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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.
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Analyze data to comprehend user sentiment
With the rise of social media, people often share their experiences with products and services online through blogs, vlogs, news articles, social media stories, reviews, recommendations, roundups, hashtags, comments, direct messages, micro influences etc.
Shaip offers you different techniques i.e. emotion detection, sentiment classification, fine-grained analysis, aspect-based analysis, multilingual analysis, etc. to uncover meaningful insights from user emotions & sentiments. We help you determine if the sentiment in the text is negative, positive, or neutral. Language is often ambiguous or highly contextual, making it extremely difficult for machines to learn without human assistance, and hence, training data annotated by humans becomes critical for ML platforms.
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
This method determines the emotion behind using your brand for a purpose. For instance, if they bought apparel from your eCommerce store, they could either be happy with your shipment procedures, quality of apparel, or range of selections or be disappointed with them. Apart from these two emotions, a user could face any specific or a mix of emotions in the spectrum as well. One of the shortcomings of this type is that users have a multitude of ways to express their emotions – through text, emojis, sarcasm, and more. The model should be highly evolved to detect the emotion behind their unique expressions.
A more direct form of analysis involves finding out the polarity associated with your brand. From very positive to neutral to very negative, users could experience any attribute concerning your brand and these attributes could take a tangible shape in the form of ratings (e.g. – stars based) and all your model needs to do is mine these various forms of ratings from diverse sources.
Reviews often contain sound feedback and suggestions on the other hand aspect-based sentiment analysis takes you a step further. Here the users generally point out some good or bad things in their reviews apart from ratings and expressing emotion. For instance – The travel desk associate was extremely rude and lethargic. We had to wait for an hour before we got our itinerary for the day.”
What lies beneath the emotions are two major takeaways from your business operations. These could be fixed, improved, or recognized through aspect-based analytics.
This is the assessment of sentiment across diverse languages. The language could depend on the regions you operate, countries you ship to, and more. This analysis involves the use of language-specific mining and algorithms, translators in the absence of it, sentiment lexicons, and more.
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.
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.
Using AI to improve business performance through customer experience
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.