictions per day with an accuracy level of > 80 %. The model is designed to read messages in context and extract opinions/sentiments from the text it reads and categorizes them as positive, neutral, or negative.
Use cases of Sentiment Analysis
Gives you real-time insight into the brand's health by showing current sentiment towards the brand/topic.
Helps you measure product feature performance.
Helps you track the sentiments of major influencers of your brand.
Helps you create and monitor your campaigns by analyzing the sentiments attached to them.
Helps you create customizable dashboards to show current sentiment toward the brand.
Helps you keep track of major influencer accounts.
Helps you manage potential crises conversation before they get viral and consequential. Sprinklr’s Sentiment Analysis enables you to make use of Smart Alerts to send you alerts if a topic is getting a lot of messages with negative sentiments.
Helps you prioritize messages in order to engage better with your customers.
Helps you compute satisfaction scores for your customers.
Note: This capability is enabled by default for all users.
Sprinklr's Sentiments Analysis algorithm
Sprinklr's Sentiment Analysis is powered by an In-house Large Language Model (LLM) trained on diverse, multilingual datasets. The model uses transformer based architecture and in-context learning to deeply understand language, sarcasm and humor to classify messages as positive, neutral, or negative.
Following is a step-by-step description of how our Sentiment Analysis model works:
Data Collection: Sprinklr's In-house LLM is trained on a comprehensive dataset combining synthetically curated data and high quality open-source datasets. The synthetic data was specifically generated to replicate failure cases observed in older models such as misclassified sarcasm, humor, and nuanced sentiment ensuring the model is robust across edge cases. This is complemented by diverse open-source data spanning multiple domains, languages and tasks, giving the model broad generalization capability.
Prompt-Based Adaptation: Rather than retraining the model for each partner or domain, Sprinklr tailors the LLM's behavior using natural language prompts. These prompts define the classification guidelines, relevant patterns, and any partner specific context the model should consider during inference. This approach significantly reduces deployment time and eliminates the need for extensive fine tuning when onboarding new partners or expanding to new domains.
Training the Model: Unlike traditional ML models that require iterative parameter adjustments on labeled datasets, our LLM is trained once on this comprehensive dataset using supervised learning. The training process focuses on enhancing language understanding, tone detection (including sarcasm and humor) and instruction following. The result is a single, domain agnostic model that performs accurately across varied industries and use cases without needing separate models for each.
Feedback Analysis: You can give feedback on the sentiment of a message on the dashboard and make corrections. This feedback is stored in the backend.
Note:
Since Sprinklr's sentiment model is LLM driven, classification quality is influenced by how clearly guidelines are defined. Avoid vague instructions (e.g., 'strong negative sentiment') and instead provide deterministic, explicit criteria. All relevant patterns must be included in the configuration for accurate results.
The model generates predictions based solely on the content of the message. No external context or assumptions are applied during inference — what's in the message text is what the model evaluates.
Languages covered
Sprinklr Sentiment Analysis works across 100+ languages. We deploy different algorithms for different languages. Our Sentiment Analysis has an accuracy level of more than 80% in the languages we support.
Following is the list of supported languages –
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