5 Key Business Applications of Sentiment Analysis

In order to gauge whether your customers are happy with what you’re doing, customer satisfaction is crucial. It is proven that a high degree of satisfaction increases the lifetime value of a customer, more customer retention, and a stronger reputation of the brand. Alternatively referred to as “opinion mining,” sentiment analysis can help product managers understand the customer satisfaction level.

Analyzing customer sentiments to predict accurate sentiments has been challenging and time-consuming due to the high volumes of collected data from various sources. Having an NLP and AI expert on hand can help you get started with sentiment analysis if you want to understand your customers in a dynamic market.

What Goes into Procuring Sentiment Analysis

Artificial Intelligence algorithms, Natural Language Processing (NLP) methods, and Machine Learning (ML) models contribute to sentiment analysis. This includes neural networks, unigram algorithms, skip-gram algorithms, and bag-of-word regression models using machine learning and deep learning (DL). The use of natural language processing techniques in machine learning software allows unstructured texts to be sorted by emotion and opinion.

As an example, let’s look at this. Imagine that you are responsible for understanding the general sentiment in the support conversations at your company. Get a sense of your customers’ ongoing mood and tone. You would need an emotion classifier in such a situation to produce predictions regarding relevant conversations. Dashboards that are often designed to measure customer satisfaction can be populated with sentiment data you’ve collected from your customers.

Why is Sentiment Analysis Important in Business?

At first glance, it may seem like a fancy class project, but sentiment analysis has many practical applications in the business world. Using sentiment analysis to solve business problems is an excellent starting point.

1. You can aggregate customer sentiment from free-form feedback data and identify the primary promoters or detractors of your product or service. Once the detractors have been converted to promoters, you can gradually implement corrective measures to rebuild trust.
2. It is possible to detect hateful and inappropriate comments on your online platform, which will help you keep it free of bullies.
3. The feedback you receive from recent employees, peer reviews, and manager feedback can help you identify demotivated or about-to-quit employees and provide them with constructive ways to succeed.

Based on these sentiment analysis examples, you can see that sentiment analysis is an excellent tool for gaining a better understanding of employees and customers, keeping platforms safe, improving customer shopping, and learning from competitors. Sentiment analysis can be combined with other artificial intelligence technologies like text summarization to provide deeper, more powerful insights.

Industry Applications of Sentiment Analysis

In sentiment analysis, the text is analyzed to identify and extract subjective information, which businesses can use to measure how customers feel about their service offerings. Using AI-driven technology, sentiment analysis traces the undertone of text by channelling extensive digital information.

The use of sentiment analysis is widespread across various industries. Several popular sentiment analysis applications exist, such as monitoring social media, managing customer support, and reviewing customer feedback. Here are a few more to get you going with the idea of integrating sentiment analysis into the marketing & customer analysis symmetry of your business:

1. Social Media Monitoring

Social media posts often contain some of the most honest opinions about your products, services, and businesses because they’re unsolicited. By incorporating NLP experts on board, you can analyze individual emotions and overall public sentiment across all social platforms based on their comments, feedback, and reviews.

In chat, sentiment analysis can detect sarcasm, understand acronyms (OMG, LOL, ROFL, etc.), correct misspellings and misuses, and detect sarcasm.

2. Managing & Monitoring Business Reputation

Sentiment analysis is widely used in business for brand monitoring. A business can leave negative reviews online for a long time, and the longer it does so, the worse the situation will become. Negative brand mentions can be detected as soon as they occur with sentiment analysis tools.

Furthermore, you can monitor your progress over time by keeping track of your brand’s image and reputation. You can use this information to develop usable statistics and information about your brand, whether you are monitoring news stories, blogs, forums, or social media for information about your brand.

In addition to following trends and anticipating outcomes, machine learning can be relied upon to stay one step ahead of the competition and become proactive rather than reactive.

3. Analyzing the Market Response to a Product

Learn what users are saying about your new product soon after launch, or examine feedback you may have yet to see. Aspect-based sentiment analysis allows you to locate only the information you need based on your search terms (interface, UX, functionality). Identify what makes your most valuable customers happy, learn how your product is perceived by your target audience, and improve your product. The whole thing is based on sentiment analysis.

4. Get to Know the Voice of Your Customers (VoCs)

Creating a personalized customer experience requires communication and listening to the voice of your customers. As a result, businesses have access to a large number of conversation sentiment analysis datasets that can be integrated into the development of ML models in order to automate the sentiment analysis process. Taking all feedback, you receive from customers, such as web feedback, surveys, chats, and customer service calls, and evaluating it. This data can be categorized and structured with the help of sentiment analysis so that you can discover recurring topics and concerns.

5. Analyzing Market & Competitors

In order to research the market and your competitors, you can use sentiment analysis. Get a sense of who your competitors are mentioning positively and how your marketing efforts compare. Look at how your competitors speak positively to their customers, and take some of those phrases and use them in your own brand message and tone of voice guide.

Advantage of Manual Sentiment Analysis Over AI-Integrated Machines & Apps

In a world of billions of people, each communicates uniquely, e.g., adding tiny variations to the language and attaching sentiments to it, which are easy for us to understand but difficult for machines to understand. Sarcasm, for instance, is the opposite of what is true when we use it in our sentences. Sentiment analysis through machine learning models is difficult to predict customer behavior, as a matter of fact. Therefore, manual sentiment analysis has an advantage over automated sentiment analysis performed through AI-integrated applications and machines.

Final Words

For accurate sentiment analysis results, a blended approach to sentiment analysis is the most effective way for businesses to utilize it to understand customer behavior. Consequently, Cogito, an NLP expert, combines manual and automated approaches for accurate sentiment analysis.

In addition to manually conducting sentiment analysis of your social media channels and customer feedback on other platforms, the experts here can also assist you in getting AI training data in order to build ML models for automated sentiment analysis based on real data rather than assumptions made based on a small sample.

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Insightful Interpretation of Machine Learning Datasets

It is possible to simulate human intelligence in machines with artificial intelligence (AI) and machine learning (ML). These simulations allow them to complete a variety of tasks without much human assistance – Companies need precise training data if they are to develop AI and ML models that are more efficient and newer. It is possible to gain a better understanding of a given problem through the use of training datasets which can subsequently be enriched through data annotation and labeling for further use as artificial intelligence (AI) training data.  

What is Machine Learning?

The goal of machine learning is to imitate humans’ learning process through the use of data and algorithms. It gradually improves the accuracy of its predictions. Statistical methods allow algorithms to be trained to make classifications or predictions within data mining projects using machine learning — this provides key insights into the data. 

Ideally, data mining improves business and application decision-making, influencing key growth metrics through these insights. Increasing demand for data scientists will result from the continued growth and development of big data, which requires them to identify the most pertinent business questions and the data that will be required to answer the questions.

Types of Machine Learning

An algorithm learns to improve its accuracy by applying supervised, unsupervised, semi-supervised, and reinforcement learning approaches. These four basic approaches are classified according to how an algorithm learns. Data scientists choose which algorithm and machine learning type depending on the data they wish to analyze.

1. Supervised Learning 

These types of machine learning algorithms require labeled training data and variables data scientists want the algorithm to evaluate for correlations. Here, the input and output of the algorithm are both specified by the data scientists.

2. Unsupervised Learning

It involves algorithms that learn from unlabeled data, where an algorithm scans data sets to identify meaningful connections. All predictions or recommendations are predetermined by the data that the algorithms train on.

3. Semi-supervised Learning

There are two approaches to machine learning in this approach, the model is fed mostly labeled training data by a data scientist, but it is free to explore the data on its own and develop its own insights about it.

4. Reinforcement Learning

As part of reinforcement learning, data scientists teach a machine how to complete a multistep process governed by clearly defined rules. For the most part, an algorithm decides how to complete a task on its own, but data scientists program it to complete it and give it positive or negative cues as it works out how to accomplish it.

Real-world Machine Learning Use Cases

You might encounter machine learning every day in the following ways:

1. Speech Recognition 

Alternatively called automatic speech recognition (ASR), computer speech recognition, or speech-to-text, this technology converts human speech into written form using natural language processing (NLP). A number of mobile devices include speech recognition in their systems so that users can conduct voice searches—like Google Assistant in Android smartphones, Siri in Apple devices, and Amazon’s Alexa in media devices. 

2. Customer Service

Human agents are being replaced by online chatbots as customer service grows. We are seeing the shift in customer engagement across websites and social media platforms as these companies provide answers to frequently asked questions (FAQs) around topics such as shipping or product delivery, or cross-selling product recommendations. Slack and Messenger, for example, as well as virtual agents and voice assistants, are some examples of messaging bots on e-commerce sites with virtual agents.

3. Computer Vision

Computers and systems can use this AI technology to glean meaningful information from images, videos, and other visual inputs; Using this technology, they can take action based on these inputs. It is distinguished from image recognition tasks by its ability to provide recommendations. The application of computer vision in the industry of photo tagging on social media, radiology imaging in healthcare, and self-driving cars is based on convolutional neural networks.

4. Recommendation Engines

Online retailers can make useful add-on recommendations to customers during checkout using data on past consumption behavior. AI algorithms can help us discover data trends for developing more effective cross-selling strategies.

5. Automated Stock Trading

Without human intervention, AI-driven high-frequency trading platforms execute thousands or millions of trades every day in order to optimize stock portfolios. 

What is Training Data?

Machine learning algorithms develop an understanding of datasets by processing data and finding connections. In order to make this connection and find patterns in processed data, an ML system must first learn. After the ‘learning,’ it can then make decisions based on the learned patterns. ML algorithms can solve problems from retro observations – Exposing machines to relevant data over time allows them to evolve and improve. The training data quality directly influences the ML model’s performance quality. 

Cogito is a leading data annotation company assisting AI and machine learning enterprises with high-quality training data. In its decade-long journey as a data procurer, the company has built credibility for the accuracy and timely delivery of training data to ensure the quick accomplishment of data-driven AI models. 

What is Test Data?

When an ML model is built using training data, you need to test it with ‘unseen’ data. This testing data is used to evaluate the future predictions or classifications the model makes. The validation set is another partition of the dataset that is tested iteratively before the test data is entered; this testing allows developers to identify and correct overfitting before the test data is entered. 

Both positive and negative tests are performed using test data to verify functions produce the expected results for given inputs and to determine whether the software is capable of handling unusual, exceptional, or unexpected inputs. As your test data management strategy can be optimized by outsourcing data annotation to an industry expert, you can ensure quality information reaches test cases more quickly.

Training Dataset vs. Test Dataset

An ML model can learn patterns by learning insights from training data, which is approximately 80% of the complete dataset to be fed into the model. Testing data represent the actual dataset since they evaluate the model’s performance, monitor its progress, and skew it for optimal results.

The training data is typically 20% of the entire dataset, while the testing data confirms the model’s functionality. In essence, the training data train the model, and the testing data confirms its effectiveness. 

Enriching Datasets Using Data Annotation & Labeling

Building and training an ML model will require large volumes of training data. Data annotation is the process of adding tags and labels to training data. In order to achieve this goal, ML models require properly annotated training data in order to process data and gain specific information. 

Data annotation helps machines identify specific patterns and trends in data by connecting all the dots. Enterprises must understand how different factors affect the decision-making process in order to achieve business success. Data annotation services hold the key to accelerating businesses into the future. 

Cogito can Help with Data Annotation Services

With Live Enterprise, organizations can make intuitive decisions automatically at scale, get actionable insights from real-time solutions, experience anytime/anywhere, and get deep visibility into data across functions to become more productive with AI and machine learning innovations. Cogito offers training data annotation services for machine learning and artificial intelligence. The agile system at Cogito combines human-empowered data annotation and automated annotation & labelling tools to process unstructured data. 

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Training Data to Employ AI in Healthcare

As artificial intelligence (AI) becomes an increasingly important tool in health care, it offers unprecedented opportunities for improving patient outcomes, reducing costs, and impacting population health. There are many examples, including automation, delivering a simple synthesis of complex health information to patients, families, and caregivers, and providing recommendations and visualizations for shared decision-making among patients, family members, and health professionals.

Need for AI Technology Adoption in Healthcare

As healthcare organizations realize the benefits of technology adoption as patient numbers rise and quality and affordable treatment become more important, they are adopting artificial intelligence. It is still early in the adoption of artificial intelligence in healthcare. The reason behind this is that many emerging economies lack the means of digitizing patient records. 

The increased number of diseases and the need for better understanding and diagnosis with the help of deep learning are some of the reasons for AI adoption in healthcare. In many healthcare organizations, AI has enabled them to treat more patients at lower costs and with improved efficiency. 

Need for Building Database for Healthcare AI

To effectively utilize AI in the healthcare industry, there is a need to build a database with all the historical data of patients, required to identify the patterns and accordingly use AI for diagnosis and therapy. If customized accurately, AI would not only be ‘intelligent’ but also highly user-friendly. 

It is also necessary to maintain records physically in certain countries in South Asia and Africa. However, the adoption of AI is relatively less challenging in developed countries such as the US and the UK, where the majority of healthcare data have already been digitized, and the technology has flourished. 

Significance of Data for AI Utilization in Healthcare 

In healthcare, data can be related to health conditions, reproductive outcomes, quality of life, and many similar life events determining an individual or an entire population. Health data includes clinical metrics along with environmental, socioeconomic, and behavioral information pertinent to health and wellness.

After getting to know the most important terms, definitions, and methods used in the field of artificial intelligence, we need to be familiarized with the current situation of data and data analytics in healthcare in order to be able to find out where artificial intelligence can come into play in healthcare; why and how we could make use of machine learning and smart algorithms – as the most important ‘base material’ for smart algorithms is data.

AI technologies that can Transform Healthcare

AI is based on technologies such as Natural Language Processing, Deep Learning, and Context-Aware Processing. The healthcare industry is undergoing a transformation as a result of these technologies. 

1. Natural Language Processing (NLP) 

In today’s world of complex data, AI plays a significant role in simplifying insights, and NLP makes the process more accessible to more people. The goal of Natural Language Processing (NLP) is to mimic the responses of humans when answering queries and holding conversations by utilizing algorithms. Using NLP healthcare providers can summarize narrative text by highlighting key concepts or phrases, such as those found in long journal articles or clinical notes. 

Data elements in EHRs, which are represented as unstructured text, can be transformed into structured, meaningful data by using Natural Language Processing. Using NLP, Intermountain Healthcare, for example, has identified the causes of illness in patients by mining their medical records in order to identify cases like strokes, cancer, heart failure, and venous thromboembolisms (blood clots in the leg’s deep veins).

2. Deep Learning 

By implementing AI in healthcare, diseases can be diagnosed and treated more efficiently. Medical data and images can be analyzed using deep learning, a component of AI, to enhance the ability of doctors to treat diseases. Visually challenged individuals can benefit from deep learning by narrating the text, identifying the facial expressions of nearby people, studying the surroundings, and describing the environment using computer vision, text-to-speech, and computer vision. 

Advanced neural network algorithms, GPUs that can process large amounts of data, and access to enormous amounts of internet data are all driving the deep learning revolution. A cognitive computing & deep learning approach is being used to train IBM Watson, for example, to assist doctors with medical diagnosis.

3. Context-Aware Processing 

As examples of AI applications that can be used in healthcare, Apple’s Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana can provide assistance based on the instructions provided by the programmer. Using AI chatbots in healthcare can eliminate the burden on medical professionals and reduce the time and cost of coordinating care and detecting issues or diagnosable health issues. 

Bots could be considered the next step in the transformation of health assistants. HealthTap, for instance, or Your.Md are Bots that use AI to diagnose the most common symptoms. Nevertheless, chatbots play an important role in directing patients to the right doctor for diagnosis and treatment. Doctors with experience would be able to rely on them to supplement their duties. 

4. Intelligent Robotics 

There is also the possibility of using AI with robotics. Healthcare facilities can be revolutionized with physical robots that can help people stay healthy and reduce the number of hospitalizations. In addition to AI, advanced humanoid designs are enabling robots to converse with and socially interact with elderly patients. With robots, surgery can be performed with more precision and smaller incisions since their flexibility and reach are greater. 

Certain robots can serve as social partners to treat mental health issues or alleviate loneliness. Blue Frog Robotics (developers of BUDDY), National Institute of Advanced Industrial Science and Technology, and AIST (developers of PARO) are among the companies that have expanded companion robots into healthcare.

Many technology companies are currently conceptualizing AI-powered surgical robots by leveraging the capabilities of machine learning applications like Google DeepMind, IBM Watson, and others. With AI-enabled robots, less damage will occur, precision will increase, and recovery will be faster.

Conclusion

With the growing amount of digital data and the complication of overseeing a large number of patient records, all end-users under the healthcare umbrella are embracing artificial intelligence. AI-enabled content analytics can enhance providers’ ability to administer effective and efficient treatment protocols by speeding up the diagnosis of patients’ conditions through the use of NLP tools.

Although AI has many proven applications in the healthcare sector, the extent of its implementation in medical practice is still at an initial stage. The healthcare industry is expected to undergo a transformational change as AI adoption increases. It is estimated that healthcare data diagnosis and treatment procedures will improve as a result of the expected growth in the future. The healthcare industry can reap numerous benefits from AI, which has a wide range of applications.

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Sentiment Analysis: Types, How it Works & Why it is Difficult?

Understanding the sentiments of the people is not easy unless they express their feelings, opinions and perspective anything. But if you have such platforms where people are freely speaking up about their thoughts and concerns, you can easily find out their sentiments. Here where Cogito comes in the facility of sentiment analysis.

Sentiment Analysis is the process of determining the conceptions, judgments, feelings, opinions, viewpoints, conclusions, and other notions towards anything. It is a technique to analyze texts, images, emojis and various other actions to know what other people think about a product, service, company, brand name, or a reaction to a specific event, social movement, etc.

The Usefulness of Sentiment Analysis

Sentiment analysis is playing an enormous role in understanding people belonging to different groups and their sentiments. On political grounds, it helps to know how much of the majority is in favor of the Govt. or how many stands opposing to their services and measures undertaken.

While on the other hand, in the business world, it is helping companies to know their customers in a better way. Such a resource becomes useful for the business enterprises to offer products and services as per the expectations of their potential customers and get appropriate results.

Social Media is one the best and biggest platforms where the theory of sentiment analysis is and must be applied, to interpret the feelings of various people. Hence, we need to understand it as a process, how it works, its applications and why it is important for business organizations and other aspects.

What is Sentiment Analysis in NLP?

Natural Language Processing (NLP) is a way to understand the actual meanings of the text words, sentences, or entire written documents. NLP is used to train the machines thereby, helping them understand the language and communication process among humans while discussing a topic.

The main motive of sentiment analysis is to find out expressions of people that are eventually classified as positive, negative, or neutral. It can be used in diverse areas such as company products, market research, marketing analysis, customer targeting, product reviews, customer feedback, reputation management, etc.

How Does Sentiment Analysis Work?

As mentioned above, sentiment analysis is used in NLP-based Machine Learning algorithms to develop such AI applications that can understand the ways of linguistic context showing the sentiments of different people.

But the question here is, how does sentiment analysis work? The developers begin by creating a text Machine Learning-based algorithm that can detect the contents showing any specific sentiment indicator.

Afterwards, they train the ML classifier by feeding it a huge quantity of training datasets containing reactions based on positive, negative, and neutral sentiments. Every piece of content is scattered and divided into basic components such as text words, phrases, sentences, and other entities.

Once this process is completed, the relationship between the topics and the identification process commences. Then, AI model assigns a sentiment score to that particular post. The post can range from 1 representing negative and +4 representing 4 positive comments. If a sentiment is neutral, the score is usually given 0.

As we already know, understanding the different human languages is a very complex task due to their cultural and social diversity. Hence, it is important to train the programs in such a way that they are able to detect and analyse grammatical nuances.

TYPES OF SENTIMENT ANALYSIS

To understand the sentiments of people, there are different types of sentiment analysis used in the market. Apart from normal opinions – positive, negative, or neutral, other types of sentiment analysis help in understanding people’s inner feelings, their actual intentions, and emotions.

Fine-grained Sentiment

This is one of the most simple and common ways of understanding your customers’ sentiments. Yes, fine-grained sentiment analysis helps in studying the ratings and reviews given by the customers.

While analyzing the sentiments, you can use the readily available categories like very positive, positive, neutral, negative, or very negative. Providing a rating option from 1 to 5 is yet another way to scale the feedback given by your customers. Most e-commerce sites use this technique to know the sentiments of their customers.

Aspect-based Analysis

This type of sentiment analysis is more focused on the aspects of a particular product or service. To make it easier to understand, let’s take an example – if you are talking about a soundbar or a wireless speaker system.

Here you can analyze your customer’s sentiments by asking them for feedback about the sound quality, connectivity, and other features, making such devices more useful and productive for the users. It helps in determining specific attributes of the product.

Intent-based Sentiment Analysis

To know the intent of the customers; whether they are looking to buy the product or just browsing around, is achievable through intent analysis. It not only helps to identify the intent of the customers but also to track and target them through advertisements or other ways of online promotions.

With intent analysis, companies can save their time, efforts, and cost while targeting the potential customers as per their intentions. This helps in getting a more vivid understanding of the intentions of the customers.

Emotion Detection Sentiment Analysis

As the name symbolizes, this type of sentiment analysis helps to detect and understand the emotions of the people. Emotions like anger, sadness, happiness, frustration, fear, panic, worry, or anxiety, may all be included.

Understanding the sentiments of people using emotion detection is even more difficult as people use a collection of words having a different sense of meaning. Hence, inaccurate emotion detection can lead to an inaccurate decision while analyzing the true sentiments of the people.

How Can Sentiment Analysis be Used?

The main motive of using sentiment analysis is to find out the true feelings of the varied people living in our society. It can be used for analyzing the customer’s feedback of a particular company, normal users on social media towards a product, services, social issues, or political agendas.

Companies also use it for brand analysis, reputation crises, campaigns performances, competitor analysis, and improve the service offered to the customers. Analyzing the sentiments of the customers helps the customer support team to prioritize their work for offering better service to end-users.

Why Sentiment Analysis is Difficult?

Sentiment analysis is a very difficult task due to sarcasm. The words or text data implied in a sarcastic sentence come with a different sense of meaning depending on the senders or situations.

Sarcasm is remarking someone opposite of what you want to say. It is expressed to hurt someone’s feelings or humorously criticize something. On social media, sarcasm is one of the most common behavior you can see nowadays interfering with the results.

Sarcastic words or texts show the unique behavior of people. It is usually used to convey a negative sentiment using the positive intention of words. This kind of caustic remark can easily mislead the sentiment analysis decisions.

The presence of sarcastic words makes it difficult for sentiment analysis processing in turn making it difficult to develop NLP-based AI models. Hence, a deeper analysis of such words is required to understand the true sentiments of people with accuracy.

In such a case, we can use the psychographic-based analysis to understand such people and their exact intention of what they are trying to say. Using the psychographic segmentation in sentiment analysis can give more comprehensive perception of different kinds of people interacting with each other.

Sentiment Analysis using Psychographic Segmentation

Sentiment analysis can be a big game changer in forming a more focused marketing strategy for the companies. But these establishments need to understand their customers by segmenting them into their characteristics like their cultural values, beliefs, desires, goals, interests, and lifestyle choices.

Psychographic Segmentation becomes helpful here in analyzing the customers’ sentiments by segmenting them based on their activities, lifestyle, and interests. It is a more qualitative approach to study consumers according to their psychological characteristics.

Psychographic segmentation not only helps in improving the customer experience but also authorizes companies to offer more tailored products or services to the right customers at the right time improving the return on investment.

Psychographics allows you to learn about the deeper motivations and emotions that influence potential customers. Hence, in the next article, we will learn more about psychographic segmentation, and how it is helpful in sentiment analysis.

Cogito offers sentiment analysis services for the wide-ranging industries using the insights of our skilled workforce making every interpretation meaningful. From social media to other useful online platforms, we are experts in analyzing the sentiments of a different group of people towards the different entities.

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Specialized Training Data With Cogito Tech LLC

AI programs are well learned, developed to perform complex computation and provide dependable results. The efficiency of an AI program depends upon the data it feeds on.

When conversation on machine learning and AI (Artificial Intelligence) programs and machine learning (ML) started a decade ago, the relevancy of data utilization came to the fore. Going a level higher than software programming, artificial intelligence with the help of machine learning, artificial neural networks, and computer vision has changed the way the world is solving complex problems and finding their solutions. In all these efforts, data has worked as the sole enabler through which distinct AI programs have been able to perform and deliver quantifiable results.

Specialized Training Data Matters

To begin with, the applicability of AI programs is diverse. Every algorithm backed with reliable training data is required to deliver optimum results or learn from the results to develop another model for prediction. The entire calculation is complex and must produce accurate solutions. When an ML algorithm makes use of the specialized training data, then it should either be useful enough to evoke measurable action from the customer; or if in case, an AI program is built for detecting scenarios, it should point out problems, or merely need to compute a solution, the training data should be structured to suit the algorithm model.

Depending upon the objective the ML process and the computation, below steps are found in every process:

Step 1: Defining program, and proposing solution (as per the objective)
Step 2: Construct data (raw data, sample collection, sampling, data splitting)
Step 3: Data optimization (clean the data, re-engineer)
Step 4: Training the model
Step 5: Use model for predictions

Now if we talk about the applicability of ML algorithms on the basis of this process, then we can say that algorithms are required to do a variety of human-like tasks. Be it for analyzing visitor identities for on-premise security and medical diagnosis based on a patient’s medical history or to recommend appropriate retail products for purchase to end customers, AI programs are built to serve diverse business purposes. Some simple AI programs can perform exceptionally well with small data sets while big computations on low quality large data sets can fail to come up with accurate results. As a reason, training data holds actual importance in the quality of output produced while an AI program will marvel only when its data requirements are on point.

Building Specialized Training Data for AI

Based on the objective of the ML model, the need for training depending upon the learning model, data labelling, ML model complexity – are crucial for analyzing the reliability of the training data. Meanwhile, accessing training data quality can be a tough task. And, data accuracy is as important as checking the data quality and size. The training data should be reliable and able enough to fulfill the objective of the ML model. The reliability of the data can be checked on parameters of how the firm is ensuring training data quality.

Taking an instance, if the ML model of a healthcare service provider needs to be trained for detecting dental problems, then a specialized data labelling firm onboards dental practitioners to train in-house work forces for data labelling. To train the workforces to label the data correctly for training, the dental practitioners often suggest actual professional methods for training such as by marking the structure of the teeth and identifying the dental problems by the dental schema. Post which, the workforces can label the data comprehensively with labelling tools and annotate the dental images data.

Similarly, Radiologists read the MRI or PET Scans, CT Scans or X-Rays and train the workforces to identify and label the unlabelled training data. Pathologists are specialized to comprehend and observe molecular or microscopic images to identify and label tumors, cancer cells in the data. A Cardiologist trains the workforce to do semantic segmentation of heart and label ventriculares, atriums or myocardium around the heart. Professionals and experts from specific medical disciplines of the field train & supervise a team of data annotators. This trained team, in turn, disseminates the guidance further to workforces. Further, quality checks are carried out to transform the training data to arrive with a final clear data set that can be used for training the model. The process ensures a high level of accuracy in the training data and enables data labeling companies to maintain high quality through QC’s.

Sometimes as per the business requirement data quality is optimized using multiple computational methods such as below –

1. The question pool comes with correct and known answers at each step which alert the labeler and helps in checking their performance.
2. Consensus is used for subjective questions where common answers are based on majority.

On the basis of such methods and more such quality ensuring methodologies, training data is prepared for the agriculture sector too. Image segmented deep learning is carried out with the help of satellite imagery, drone recordings and robotic machines, which detect or identify the condition of the produce in the agricultural fields. To take an instance, robotic machines enabled with an AI algorithm zooms over the seedling of a vegetable and predicts the time by which it will be ready for harvest. Image annotation works at the back of such algorithms which help AI programs learn simply by viewing the crop. For farming and agriculture helps in crop health monitoring, crop protection, identifying crop spoilage, fructification, and livestock management.

Self reliant technology trained by high quality training data is reducing human efforts and errors, adding more value at each step of industrial operations. This is just the beginning of what AI can do. Powered by machine learning and computer vision, it can increase business profits and spearhead major transformations to bring greater efficiency and prevent issues before they take place.

Concluding Note

While most of us believe that it is the algorithm that helps in defining the solution, however, it is the potential of specialized training data that makes the prediction model deliver concise results.

To add, as businesses start relying more on Artificial Intelligence, and Machine Learning procedures to provide their services, the market for data labelling will flourish. It is expected that the market will grow three-fold by 2023, paving the way for more solutions on the horizon to make use of. For industry 4.0 and the subsequent leap towards industry 5.0, AI-enabled self-reliant machines are needed as they are capable of doing more complex tasks, in comparison with what application programming can offer to the global enterprises. Originally – source