Text Analysis, also known as text analytics or text data mining, is the method of utilizing high-quality information in texts, referred to as structured data. Structured data is highly specific and stored in a predefined format, unlike unstructured data which is a combination of various different forms of data that are formed in their original form. 

The purpose of Text Analysis is to take the unstructured data and create structured data which is both easily searchable and offers important insights in numerical form. Structured data has much higher click-through rates, better search visibility, voice search domination, and faster indexing which can be achieved through AI tools that make it possible to analyze unstructured data. 

Text Analysis involves converting unstructured data into structured data for improved searchability and insights. With AI tools, businesses can analyze unstructured data and achieve higher click-through rates, better search visibility, voice search domination, and faster indexing.

Text Analysis offers a variety of different capabilities involving information retrieval, frequency distributions, pattern recognition, tagging, text extraction, data mining, including associative, visual and predictive analytics. AI can help businesses process and understand their data, with much more efficient and effective methods.

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How does Text Analysis differ from other operations?

Text analysis is a machine-learning technique used to automatically extract machine-readable valuable insights from unstructured text data. Its purpose is to generate structured and understandable information from free text.

Text analysis software can extract specific information like keywords, topics, and action items  categorize feedback, or even summarize a podcast. There are plenty of different types of methods and techniques that can be used for these purposes, but all of them are based on text analysis. 

Text summarization - the process of shortening text without removing its context or structure allows us to obtain a short subset of the most essential information from a long document in a human-readable format; although, it would be impossible without analyzing the whole text first. 

What are the data types and volume of data typically analyzed?

Modern AI-powered solutions allow for analyzing all types of unstructured information that exist. It can be a single-sentence text message, a whole document, or a transcribed business video meeting. Here are some examples of the data types and the most used text analysis techniques.

Text Classification

Text Classification is the process of assigning tags and categories to unstructured text through Natural Language Processing. It helps organize, structure, and categorize any textual data. The most common text classification tasks are sentiment analysis, topic modeling, language and intent detection. 

Use examples: With the help of text classification, we can categorize email threads by tags “urgent” or “personal” or help customer support tickets get to the right department.

Sentiment analysis is the most popular classification example. It helps detect positive, negative, or neutral tones behind a customer’s message and helps companies understand their direction. 

Text Extraction 

Text Extraction helps highlight relevant pieces of data from text. It can be keywords, named entities (names, addresses, locations), or summary extraction. 

Use examples: Keywords extraction may be helpful to tag support tickets for urgency, generate tag clouds, and marketing teams can use it to find frequently discussed topics. Named entity extraction can be used to create custom databases and provide feedback, scan news content to reveal important data, and provide directed content recommendations through customer data analysis. Summarization is a lifesaver when people need to get insights from a long text easily and quickly.   

Entity Recognition

Entity Recognition is a statistical technique method that can identity named text features such as names, organizations, dates, prices, and so on. 

Use Cases: Entity Recognition can help power recommendations by extracting entities from one document and store them in a relational database. It can also help with leveraging AI Support through chatbots, process categorizations, augment research by identifying all companies, people, dates, and more on a web page. Entity Recognition can be run on all documents to extract entities associated with the documents and stored accordingly. 

Sentiment Analysis

Sentiment Analysis involves determining subjective material and extracting different forms of attitudinal insights. Some of these insights include sentiment, opinion, mood, and emotions. Text analytic methods are supportive in processing sentiment at the entity, concept, or topic level.

Use Cases: This can be used for social media, customer service and marketing. Social media platforms allow businesses to monitor how people react and perceive a specific brand or product. Sentiment analysis help companies communicate better with customers and develop more relevant messages by identifying users’ emotions.

Clustering

Clustering with Text Analytics allow people to take large amounts of data from different medias and analyze trends and similarities between them. This allows companies to analyze large-scale texts and information from legal documents to customer service.

Use-cases: In healthcare industries, clustering is used to find patterns in doctor’s reports and patient data. Researchers and product development can analyze patterns through history and customer reviews. Customer service experiences can be measured with clustering by studying different customer tickets and requests and how the experiences were handled. 

Top 7 AI text analysis solutions on the market:

#1 Google Natural Language AI

Google Cloud NLP focuses on different text analysis applications, such as entity extraction, syntax analysis, sentiment analysis, and content classification. If you’re keen to train your machine-learning models, all you’ll need is some training data to fine-tune your models to your domain-specific keywords, sentiments, topics, etc.

#2 IBM Watson

IBM Watson Analytics is a great business intelligence tool that offers an analytics engine along with a natural language querying tool. Their main component is called Watson Developer Cloud which gives access to APIs that perform a wide variety of natural language tasks, with substantially less for audio and visual data. IBM Watson is an easy-to-use platform and is a well-developed NLP platform.

#3 Stratifyd

Stratifyd is a text analytics platform designed to find trends and anomalies that point to changes necessary in customer, product and employee experiences.  Many users report user friendly and intuitive uses, with clear and concise language along with recommendations that help them access more insights in business practices. Stratifyd is a growing cloud-based software that is designed to support medium and large businesses. 

#4 MonkeyLearn

MonkeyLearn is a machine learning tool that automatically analyzes text and extracts actionable insights from data. You can use pre-trained text analysis models or create your own – and tailor them to your needs for a higher level of accuracy. Text analysis models include text classifiers and text extractors, giving users the opportunity to perform sentiment analysis, keyword extraction, intent classification, and language detection. MonkeyLearn embraces word clouds and offers its own WordCloudGenerator.

#5 SoluLab 

SoluLab is one of the top blockchain development companies with over 50M+ active users on their apps and a 97% customer satisfaction score. They offer a comprehensive set of data analytics services that convert historical and present data into actionable insights.  Users have used SoluLab to discover new drugs, genetic science, predictive medicine, network management, fraud and risk detection and much more. 

#4 OpenText

OpenText is a market leader in Enterprise Information Management software and solutions which enables intelligent and connected businesses in managing and understanding insight in BI. Their Business Network Cloud provides businesses with integration solutions that can securely connect data to people and systems. They offer services from strengthening end-to-end customer experiences to accelerating supply chain digitization. 

#6 Qualtrics XM Discover

XM Discover utilizes over 130 out-of-the-box industry templates and categorization models to analyze unstructured text data. XM Discover uncovers why customers reach out, how they feel, and what they plan to do next. Users use ​​XM Discover analytics to optimize their customer service chatbots to better understand people. As part of the Qualtrics suite, it integrates with other Qualtrics products for an all-in-one solution, from collecting data to analysis and implementation.

#7 OneAI 

Definitely expected, however, OneAI is a Text analysis service built for developers with no required background in AI/NLP. Our text analytics solutions help businesses gauge customer reactions and allow developers to build products utilizing our text analytics service without having to know code. Our product-ready APIs, no-code Language Studio, and vertically pre-trained models, allow every developer to deliver immediate value with zero risk.

Cost and time required to integrate text analysis into an application

Once a business concludes that they want to use AI solutions to make things run smoother, a simple question arises: do it from scratch or buy a ready SaaS solution? Let's take a look at both options. 

Creating software from scratch can be effective and rewarding in the long run. So, here’s a list of what is needed to start the development:

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1. Hire a team of data scientists and engineers. Here are the specialists you should think of:

2. Estimate development time. A simple software development can take up to six months; however, more complex text analysis software can take years. It is not possible to rule out unforeseen complications or errors in the original planning. In that case, the team will have to start all over. 
3. Maintain the software. Let’s say the exclusive software is up and running. In that case, we still need a team to support and upgrade it. And that means you either keep your dedicated team forever or hire freelance engineers every time you need bug fixing or a new feature. 

The SaaS tools, on the other hand, allow you to jump right in. What is needed to start using SaaS solutions:

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1. Pick the most suitable text analysis tool, tailored to your needs. Most of them are highly customizable and no coding experience is necessary. 
2. Pay only for the service. Even the most expensive ones will cost you much less than investing in the simplest custom software development.
3. No support is needed, any issues will be handled by the firm itself. No on-site maintenance, as servers are in the cloud.
4. The time of integration: implement APIs with just a few lines of code, integrate into your project - less than a day. 

In any case, every company knows its needs best, and if there is no need for a super custom solution, then the SaaS tool is really the best choice. Especially since SaaS tools became vastly customizable and easy to use, and there are many solutions for any budget. 

Using OneAI API’s

OneAI is an NLP-as-a-service platform. Developers can use our APIs to analyze, process, and transform language input in their code. No training data or NLP/ML knowledge are required. Language AI allows language processing with comprehension, of both meaning and information presented in text, generating structured data in context.

Estimated development time

Time estimation in software development is a complex process and requires experience to evaluate not only the development time itself but also lay risks and consider all hidden pitfalls. 

But it’s fairly easy to estimate development time using OneAI. You can start using OneAI APIs with just a few lines of code and integrate it with your project in less than a day.

Effort required

Using OneAI’s Language Studio is pretty straightforward and effortless. As you can see in the picture below, this is what the site looks like. The tool input is where you will be entering the content, alongside it is the pipeline box - this is where the magic happens.

Underneath the pipeline box, you can see the Language Skills, which are different ways we can apply AI to the content provided. There are different skills we can apply, such as ‘Summarize,’ ‘Proofread,’ ‘Names’, etc.

After uploading your data and choosing suitable skills just press “Run Pipeline” and you can copy the ready code in the “Generated code” section. Further, you paste it into your code editor and proceed with your tasks. Check more in this tutorial. As you can see it’s as simple as it seems.

Data analysis skills (Emotions, keywords, etc.)

OneAI operates with Language Skills. A Language Skill is a package of trained NLP models, available via API. Skills accept the text as input in various formats and respond with processed texts and extracted metadata. For example, use the “Sentiment Skill” to extract positive or negative sentiments from a chatbot session. Or “Keywords” to find the most significant words and phrases in the analyzed text.

Language Skills are the building stones of the Pipeline API. The pipeline API allows invoking and chaining multiple Skills to process your input text with a single API call. Pipelines can be defined by listing the desired Skills.

Summary

Text analysis is extremely useful for developers and businesses. From knowing how customers react to certain products to extract necessary information that originally might have taken hours to read, text analytics can reduce time and increase productivity. As a powerful tool that can help companies throughout a number of different industries, text analytics create actionable insights from data. The ability to save time, automate tasks, and increase productivity has never been easier, allowing businesses to reduce costs and improve customer service.

Check our OneAI’s Language Studio and start utilizing our text analysis Services to advance your business. 

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