Customer Sentiment Analysis Services

Sentiment analysis is discovering how people feel about your product, service, or brand in the market. With the rise of social media, people are talking online about their experiences with products and services. They do this through blogs, videos, social media stories, reviews, recommendations, roundups, hashtags, comments, direct messages, influencers, and other mediums. When this happens online, a digital trail of how a person feels about an event is left. It could now be a good, bad, or neutral sentiment. Sentiment analysis is the process of sifting through all of these written online thoughts and emotions.

6 example of sentiment analysis are:

1. Social Media Monitoring: Sentiment analysis is applied to tweets and postings to evaluate real-time reactions on various social media or forum sites. Monitoring social media communities helps gauge people’s emotions, views, and intentions toward businesses, products, or services.
2. Finance & Investing: Klatch specialists help clients improve business operations, analyze performance, and formulate plans by leveraging AI/ML technology and RPA. It analyzes news articles, informs investment decision-making, and predicts market trends.
3. Commerce: Sentiment analysis in commerce analyses survey responses, product reviews, movie reviews, and other online reviews to improve customer supoort or experiences. It’s utilized with text analytics for proactive market research and brand reputation.
4. Brand Monitoring: Using appropriate sentiment analysis, you can monitor competing businesses’ name recognition, strategies and customer satisfaction of products and services.
5. Healthcare: Sentiment analysis in healthcare is focused on enhancing patient experience by analyzing conversations to identify patient intent and mood while seeking detailed information.
6. Government: In the public sector, sentiment analysis helps find fake news and cyber threats and stop them. It helps determine the tone and meaning of suspicious messages on many social media sites.

Sentiment analysis, also called “opinion mining” is an NLP (natural language processing) method for figuring out, evaluating, or understanding how your product, service, or brand is seen in the market. With the rise of social media, people are talking more openly about their online experiences with products and services. They do this through blogs, videos, social media stories, reviews, recommendations, roundups, hashtags, comments, direct messages, influencers, and other mediums. When this happens online, a digital trail of how an individual feels about an event is left. This opinion could be good, bad, or just okay. Sentiment analysis is sifting through all of these written online opinions and emotions.

Sentiment analysis is done or needed because:

1. Analyze user-generated content: Sentiment analysis can help extract hidden elements from user-generated comments, reviews, and articles. Because most user-generated material is instinctive and without an apparent goal, it’s easy to grasp what people want, need, and think and how it relates to your brand.
2. Find product/service problems: People discuss products and services online. These conversations focus on snags and purchases. Sentiment analysis may show how the business is functioning overall, if people are content with your goods, and if issues are common.
3. Know consumer and market trends: Sentiment analysis can increase your marketing campaigns and business plan by revealing consumer trends. Using social media listening data, you may get a comprehensive perspective of your user, product base, and future plan.
4. Medical AI: Klatch provides medical image data to train ML models for pharmaceuticals, medical devices, and health insurance companies with HIPAA-compliant image annotation.
5. Retail and E-Commerce: Katch delivers image labeling and computer vision solutions to retail and e-commerce brands to improve customer service, market research, inventory management, trend forecasting, and brand reputation.
6. Educate customers: Use social media to educate users about your product, make recommendations, and provide broader information. This strategy helps host Q&A sessions during a new product launch.
7. Engage and help your customers: Social media is an excellent place to communicate with consumers for brand experience insights and to help them with product or service concerns. This sort of customer support can help establish brand equity. Sentiment analysis may also inform you if your plan is working and what needs improvement.

We assure your data protection by combining streamlined sentiment analysis operations and cross-quality checks. The results are supplied in a secure manner tailored to your specific requirements. Our teams adhere to a stringent nondisclosure agreement.

Analyzing data can be done in-house until there is too much data to handle. When things get hard, you’ll need specialists and the correct tools to label and alayze them accurately. For example, if you are developing a medical product, you will need a subject matter expert to classify objects found on an MRI report. Klatch data specialists can analyze datasets in various processes.

Rule-based systems automatically execute sentiment analysis based on manually defined rules and a vocabulary of phrases with the general sentiment. Automatic: These systems often depend on machine learning techniques To learn from training data. When nuanced emotions (angry, amused, sad, and jealous) are included, classifiers are required to conduct binary or multi-class sentiment classification. This type of sentiment analysis is often implemented in open-source Python toolkits such as NLTK. Hybrid systems combine rule-based linguistics with automated techniques to analyze sentiment from a semantic standpoint.

Klatch has a specialized staff of qualified analyst and subject matter experts who use a tried-and-true combination of manual operations to analyze data. Our team of analyst begins by comprehending the project specifications as laid forth by the client. As a result, they conform to the current dashboard and terminology. To enhance the utility of training datasets, they use a customized technique to extract datasets and add suitable tags and analyze. What seperates us from the competition is that our founders handle your projects and give direction on formatting data.

The four main steps of sentiment analysis are:

1. Data collection: It involves live APIs and manually collecting and organizing data.
2. Data processing: It includes audio transcription, caption/ image overlay, logo identification and text extraction.
3. Data analysis: It comprises training the AI/ML model, multilingual processing, adding custom tags, topic/ spect classification and then sentiment analysis.
4. Data visualization: It includes converting the insights into actionable reports as graphs and charts.

Klatch Technologies offers sentiment analysis services to prepare and provide various relevant, high-quality, and unique datasets.

The 4 different types of sentiment analysis are:

1. Emotion Detection: This approach identifies the emotion associated with utilizing your brand for a specific reason. A customer may experience any or a combination of emotions on the spectrum. One of the drawbacks of this kind is that users may communicate their feelings in various ways, including text, emojis, sarcasm, and more. The specialist should be highly skilled in recognizing the sentiment behind their distinct expressions.
2. Aspect-based Analysis: Reviews frequently offer helpful criticism and ideas; however, aspect-based sentiment analysis goes further. Aside from ratings and emotion, people often highlight positive or flawed points in their reviews. Behind the emotions, many significant insights from your business operations can are found. Through aspect-based analytics, they can be rectified, enhanced, or recognized.
3. Fine-Grained Analysis: A more thorough analysis would be to find out the two aspects of your brand. Users could have any feeling about your brand, from very good to bad. These feelings could take the form of ratings (e.g., based on stars), and all your model has to do is collect these different kinds of ratings from various sources.
4. Multilingual Analysis: It measures how people feel in many different languages. The language you use may depend on where you do business, sell your products, and other factors. This analysis utilized tools like language-specific mining and algorithms, translators when no translation is available, sentiment lexicons, and more.

Klatch Technologies caters to all kinds of annotation or sentiment analysis requirements for any project size.

Sentiment analysis is a part of AI. It is also known as opinion mining or emotion AI. It helps you figure out, evaluate, or understand how the market sees your product, service, or brand. People are more open about their online experiences with products and services now that social media is becoming more popular. They do this through blogs, videos, social media stories, reviews, recommendations, hashtags, comments, direct messages, and influencers. When this happens online, a digital trail is left that shows how someone feels about an event. It could be a good, bad, or just okay opinion. Sentiment analysis is sorting through all views and feelings written online.

Our corporate headquarters are in India. We have various offshoring centers in Asia and Africa. Our client support teams are available 24 hours a day, 7 days a week, and keep them up to date on the project’s status.