Sentiment and Emotion Analysis
Understand your audience's emotions—exactly, in context, and in any language
Human communication is made up of layers of emotions and sentiment, often found between words. We help your AI models understand not only the communication but also the emotions behind both the content and intent. We provide Sentiment and Emotion Analysis services with tagged, multilingual data to train and test models to capture emotions, opinions, and sentiments across contexts, cultures, and languages.
From customer feedback to social media conversations, our approaches allow businesses to capture emotional intentions, forecast behavioral tendencies, and build empathetic AI systems.
Our Services
We provide high-quality tagged datasets, emotion taxonomies, and sentiment lexicons tailored to your specific industry, language and use case.
Multilingual Sentiment Annotation
We provide sentiment annotation for data in over 100 languages to tag polarity (positive, negative, neutral), intensity (mild to strong), and context (irony, sarcasm, ambiguous).
Emotion Classification & Tagging
We label emotional states using standard and custom emotion models, from Ekman's six emotions to Plutchik's wheel, including joy, anger, fear, sadness, surprise, trust, and more.
Domain-Specific Sentiment Datasets
We obtain and create sentiment-rich datasets from domain-specific sources, including product reviews of specific brands, financial reporting analysis, speeches, and healthcare discussion forums, to gain contextual accuracy.
Aspect-Based Sentiment Analysis (ABSA)
We detect and label sentiment specific to entities, attributes and characteristics (such as "battery life" or "customer support"), allowing for a nuanced interpretation of sentiment.
Cultural Nuance & Sarcasm Detection
We have linguists who are native to a language and partner with organizations to develop local sentiment frameworks to ensure cultural meaning, humor, and sarcasm are properly identified and labelled.
Real-Time Sentiment Streaming Support
We offer real-time data sentiment tags for chats, live-streams and customer support mapping for organizations needing quick feedback/evidence to identify or create sentiment dashboards and alerting systems.
Applications in Industries
E-commerce and Retail
Product reviews, brand sentiment and competitor sentiment.
Healthcare
Patient emotion analysis, chatbot development for therapy.
Finance
Market sentiment based on news, reports, social media sentiment.
Media and Entertainment
Audience and sentiment engagement to their content and campaigns.
Customer support
Detect dissatisfaction or potential escalation in engagements.
Public Policy and Politics
Assess changing and trending voter sentiment and citizen emotion in real-time.
Our Sentiment and Emotion Analysis Workflow
We take a serious step-by-step approach to make sure your AI systems are learning from more accurate, relevant, and culturally relevant emotional intelligence data.
Requirement Understanding
We start with the overall end use case, target demographic, languages to include, and the granularity of sentiment or emotional depth needed. We work closely with your data science and product teams to define the emotion categories, polarity, and any linguistic rules.
Outcome: A sentiment analysis framework that is aligned with your AI model objectives.
Data Collection & Source Identification
We'll procure raw text, audio or video data from either publicly available datasets or through your own data, depending on your domain, geography, and users. Examples of sources could include social media, call transcripts, email, customer/ product reviews, or news content.
Outcome: A collection of emotionally diverse and ethically sourced data to analyze.
Sentiment & Emotion Annotation
Using meaning and speaker-based sentiment and emotion labels that we have defined and provided, our team of annotators will apply the labels efficiently. Our annotators consist of proficient native linguists and subject matter experts. Since we use multiple annotators for each dataset, we can have consistent labeling and reduce bias in annotating the data samples. Each sample will be labelled according to tone, sentiment, emotion, intensity of emotion, and classification of an entity, where applicable.
Outcome: A dataset tagged with sentiment labels that captures the spirit of actual human communication.
Quality Assurance & Validation
Our QA process is multi-layered and includes inter-annotator agreement checks, audits on randomly sampled coding, and expert review for coding quality to ensure accuracy and consistency. Part of sentiment calibration is addressing borderline (and ambiguous) sentiment cases to maintain and promote standardization.
Outcome: A dataset with high accuracy, for situations that may improve the model's usefulness, or minimize the likelihood of false assumptions of use by others.
Deliverables, Formatting & Integration
We will provide the annotated datasets in the format of your choice (CSV, JSON, XML, etc.), including documentation that outlines which sentiments in the taxonomy we included a tagging methodology for. We will provide all the integration support you will need to use the data in your model training pipelines or within your analytics engines.
Outcome: Structured sentiment data that uses the technology stack described above, ready for use in your operations.
Continuous Feedback & Updates
As part of our ongoing support model, we offer regular updates based on model performance, resulting in enhancements dataset, retraining of the documents, or expansion of the categories we use. Further, we provide a mechanism to track the evolution of emotion, so that your models can learn from emerging ways of expressing emotion and adapt to shifts in societal culture over time.
Outcome: A dynamic approach to sentiment analysis that evolves with your AI and the people that interact with it.Why Should You Choose Crystal Hues?
Multilingual Capability Across 200+ Languages
Thanks to our multilingual workforce, we can accurately tag sentiment in any geography, language, or cultural nuance.
Understand Cultural Context & Nuance
We train our annotators to recognize sarcasm, idioms, humor and cultural sentiment to add richness to model learning beyond the literal.
Flexible with Custom Taxonomies & Deep Emotion Models
We support both customary emotion frameworks and bespoke emotion frameworks that fit the underlying emotional complexity of your use case.
Domain Experts in Specific Sentiment Understandings
We bring domain specific experience in a number of sectors, including healthcare, fintech, retail, and government, to enhance your sentiment experience.
Robust & Scalable QA Processes
With our quality assurance processes and trained workforce, we can provide you with large quantities of annotated sentiment data while still adhering to internal accuracy levels.
What You'll Get Working with Us
Industry-specific emotional and sentiment datasets
Native-level multilingual coverage
Quality-checked data in structured, model-ready formats
Custom emotion taxonomies and in-depth annotations
Easy integration, delivery and scaling assistance
With Crystal Hues, your AI won't just work with data; it'll understand human emotion.
Contact us today to develop emotionally aware, culturally aligned, and sentiment aware AI systems.