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It is rightly said that good business always listens to its customers, but the question is do they truly understand them? Understanding human sentiments, emotions, or intent is often considered difficult. The solution? Sentiment Analysis – It is a technique to deduce, gauge, or understand the image your product, service, or brand carries in the market.
According to a study, 360,000 tweets are tweeted every minute.
40% of the employees receive between 26-75 emails per day.
Your customers tell you how they feel every day — in reviews, social posts, support tickets, and call recordings. Sentiment analysis services turn that raw feedback into labeled training data, so your AI models learn to read emotion, opinion, and intent the way a human would.
Shaip helps you build sentiment models that actually hold up in production. Our human annotators label your text and voice data across 60+ languages, capturing the nuance — sarcasm, dialect, and aspect-level meaning — that off-the-shelf models tend to miss.
focuses on the reviews your brand receives online (positive, neutral, and negative)
focuses on the emotion your product or service kindles in the minds of your customers (happy, sad, disappointed, excited)
focuses on the immediacy of using your brand or finding out an effective solution to users’ problems (urgent and waitable)
focuses on finding out if your users are interested in using your product or brand or not
Shaip annotators label fine-grained emotions — happy, sad, angry, frustrated, excited, neutral — across text, chat logs, and voice recordings.
We annotate sentiment from very positive to very negative, including neutral and ambiguous cases for more nuanced model training.
Aspect-based annotation identifies what customers react to — staff, delivery, pricing, app performance — and the sentiment tied to each aspect.
Shaip supports sentiment annotation in 50+ languages with regional annotators who understand slang, irony, and local idioms.
Sentiment annotation for call recordings, IVR audio, and conversational AI transcripts with tone, emotion, and intent labels.
Human annotators flag sarcastic, ironic, and ambiguous text so models learn when surface meaning differs from real intent.
Product-review polarity, return-driver tagging, and aspect-level feedback on listings.
Patient-feedback sentiment and clinical-note tone tagging, annotated by HIPAA-trained workers.
Sentiment on financial news, earnings calls, and analyst commentary for trading-signal and risk models.
Claims-call tone, complaint classification, and customer-effort scoring for service-quality models.
In-app feedback, support-ticket sentiment, and NPS open-text analysis.
Voice-call sentiment and chatbot transcript labeling for training and QA.
Brand-mention sentiment across X, Reddit, news, and review sites.
Public-opinion monitoring, misinformation detection, and citizen-feedback analysis.
We align on languages, sentiment categories, project goals, expected accuracy, and data volume in a quick consultation call.
We create annotation guidelines tailored to your business use case and ensure alignment on edge cases before annotation begins.
An initial batch is reviewed to validate annotation quality, refine workflows, and ensure consistency across the project.
Annotation workflows are executed using secure tools and structured processes to maintain consistency, scalability, and timely delivery.
Multiple quality checks and expert reviews are conducted to maintain high annotation accuracy and reliable outputs.
Final datasets are delivered in your preferred structure and format, ready for seamless integration into your AI/ML workflows.
Brand Monitoring
Social Media Monitoring
Voice of customer
Customer Service
To effectively deploy your AI initiative, you’ll need large volumes of specialized training datasets. Shaip is one of the very few companies in the market that ensures world-class, reliable training data at scale complying with regulatory/ GDPR requirements.
Create, curate, and collect custom-built datasets (text, speech, image, video) from 100+ nations across the globe based on custom guidelines.
Leverage our global workforce of 30,000+ experienced & credentialed contributors. Flexible task assignment & real-time workforce capacity, efficiency, & progress monitoring.
Our proprietary platform & skilled workforce use multiple quality control methods to meet or exceed quality standards set for collecting AI training datasets.
Our process streamlines, the collection process through easier task distribution, management, & data capture directly from the app & web interface.
Maintain complete data confidentiality by making privacy our priority. We ensure data formats are policy controlled and preserved.
Curated domain-specific data collected from industry-specific sources based on customer data collection guidelines.
Shaip helped an AI company build a speech emotion and sentiment analysis solution using multilingual call center audio data and annotations. The project improved emotion detection, customer insights, and AI model performance.
Sentiment analysis is the process of deducing, gauging, or understanding the image your product, service, or brand carries in the market. If this sounds too complicated, let’s refine it further.
Automatically detect one or more human faces based on facial landmarks in an image or video. Search an existing database of human faces to compare & match to build an intelligent facial recognition platform.
Every time we hear a word or read a text, we have the natural ability to identify and categorize the word into people, place, location, values, and more. Humans can quickly recognize a word, categorize it and understand the context.
Empowering teams to build world-leading AI products.
Sentiment analysis, or opinion mining, is the process of analyzing text or voice data to determine if the sentiment behind it is positive, neutral, or negative. It uses natural language processing (NLP) to interpret words, context, and emotions expressed in feedback or social media content.
Social media is a platform where customers openly share opinions. Sentiment analysis helps businesses understand public perception, manage their reputation, and engage with customers effectively.
By analyzing reviews, comments, and mentions, companies can track public sentiment, identify negative trends early, and take action to improve their brand image.
Fine-grained sentiment analysis provides detailed sentiment scores, such as very positive or slightly negative, rather than broad categories like positive or negative. This helps businesses understand feedback with greater precision.
Aspect-based analysis focuses on specific parts of feedback, such as customer service or product quality, to determine positive or negative sentiment for those individual aspects.
Multilingual analysis uses tools and translations to interpret sentiment in different languages, ensuring accuracy for global businesses operating in diverse regions.
Ambiguity and sarcasm are difficult for machines to interpret without context. High-quality human-annotated datasets help models understand these complexities better.
It helps identify customer pain points and track satisfaction by analyzing feedback from calls, emails, and reviews, enabling quicker resolutions and improved service.
Industries like eCommerce, healthcare, finance, and hospitality benefit by using sentiment analysis to enhance customer experience, manage reputations, and refine marketing efforts.
Timelines vary based on complexity, data size, and languages involved but are typically completed within a few weeks.
Sentiment analysis is commonly used for brand monitoring, social media listening, customer service improvement, and creating targeted marketing campaigns.
Shaip offers scalable, multilingual sentiment analysis with diverse, high-quality training data. Their services comply with privacy regulations like GDPR and HIPAA and ensure accurate results through human annotation.
Shaip uses rigorous validation processes and proprietary tools for quality control while adhering to privacy regulations through data anonymization and secure handling.
Costs depend on the complexity, size, and customization of the project. Contact Shaip for a tailored quote.
