Text Analytics is an artificial intelligence (AI) based technology that extracts key insights from unstructured data, using natural language processing (NLP). Text analytics does away with the manual processing of text data. It categorizes the data in such a way that Machine Learning (ML) algorithms can analyse it based on different topics, themes, etc. This is how companies can draw meaningful and actionable information from survey responses, social media comments and videos, and product & service reviews.
Do you want to understand your customers on a deeper level?
Decipher the true meaning behind hashtags when your customers use them?
With Repustate's text analytics, you can do all that. Your customers have a lot to say but a few words won't do justice to the true meaning behind a social media post or review. With our text mining software, you can understand the voice of customer and serve exactly what customers want from your company.
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No. Text analytics and text analysis are not the same. They are separate techniques that serve different purposes. But both are required to draw insights from data. Text analysis understands the intent and meaning behind words, while text analytics allows that data to be presented in graphs and charts.
Text analysis uses Named Entity Recognition (NER) and text classification to detect entities (people, events, brands), topics, sentiment, and intent in a text. This translates to getting competitor evaluation, brand insights, location-based information, and such. To analyse data that is in audio and video formats, it uses Video Content Analysis (VCA) and Audio Analysis.
Text analysis also applies techniques such as concordance and collocation to identify words that occur commonly, or together. In this way, it is able to understand a text semantically and in the correct context. For example, the algorithm will figure out that at an airport, the words “flight delay”, will be used together more often than not. Or the term “air conditioning” is used more often together in hospitality than otherwise.
Data analysts can view all the semantic insights derived from text analysis in a graphical format using text analytics. These graphical representations give companies a tangible view of how the information is affecting them. They can use this information to study historical data and identify past trends, and strategize on what they can do for the future.
Machine Learning (ML) is a subset of artificial intelligence technology that can be trained to learn from experience rather than through an explicit written code. When fed structured, well organized data, it can infer meanings from it, and then provide a result based on how it has been trained. Over time, the ML model learns from its own responses. It becomes smarter with every iteration, learning correlations between entities and results, and so starts giving predictive analysis. This can be valuable for any industry - national security, healthcare & patient voice, public service, customer service, brand reputation management or even employee retention & training.
To train machine models, there needs to be clean, organized data. The unstructured data derived from customer reviews, health records, user-generated videos, employee surveys and the myriad forms of data that a business collects, needs to be extracted, and organized in clusters based on topics. Only then can it be fed to the advanced predictive models used in machine learning. That is why text analytics tools use natural language processing (NLP) that does the phenomenal job of data extraction and data clean-up so that the machine lead model can read the text. An NLP algorithm analyzes words just like a human would, for relevancy, variations in spellings, correlation, and many other criteria. It will do so for each language that it reads. Text analytics solutions that don't have the capability to read a language in its native tongue, will first translate the text into English. But doing so is dangerous and will give inaccurate results as each language is unique, and English may not have the lexical width to accommodate the morphology, grammar and lexicon of other languages. Which is why Repustate's Text Analysis API analyses languages natively, thus producing accurate results every single time.
Semantic Search is when an algorithm deciphers the meaning and intent behind a series of words and helps return improved and relevant search results. With semantic search and entity extraction, the program matches the sentiment or feeling to the subject, which can be a place, event, product, or person. And because sentiment analysis of the text is done based on aspects, or topics, that's how companies get an insight that is actionable. For example, if a comment says, "The book was great, but too expensive." and these are the same sentiments expressed on an average or more, a business knows that the book itself has a positive attribute, while its the pricing that needs to be thought over.
Repustate's Text Analysis API uses Natural Language Processing (NLP) technology that applies machine learning to find insights and relationships within your textual data. So how does it actually work? It follows the steps below:
Data Collection & Preparation
Data needs to be gathered and prepared so that it can be analyzed. This data can be an internal data set or an external one based on what the source is. To prepare the data for text analysis, it needs to be in a machine-readable format (CSV, XLS, JSON) so it can be ingested into any AI training pipeline.
Applying Text Analysis API
Run your input data through our Text Analytics API, and it quickly returns sentiment scores for each relevant topic, aspect, or entity ranging from: -1 for negative emotions, 0 for neutral feelings, and 1 for positive sentiment.
Using Visualization Dashboard
Once the text analytics tool returns the sentiment scorings, they are quickly turned into visual reports consisting of charts, graphs, and tables through a sentiment visualization dashboard so it's easy for the user to discover trends and patterns.
Learning to use sentiment analytics is easy! With over 3,000 organizations currently using Repustate's Text Analysis solution, you'll be in good company.
The best text analytics solution should give high-precision results and possess uncompromised processing speed, even with multilingual data. It should be easy to use, agile, and have an effective visualization tool. Every enterprise needs to choose the right text analytics software, based on its unique requirements. That’s why here are key aspects that a company must consider:
For a text analysis software to give accurate results, it needs to have a massive bank of data, or corpus, with which the machine model was trained with in the first place. Further, if the textual analysis is to be done on multiple languages, then the algorithm needs to understand the language in its native tongue or else it will give inaccurate results because it will lose the meaning of words in translations. Which means, it needs to have corpus in all those languages.
Feature & Aspect Classification
In order to be able to do a comparative analysis of business results, it is important that the text analytics tool or API performs aspect based sentiment analysis to identify the widest range of business aspects in your particular industry. This is the only way you can truly unearth insights from product reviews, customer forums, social media, and other platforms of data.
Language detection & analysis
A good text analysis API should be able to understand and decipher insights from data in multiple languages since most businesses are multinational. Each language differs from the other phonetically, in syntax, word structure and vocabulary, and so when data is translated into another language so completely different in etymology, it cannot give accurate results. THis is why a solution that can read the data in the native language will give the most accurate results.
A Knowledge Graph is a graphical depiction of the relationship between entities (people, places, events). It puts data in semantic context and so provides a framework for data analytics and integration. The bigger the Knowledge Graph owned by the text analytics software, the more accurate its results will be.
A company has terabytes of data that keeps getting generated non-stop on an ongoing basis in the form of emails, marketing material, advertising and sales collateral, presentations, videos, employee information, and more. Further, it needs insights from the data that is related to it, in the form of reviews, surveys, user-generated videos, news, and such. Now, if this need was also in multiple languages, it means that the text analytics software needs to have an extremely efficient processing speed for not only this mammoth data but also do it in a multilingual way without mistakes.
If a text analytics solution has no way of giving its analysis in an actionable way, then it doesn't really fulfill its purpose. Having a graphical representation of the insights not only helps in understanding the data better, but also in knowing if a business decision made on the basis of that insight was showing the desired results or not. With a visualization dashboard, these reports can be viewed in real time as well as shared among departments and team mates, which means that critical changes can be made in a timely manner to thwart brand reputation crisis or customer attrition.
The challenge in adopting any technical solution is the degree to which it is user friendly or difficult. Depending on who is going to be the end-user, the text analytics tool needs to be easy to understand and practical. Certain softwares may be more technologically advanced depending on the industry and need additional training of staff. But if it is going to be used in multiple departments simultaneously, the tool needs to be easy to grasp.
The text analytics API needs to seamlessly fit into your current platform without interfering with workflow. These softwares are available on the cloud as well as in on-premise installations, and so really, it is upto the company to decide, which one suits it better while keeping in mind data security.
Businesses have a lot to gain by using text mining solutions. They can deliver valuable information and be a catalyst for business intelligence in every industry conceivable. Some common applications of a Text Analysis API in business are:
Electronic Health Records
Text analytics in Healthcare can help tremendously in patient management and engagement - right from analyzing patient history to responses to varied dosages. Text mining is also helpful in Psychiatry where patients notes have been used to predict precedents of certain types of behaviours that call for certain treatments. A study published by the US National Library of Medicine discovered how analyzing texts of EMR reports helped in the prediction of seclusion of patients, and so demonstrating the importance of text analysis in evidence-based clinical decision-making.
Text mining is used to analyze client forums, call logs, surveys, social media channels, emails, news feeds and tweets. It gives businesses better insights into what their clients expect from them, and which areas need focus for improvement. Whether this information is derived from posts and comments, or through videos on channels like Youtube, TikTok, and employee engagement portals, the tool can decipher the data.
It is imperative to a company that it's public perception is impeccable. Text mining allows a company to understand data captured from Voice-of-Customer programs by analyzing tweets, comments, news articles, and other feedback that mentions any of its entities including its executives, investors, political parties and organizations it supports, employees, and partners. Companies can boost the status of their reputation in real time by taking measures to thwart the crisis.
Search Engine Optimization
Search engines like Bing and Google use text analytics to identify spams and filler content in content marketing websites. The engine can identify spelling variations, context, and intent, and so mark an email as spam; or it can penalize a company website that has been trying to increase its Search ranking by keyword stuffing.
Returns & Warranty
Text analysis is crucial in understanding data that a company can receive from hundreds of dealer service professionals, spread across locations with issues of warranty claims, and returns. A good text mining tool can read and categorize data even if it contains misspellings, acronyms, shorthand, technical words, or any other inconsistency. Whether it's large corporations like Costco or Walmart or small & medium businesses (SMBs) in industries as varied as tyres, automotive equipment or electronic white goods, a text analysis software can study both customer and technician comments entered into the warranty claim system, that the manufacturer can then analyse for reference and corrective measures.
Surveys & Reviews
Whether it's through social media posts, emails, customer service tickets, research surveys, news articles, or business documents, a smart text analytics tool gives you unparalleled insight into what your customers are really thinking about. Through its video content analysis (VCA) tool, Repustate's text analysis software also captures text from video formats using optical character recognition. The solution not only understands regular language but also short forms and slang, emoticons and emojis, and hashtags. No matter where your customers choose to speak, brands can understand the meaning behind their words.
Voice of Employee & Recruitment
Finding the right candidate to hire can be made easy with Text mining. It can go through thousands of records in a recruitment database to find the right candidate using keyword analysis. Ensuring that your star employees are happy at work means you can significantly reduce your employee attrition rate. Using feedback programs including voice, chat, and video throughout the employee journey can give you valuable insights on how to ensure a nurturing work environment, and deep employee-employer engagement.
Arguably the best, Repustate's text analysis tool is scalable and can be customized for sentiment rules no matter what industry your company is in. We have been doing this for more than a decade, which means our corpus for different industries is formidable. Repustate is a power-house in multi-linguistics, having developed a corpus that covers 23 languages and dialects as varied as Arabic, Mandarin, and French without having to translate them into English first, as most other text mining tools do. Furthermore, our text analytics solution is available as an API, and as an on-premise installation, so that companies never have to worry about data security, or workflow downtime.