Text Analytics: Harnessing the Power Within Unstructured Data

This blog post is the second of two exploring how text analytics can drive success in any industry. 

In today's world, where information is everywhere, text analytics has become incredibly important. It helps businesses listen to what their customers are saying, it aids researchers in studying trends and opinions and it enables organizations to make better decisions based on what people are writing online. 

In Part 1 of this blog series, we explained what text analytics is and how it works. Here, in Part 2, we’ll delve into the main stages of the process and describe use cases that showcase the benefits of text analytics.

Main Stages in Text Analytics

Text analytics is a complex process that involves several critical stages for extracting insights from unstructured data. These stages are:

1. Data Gathering

In this initial stage, the focus is on the acquisition of relevant text data from a variety of sources. This can encompass internal reservoirs such as databases, emails and company documents, as well as external sources like social media platforms, online forums and news outlets. The data retrieved may be in different formats, and it's crucial to ensure its integrity and relevance.

2. Data Preparation

The preparation of raw text data is an indispensable phase in text analytics. This involves a series of intricate operations:

• Tokenization: Breaking down continuous strings of text into discrete units, or tokens. This process establishes the foundational elements for subsequent analysis.
• Part-of-Speech Tagging: Assigning grammatical categories (e.g., noun, verb, adjective) to each token. This step provides linguistic context, aiding in understanding sentence structure.
• Parsing: Determining the syntactical structure of the text, enabling the identification of relationships between words. This is crucial for comprehension.
• Lemmatization: Reducing words to their base form (lemma) to standardize them. For instance, "running" becomes "run."
• Stopword Removal: Eliminating common, low-information words (e.g., "and," "the") that contribute little to the semantic content of the text.

Data preparation aims to refine and structure the text data for more effective analysis in subsequent stages.

3. Text Analysis

The Text Analysis stage is the heart of the text analytics process. It involves applying specialized techniques to process the prepared text data. This includes methods like text classification, which assigns predefined categories to text based on content, and text extraction, which identifies and extracts specific information. Additionally, topic modeling is used to categorize documents into meaningful topics. This stage is pivotal in deriving valuable insights from unstructured text data, forming a crucial foundation for subsequent visualization and decision-making.

4. Visualization

In this stage, the results of the text analysis are transformed into visual representations. These can include graphs, charts, heat maps, and other graphical elements. Visualizations serve to make complex textual insights more accessible and understandable to users.

By meticulously progressing through these stages, organizations can harness the full potential of text analytics, extracting valuable information from unstructured text data for informed decision-making.

Use Cases and Benefits of Text Analytics

Text analytics plays a pivotal role in modern data-driven decision-making. By extracting valuable insights from unstructured text, businesses and organizations can unlock a multitude of benefits. It enables them to understand customer satisfaction levels, track emerging trends and streamline operations, ultimately leading to more informed strategies and improved outcomes.

Here are some of the key text analytics use cases and benefits it provides:

1. Enhanced Customer Understanding: Text analysis tools are employed to analyze customer reviews, comments and feedback from various sources like social media, customer surveys and online forums. They allow businesses to gain valuable customer insights related to their opinions and preferences. This understanding is invaluable for tailoring products and services to meet customer needs.
2. Productive Social Media Monitoring: Text analytics software is commonly used to monitor and analyze conversations on social media platforms. It helps businesses keep track of discussions related to their products, services or industry, enabling them to respond promptly to customer inquiries or concerns.
3. Improved Product Development: By analyzing customer survey responses and feedback, companies can identify specific areas for improvement in their products. This leads to the development of better, more customer-centric offerings.
4. Efficient Customer Support: Text analysis software can be used to automatically categorize and prioritize customer support tickets. This streamlines the work of the customer service teams, ensuring timely responses to critical issues.
5. Empowered Market Research and Competitive Analysis: Text analytics is instrumental in processing large volumes of text data from market research surveys, focus groups and customer interviews. It helps in identifying emerging trends, understanding consumer preferences and gaining competitive intelligence. This type of business intelligence is crucial for staying ahead in a competitive landscape.
6. Efficient Risk Management and Compliance: In industries like finance and healthcare, text analytics is used to analyze documents for compliance with regulatory standards. It helps in identifying potential risks, ensuring adherence to legal requirements and mitigating compliance-related issues.
7. Proactive Brand and Reputation Management: Organizations use text analysis software to monitor mentions of their brand across different platforms. By tracking and analyzing brand-related conversations, businesses can assess their brand perception and address any emerging issues or trends. They can proactively address any negative feedback or issues, safeguarding their reputation.

Spotlight on InfoNgen: Text Analytics Software

Filter the Signal from the Noise

Developed by EPAM, InfoNgen is an enterprise-grade text analytics, sentiment analysis and enterprise search tool. It helps sift through noisy unstructured text data and gain valuable insights. Utilizing the capabilities of NLP and machine learning, it enables businesses of all sizes to efficiently search, gather and analyze crucial updates necessary for maintaining competitiveness and making informed marketing choices.

Here are some of the features and functionalities that make InfoNgen stand out:

1. Rule-based semantic & AI tagging includes automatic identification of elements or concepts, entity linking algorithm, automatic summarization of documents and exclusion of irrelevant content.
2. Prebuilt taxonomies available out of the box contain more than 600,000 entities for domain-specific ontologies for finance, pharma & life sciences, legal, manufacturing and other industries. In addition, employing white-glove service it is possible to build, ingest and integrate the clients’ enterprise taxonomies.
3. Sentiment analysis goes beyond just the basic sentiment of “positive” or “negative.” Leveraging neural networks, InfoNgen can recognize how the author feels about not only a company as a whole but also its unique attributes and features. 
4. Knowledge graph approach extracts concise insights from mountains of unstructured data. It is usually used for identifying corporate actions, products, competitors, fines, etc.
5. Discovery portal, a market-tested web interface, provides advanced capabilities to access, filter and curate content from all different public sources, premium subscriptions or private repositories necessary to monitor relevant content.

InfoNgen Use Case in Financial Services

Streamlining Application Form Processing

Customer Problem: A global insurance services provider faced challenges in efficiently handling a large volume of application forms, leading to manual reviews and data entry. This process was both labor-intensive and prone to errors.

Solution Proposed: InfoNgen introduced the Document Extraction & Processing System (DEPS) for data extraction. It also implemented robotic email processing, automatic document classification and rule-based prioritization. Additionally, InfoNgen employed OCR and advanced character recognition technologies for accurate data extraction.

Achieved Results: The implementation of InfoNgen's solutions led to a streamlined form processing workflow, significantly enhancing efficiency and accuracy. The automated data extraction and validation processes resulted in faster processing times and reduced errors. Moreover, the client's data was unified for improved accessibility and analysis.

Future-Proof Your Business

Text analytics, together with innovative platforms like InfoNgen, opens up new horizons for businesses, researchers and decision-makers, helping them make informed choices, gain a competitive edge and transform unstructured text into actionable knowledge.

Across industries, the sheer volume of data is constantly growing. You need a partner that can help harness all that information, transforming it from chaos to orderly, invaluable insights that drive success.