Sentiment Analysis Services
We analyze sentiments of user generated content to assist businesses and commercial setups in understanding the opinions, feelings, viewpoints, thought processes, and perspectives of individuals, communities, religious groups towards a brand, product, or service.
Accurately labeled data ensures success of a sentiment analysis system as best results are achieved using deep learning and big data.
Our Sentiment Analysis Process
Training data in the raw form is collected from different sources.
Data is sourced by our team in the form of customer reviews, tweets, and other social media posts and formatted for sentiment analysis.
The data is then labeled with the sentiment it expresses (positive, neutral, or negative).
Data is then divided into test and training sets for training and evaluating the sentiment analysis model.
Our Sentiment Analysis Data Services
We use labels for determining the sentiment of text (positive, negative, or neutral) as we know that computers can only understand text sentiment if it is accurately labeled.
We label customer sentiments hidden in the textual and audio data to identify whom the sentiments are directed at since this can serve as the basis for a sentiment analysis machine learning model.
An expressive-subjective tagging system is a method of tagging texts based on expressive-subjective sentiment involving specialized text-mining techniques and natural language processing. This method can help in providing insight into important customer feedback so that it can be used for developing a sentiment analysis model.
Customer reviews, for instance, can be tagged to understand the tone of the text whether it’s positive or negative.
This is a quick and easy way to determine the overall text sentiment analysis. By using a set of pre-defined rules, it is able to classify the sentiment of a given text as either positive, negative, or neutral. Words, phrases, and sentences are classified based on their polarity, such as “happy”, “pleased”, or “excited” or conversely, like “sad”, “angry”, or “disappointed”.
Audio streams can be classified and identified using objective-speech-event tagging. AI sentiment analysis algorithm is able to find the sentiment of a text, audio, and video stream by using objective-speech-event tagging. It can be used especially for sentiment analysis to identify positive and negative speech events. Positive speech, for instance, may be a compliment or an expression of gratitude, while negative speech might be an insult or criticism.
Sentiment Analysis – Use Cases
Developing social media sentiment analysis tools is one of the significant use cases of sentiment analysis. It is used in a variety of other applications including:
When businesses analyze the sentiments of their customers, they can figure out whether or not they are experiencing any issues with their products or services. Thus, the factors contributing to negative customer emotions are identified and eliminated.
Improve Product & Service Quality
By analyzing sentiment, businesses are able to identify vulnerabilities, issues, and glitches plaguing their products and/or services, in addition to capturing complaints. As a result, businesses can improve product and service quality by fixing these issues.
Enhance Marketing Strategies
A sentiment analysis tool can provide businesses with valuable insights into developing effective marketing strategies. In addition, customer conversations about the brand can be used to create specific marketing campaigns for the target audience.
Establish Communication Channels that Work
It is possible for brands to receive more comments on Twitter than on Facebook depending on the communication channel. Through the analysis of customer sentiment, businesses can make necessary changes to how they engage customers on a variety of platforms.
Tracking Brand Reputation
Preventing negative issues from negatively affecting your business begins with keeping an eye on your online reputation. Companies can get an accurate picture of their brand reputation by tracking what customers say about their company online.
Outsource To Us
We collect data through marketing surveys, analyze, and score it. Software applications or manual methods can be used to accomplish this. The data extracted can then be used to develop training data for developing AI-enabled sentiment analysis tools for various industries.
Quality on a Promise
Our team is committed to delivering high-quality Text Annotations. Our training data is therefore tailored for the applications of our clients.
Uncompromised Data Security
Data security and confidentiality are of utmost importance to us. At all points in the annotation process, our team ensures that no data breaches occur.
Scalable with Quick Turnaround Time
We at Cogito claim to have the necessary resources and infrastructure to provide Text Annotation services on any scale while promising quality and timeliness.
Besides offering flexible pricing, we can tailor our services to suit your budget and training data requirements with our pay-as-you-go pricing model.
Get Us On Board
We have a wealth of tools and experts for developing high-quality training data for developing sentiment analysis tools powered by artificial intelligence. Bringing together over 1500 data experts, We boast a wealth of industry exposure to help you develop successful NLP models.