Text Analytics: Understanding the Power Within Unstructured Data

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

Text analytics is a powerful tool in the realm of data analysis. It helps us make sense of the enormous amount of unstructured data that we encounter every day, from emails and social media posts to articles and reviews. Imagine trying to read and understand thousands of customer reviews about a product, or keeping track of what people are saying on social media about a brand. Text analytics helps gain insights from mountains of information quickly and efficiently.

In this blog, we’ll delve into what text analytics is, how it works and the variations underlying this important tool. In a second blog, we’ll break down the steps involved and offer use cases to further explain the role of text analytics in optimizing data for real-world use and driving success in any industry.

Text Analytics Defined

Text analytics, also referred to as text mining, is a computational process that involves the application of natural language processing (NLP), machine learning and statistical techniques to analyze and extract valuable information from unstructured text data. It encompasses various tasks such as text classification, sentiment analysis, named entity recognition and topic modeling.

In essence, text analytics leverages algorithms and linguistic analysis to transform unstructured text into structured data, making it suitable for quantitative analysis. This process enables organizations to identify patterns, relationships and key insights within the textual content, facilitating informed decision-making, trend identification and sentiment analysis.

For instance, in a business context, text analytics can automatically categorize customer feedback into topics, determine customer sentiment toward products or services and identify emerging trends from a large corpus of text, thereby assisting companies in optimizing their strategies and enhancing customer experiences.

So, in simple terms, text analytics is like having a smart assistant that reads and understands lots of text for us, so we can learn from it and make better decisions. It's a powerful tool in today's information-driven world.

Text Analysis, Text Mining and Text Analytics — Clarifying Terminology

Before we continue explaining text analytics any further, let us first clarify the difference between these commonly used terms.

• Text Analysis vs Text Mining
  In practice, there is no discernible distinction between text analysis and text mining. Both terms encompass the same fundamental process of extracting valuable insights from sources like email, survey responses and social media interactions. They can be used interchangeably.
  
  
• Text Analytics vs Text Analysis
  While text analysis encompasses the process of deriving qualitative insights by scrutinizing unstructured text, text analytics involves the extraction of quantitative data through the examination of patterns in various text samples. This information is typically presented in the form of charts, tables or graphs.

Example:

Consider a scenario where a large e-commerce platform wants to assess customer feedback on a newly launched product. By employing text analysis techniques, they can analyze customer reviews and survey responses to gauge overall satisfaction levels.

On the other hand, text analytics would provide a more in-depth understanding. For instance, it could help identify if there's a specific reason behind an unexpected surge in negative feedback, shedding light on potential areas for improvement in the product or customer service.

How Text Analytics Works

Text analytics is like teaching a computer to understand language, just like a person would learn. It trains software to link words with their meanings and to understand the context within unstructured data. It's similar to how we form connections between words, objects, actions and feelings when learning a new language.

The technology behind text analysis relies on two key principles: deep learning and natural language processing (NLP).

1. Deep Learning

Deep learning is a specific method used in artificial intelligence (AI) and machine learning. It uses so-called neural networks, which are software structures that mimic how the human brain works. Deep learning algorithms have significantly enhanced the precision and capabilities of text analysis This sophisticated technique lets text analysis software understand and process text much like humans process language.

2. Natural Language Processing (NLP)

Natural Language Processing, also called Natural Language Understanding, is a branch of AI that aids computers in understanding and processing human language. It employs language models and mathematical algorithms to train advanced technologies like Deep Learning, allowing them to analyze text data from various sources, including handwriting. NLP also utilizes techniques such as Optical Character Recognition (OCR), which converts images of text into readable documents by recognizing and interpreting the words within the image.

Text Analytics Techniques and Methods

In text analytics, various techniques and methods are used to extract meaningful insights from written or typed language. Let's explore some of the key approaches.

1. Sentiment Analysis

Sentiment Analysis, also known as opinion mining, involves training models to recognize the sentiment conveyed in text. Deep learning architectures like recurrent neural networks (RNNs) or transformer models are used for this. These models learn contextual representations, allowing them to distinguish between positive, negative or neutral sentiments expressed in the text.

In an e-commerce platform, customer reviews are analyzed using sentiment analysis. For instance, a positive review like "Exceeded my expectations!" is classified as a positive sentiment, while "Disappointed with the quality" is classified as negative. This automation provides quick insights for improving customer experience and making data-driven decisions.

2. Text Classification

Text classification involves training machine learning models to categorize text data into predefined classes or labels. This is achieved through the utilization of algorithms like Support Vector Machines (SVM), Naive Bayes or deep learning approaches. By learning from annotated data, the model discerns patterns and associations in the text to accurately assign categories.

Let's take as an example a news agency that receives a constant flow of articles covering topics like politics, sports, technology and entertainment. They use a machine learning-based text classification model to automatically sort these articles into their respective categories. For instance, an article about technological advancements is swiftly categorized under "technology," streamlining the editorial process.

3. Topic Modelling

Topic modeling is a way to find hidden or "latent" topics in a large amount of documents. It uses an algorithm called Latent Dirichlet Allocation (LDA) to guess what the main topics are. It's a probabilistic modeling approach that represents each document as a mixture of topics, and each topic as a distribution of words. By iterating through this process, the model uncovers latent topics.

In a research institution, for example, a large collection of scientific papers covers various disciplines. Topic modeling is used to efficiently categorize them. For example, papers on climate change and renewable energy are grouped under "environmental sustainability." This streamlines access for researchers, enabling quicker progress in their fields.

4. Named Entity Recognition (NER)

NER utilizes machine learning models, often based on Recurrent Neural Networks (RNNs) or transformer models, to identify and classify entities within text. These models learn contextual representations, allowing them to distinguish entities from surrounding text.

Imagine a global news agency processing numerous news articles daily. They employ NER to automatically identify and classify entities like people, organizations, locations and dates mentioned in the articles. For instance, in an article about a summit between President John Doe and Prime Minister Jane Smith in New York on September 10, 2023, NER would recognize these entities. This automation enables the agency to extract critical information swiftly and provide accurate news updates to readers.

These text analysis techniques leverage machine learning and statistical models to obtain actionable insights from unstructured text data. Each approach is tailored to specific tasks, enabling nuanced analysis of diverse text sources.

Knowledge is Power

With the rise of the digital age, the volume of unstructured text data continues to grow exponentially, making text analytics an indispensable asset in decision-making processes across various industries. By employing a range of techniques and methods, including sentiment analysis, topic modeling, named entity recognition and more, text analytics helps uncover valuable insights, trends and patterns within mountains of text-based information. It aids in understanding customer sentiment, streamlining operations, enhancing product development and staying ahead of the competition.

Understanding what it is and how it works is the first step to making text analytics part of your success story.