Text analytics

What is text analytics?

Text analytics is the process of extracting meaningful information from raw, unstructured, and multi-formatted categorical data and presenting the contextual data to aid with decision making. It uses a combination of technology including natural language processing, machine learning, linguistics, visualization, and more. An example of text analytics would be a brand analyzing large volumes of emails, social media posts, customer support tickets, etc., to understand what people talk about them. The input text data could be also from offline sources like documents, physical files, and more like this.

A real-life example of text analytics

When Barbie movie was released in 2023, the marketing team used text analytics, going over more than 2.5 million words. This helped them gauge audience reaction and sentiments, which revealed how audiences’ opinions were deeply rooted with nostalgia and childhood experiences, how audiences fought passionately about Barbie vs. Oppenheimer, and how enthralled the fans were to spot life-sized Barbie Dreamhouses everywhere.

Text analytics – who it is for

There are many other real-world use cases where text analytics is quite helpful. Customer feedback analysis: Like the Barbie movie inferences we saw above, text analytics helps with understanding customers feedback, concerns, and suggestions, without going through a lot of messages. Brand monitoring: Many large brands use text analytics to see what their customers are talking about their brand, especially around new product launches. Support ticket prioritization: IT services companies use text analytics to analyze and prioritize the right customer service issue which needs to be addressed immediately. Market research: Product marketing and other research teams can use the solution to quickly learn and scrape details about their competitors’s products, queries, and user generated content. Detecting content violations: social platforms and media can use text analytics to quickly flag content that doesn’t obey creator rules. Examples like hate speeches, highly political content, fake news, etc.

The role of ML and AI in text analytics

ML and AI is crucial to extract the context and automate and scale text analytics. Here's how it helps: Natural language processing: A subset of AI that helps with understanding and processing natural language. In text analysis, NLP is used for tokenization, entity recognition, lemmatization, and more. Sentiment analysis: In short, this recognizes positive or negative emotions by scanning the text. It comes in handy to know what a group of users feel about the product/brand. Topic modelling: An ML technique that’s used to find hidden categorical insights from a large group of word data. This is to identify and decide the discussion themes. Text summarization: Does what the name suggests. Converts a long group of words into a concise one, easier to grasp. Text clustering: This groups similar texts into clusters, with or without labels. Can be used for knowledge discovery.
Entity recognition: Entity recognition understands and picks names, places, and other identity data from a group of words. Mainly used to understand common nouns discussed in a group of text along with relationship they share.

Benefits – text analytics

To retrieve actionable information: It’s hard to parse and go through millions of text files and messages. But, text analytics makes it possible, bringing us close to audience feedback, market trends, opinions, and more. Better decision-making: Be it media or marketing, text analytics helps with making decisions – faster and data-driven.
Time and cost savings: Text analytics in ticketing and customer service could route the right ticket to the right person, help with tagging and marking, saving time & resources for the team. Compliance monitoring: Brands could monitor the compliance breaches and reputation by keeping an eye and tackling them earlier.

How does text analytics work?

Data collection: This involves collecting data from one or multiple sources: social media, feedback forms, forums, other documents, etc. Data preparation: Since, this is text data, proper cleansing and prepping required to remove unwanted characters, noise, and everything irrelevant. Feature selection and tagging: This is about extracting the required features from the data and grouping and tagging them. Train the model: The ML/AI-based model is trained with the selected data so it can understand the pattern. Get insights: The last step of text analytics is to get meaningful context out of the large sets of data. Improve: Any model improves its output based on feedback given, giving more accurate inferences next time.

Challenges in text analytics

• There are a few challenges that could complicate text analytics.
• Noisy data, uncleaned and hard-to-clean data.
• Language ambiguity could lead to more confusions, even if the extracted insights are accurate. For example, models cannot parse sarcastic and sardonic statements, that express one thing and imply another.
• Vocabulary and expressions change for each sector, making it important to customize the solution to suit the business.
• Parsing sensitive information and data privacy concerns.