Beyond structured, tabular data, businesses have access to data hidden in other formats too such as emails, social media posts, customer reviews, mobile data, data logs, blogs, images, audio, and video formats. The technology that enables gathering, storing and mining that data, cleaning, and classifying it for running reports and dashboards is text analytics.

Manually going through voluminous data to identify patterns and spot trends can be time-consuming and prone to errors. Text analytics software automates the process and enables efficient use of machine learning and natural language processing algorithms for the purpose. teX-ai, is one of the best text analytics products and solutions in the market, that helps identify actionable insights from unstructured text data for improved decision making.

Text analytics uses machine learning and natural language processing (NLP) solution to extract insights from unstructured documents. It speeds up the discovery of important information through automation of pattern and trends analysis and presenting the findings using visualization tools such as bubble graphs, heat maps, and polarization graphs for quicker understanding.

The key benefits of text analytics include:

• Finding patterns to leverage data for actionable insights
• Simplifying filtering, searching, and cross-referencing complex, unstructured data for a unified view
• Accessing enterprise-wide data for a holistic perspective
• Improve the outcome of analytics on structured data by injecting unstructured data

Text analytics software can extract and analyze unstructured data from a variety of sources including and not limited to:

A solution such as teX.ai ^TM has three primary processes:

Text Extraction: Extraction of text from unstructured sources such as PDFs, images and websites is automated and converted into structured format using ML/DL/Scraping methods. This is exported to CSV, JSON and many more formats.

Text Summarization: A bite-sized summary of huge documents is created using the NLP stack app to build the crux of the collection through topics, clusters and keywords. NLP models are used to train the system to identify patterns and unearth the insights as per the modules – Topic Models, Doc Clusters, Key phrase Highlights, Name Entity Recognition (NER) Graphs.

Text Classification: Each document is assigned a category based on a multialgo-ML classification engine customized for parsing short text stories and detecting the category. Labeled training data is loaded and the teX.ai ^TM engine from indium learns using the ensemble ML models to fit the ideal model to the best-suited label.

A good text analytics solution will be capable of handling many languages. teX.ai ^TM, for instance, can work with almost all the major global languages including all Latin languages, Japanese, Mandarin, Thai, and Arabic apart from English.

Text mining and text analysis mean the same and are used interchangeably. While text analysis is a qualitative process, text analytics is quantitative. While analysis reveals relevant information within the text, analytics enables identifying patterns across different texts and even graphs, reports, tables etc. Text analysis is limited by the ability to decode the nuances of human language while in analytics, the text is only used for insight gathering.

Human communication is a mix of explicit and implicit meanings. Sometimes, the meaning of a word can change given the context. For instance, ‘good’ can mean exactly as defined in the dictionary or mean the opposite if said sarcastically. The meaning of some words can change based on the surrounding words and phrases, objects, and scenarios, etc. ‘A few’ and ‘few’ mean completely two different things.

For text analytics tools to understand exactly what content means, it is not enough to train the tool to understand the word meanings but also enable context-based interpretation. A tool such as teX.ai ^TM enables this by training it using large data sets.

By enabling semantics-based analytics, the quality of the data, as well as the categorization, improves. The system can be trained to make associations between words and their qualities and not just the synonyms. This can help improve the classification of content, identify the sentiment of a review more accurately, predict user behaviors better, and facilitate entity extraction.

Text analytics has a variety of applications include:

• Improved risk management due to access to data from within the organization as well as external sources
• Better customer care service to extract insights from surveys, customer feedback, and customer calls, etc.,
• Fraud detection combining structured and unstructured data analytics
• Business Intelligence is another area where analytics is being used to assess competition, industry trends, price movements, and so on to help you improve your decision making
• Social Media Analysis to track and interpret the content from online sources such as news, blogs, and emails, etc.
• Search Autocomplete is another capability that text analytics enables and improves search results as well
• Financial institutions can assess the creditworthiness of prospective customers, even with no prior records by analyzing geolocation data, social media posts, and their browsing behavior
• Marketing communications can benefit immensely from understanding customer conversations on social media and customizing them based on user preferences
• Sentiment Analysis solution captures the mood of the customer and helps with effecting improvements in product and services based on it