Understanding basic Text Anlytics: How it works and how businesses can take advantage
The written text and spoken words are entrenched in our everyday communication. In a world overloaded with data and information, interpretation of text, words and their underlying sentiment has become more significant than ever. However, this complex data and the wealth of information generated every day is practically impossible for humans to unlock, examine and comprehend.
This is where AI-backed Text Anlytics comes to the rescue. It helps users/companies unlock and extract troves of unstructured yet valuable written information from different sources and platforms to get insights into people’s habits and understand patterns and interests, enabling companies/users to take critical business decisions and solve problems based on customer wants and needs.
Be it emails, tweets, blogs, online reviews, Instagram posts, survey notes, forums or any other form of written communication, Text Anlytics delves deep into these scattered materials, collates, breaks them down and examines them to derive useful insights.
How does Text Anlytics work?
While this new-age concept sounds ground-breaking and revolutionary, the questions in people’s minds are ‘how is it done?’ or ‘what goes into this complex yet fascinating task’?. It is all achieved using the technique NLP (Natural Language Processing). It is a type of machine learning model used effectively to make sense of the written words and understand the meaning, intentions and emotions behind these words. The three fundamental operations in Text Anlytics are — finding (extracting entities — words, date, time), labelling (assigning tags based on the content) and grouping (organising or classifying texts or sections of the text into groups). Here are a few comprehensive steps for an accurate understanding of the process:
Sentence tagging and chunking
This is a process where the analytics tool recognises parts of speech in a sentence or even places, dates and locations. Connecting the information intelligently with data helps in the easier processing of the texts and sentences. Once the parts of speech are recognised in the words of a sentence (noun, pronoun, verb, preposition etc), the sentence is broken down and chunked into sections. For example, in a sentence like — The product was easy to use and has excellent features — The sentence will be recognised as — The product (noun phrase) was easy to use (adjective and verb phrase) and has excellent features (adjective phrase). The AI tool has the ability to identify these parts of speech and assign each word or section into different sections of the sentence.
Parsing
This is a critical step in understanding the sentiments in a sentence. Using the syntax or the structure, the sentence can be broken down into parts and characteristics or in other words, emotions can be assessed. For example, in this sentence — The company was doing poorly until new management stepped in — the first part is negative and the second one is positive. Similarly in this sentence — The company did poorly despite new management — both the parts are negative. Therefore in parsing, different keywords in a sentence are taken into consideration and corresponding sentiments are recognised.
Text Anlytics can also connect related sentences by examining similar words and patterns. Overarching sentences can be connected and an overall analysis can be drawn. The machines are also capable of identifying different languages and breaking text documents into pieces by understanding each word and character.
How can organisations benefit?
While these above steps can help break down huge volumes of unstructured pieces of documents or texts for deeper analysis by understanding words and phrases, this fine art of Text Anlytics helps businesses in sentiment analysis, theme extraction, research, document management and even threat assessment. Here are a few ways businesses can reap the benefits of Text Anlytics:
- It helps businesses understand the intent behind a text. For example, if users in a particular social media platform are asking a query about a newly launched product along the lines of — ‘What are the launch offers’? or ‘What discounts can we expect’?, the AI will examine it and release it as — “intent to get discounts.” This will help companies devise a strategy to introduce offers and discounts to launch customers.
- Data extraction will be easier and structured. If information about a certain demographic group is needed from a bunch of reviews posted by users, then the analytics can help churn out that information by compiling the titles and ages of users as given in the survey forms or even in their social media accounts. Therefore, specific data or information points can be extracted from a larger text.
- It helps understand market and user sentiments. Today, most of the feedback on services, products or businesses are scattered across digital platforms. These are collated and emotions behind words are analysed to draw an inference. For example, if a customer states — “The cell phone is loaded with some exciting features but the product looks and feels bulky and heavy”, the AI will feed it as — “Feeling of happiness, feeling of regret.” A wider analysis like this and similar feedback of this kind from varied sources, extracted through Text Anlytics, will help the business make necessary changes to the product.
- A better understanding of the main theme or the talking point of a text. About a certain subject that has been spoken about at length, the analytics tool will try to understand what the larger theme of the communication is.
For example, if a furniture store is newly launched in the city and there is a lot of noise around it and thousands have taken to online platforms and social media to write on the store, the AI tool can help pick common keywords and categorise them into a broader topic. For example, if reviews have keywords mentioned as “congested”, “Not spacious”, “No space to view products”, then AI will categorise it as “Size and space requirement”. This will help businesses solve problems effectively and efficiently.
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