Introduction
Text data contains a lot of information and mostly is the primary source of insights for the companies or educational institutions. Customer feedback, product reviews, Survey data, etc., are mainly in text form. An in-depth analysis of these data is critical to know more about products, consumer sentiment, intention, and make business decisions accordingly.
This article will look at different and most common approaches to extract insights from the text data.
Different Feature extraction methods
I have discussed about six most common feature extraction practices that one should perform to get most out of the text data.
1. Sentiment Analysis (aka Opinion Mining)
Machine learning and Natural language processing make Sentiment analysis possible, which helps us interpret and classify emotions in the text data.
Sentiment analysis is often used to discover sentiment in products, Service Quality, brand reputation, and in Understanding customers.
The most common type of sentiments category: Positive, Negative, and Neutral. We can add more categories based on the business use case like “Very Negative” and “Very Positive”.
Vast volumes of text data are created every day from emails, chats, social media conversations, reviews, feedback to articles, and a lot. But it’s hard to analyze and understand all these. As per the IMB, It’s estimated that about 80% of the world’s data is unstructured, which means unorganized.
Sentiment Analysis helps companies and businesses make sense of all this unstructured text data by automatically understanding, processing, and tagging it.
Few of the most common use case of Sentiment Analysis are:
- Social Media Monitoring
- Brand Monitoring
- Customer Feedback
- Market Research
2. Intent Classification
Machine Learning and NLP helps link text data to a specific purpose or goal: Intent classification.
In simple terms, a classifier examines a piece of text and classifies them into intents like Subscription, Price, Support, Demo, Purchase, etc. These intents categories are useful to understand the intentions behind customer queries, emails, chat conversations, etc., and assist and answer customer queries correctly.
The Sooner we answer the customer queries, the better the customer experience, the higher the conversion rate, thus the better sales. According to Harvard Business Review, Responding to Potential customers within an hour increases the chance of meaningful conversions by upto seven times.
Well, manual detection of the intent behind customer queries through email, chat conversation, social media post, etc., is slow and time-consuming. Hence Intent classification becomes very crucial.
With Machine Learning, we can automate the intent classification from the queries in real-time and reply instantly and increase potential conversions.
For example, imagine you’re interested in subscribing to SurveyMonkey, and you send them a query asking:
“Hi, I’m a Developer. I’m looking to integrate the survey form into my website. I wanted to know about the number of responses per month with the Advantage Annual Plan. Also, does this price is for a limited time, or this would always be the same?”
An Intent Classifier could easily categorize this query as a clear Purchase intent.
3. Entity Extraction:
Machine Learning and NLP enable machines to automatically understand, identify, or extract vital elements from text into predefined categories like product name, event, and location. We refer to these categories as entities.
As I mentioned earlier, Vast volumes of text data are created daily from emails, chats, social media conversations, reviews, feedback to articles, and a lot. All of these are unstructured data. And Entity Extraction helps transform these unstructured data into structured by classifying them according to predefined categories.
Here’s an example:
Entity Extraction is being used throughout different domains and enables businesses to find meaningful information from numerous unstructured text data. Going through hundreds of surveys, emails, or product reviews would require countless amounts of manual working hours. But thanks to automated entity extraction, One can process a large chunk of Text Data in minutes.
Few examples where Entity Extraction is being used:
Search Engines to understand the search queries,
Platforms to improve Content Recommendations,
Chatbots to interacts with individuals, and
Teams to automate slow tasks like data entry.
4. Text Summarization
Text Summarization is an ML-driven automated process that reduces the lengthier text and creates a short new version that contains the most relevant information present in a larger text file.
Text summarization is being used to summarize news articles, Academics papers, Web pages, etc.
Most common example of text summarization is Google featured snippet which includes a summary of answers extracted from a webpage. See the below image:
Another popular example of text summarization is in data entry work where relevant information can be automatically extracted from the product description and automatically entered into the database.
5. Text Categorization
Natural Language Processing enables us to analyze the text and then automatically assign those text to a set of predefined tags or categories based on its content.
Unstructured text data is everywhere, such as emails, chat conversions, websites, etc., and assigning those unstructured text data to certain categories becomes very important to extract real value out of those unorganized data.
For example, a business can categories upcoming queries and then assign that to the appropriate support team to quickly serve customer needs and resolve issues.
These approaches from intent classification to text categorization are a very powerful tool to extract valuable information from unstructured data. It’s magic; literally, you can analyze thousands of unorganized text data in seconds and extract useful insights like intent, theme, summary, sentiment, etc., from the text data.
Valuable insights from these unstructured data help businesses in transforming leads into customers, easily identify the key elements in a text like names of people or brands, in brand monitoring and product analytics, etc.
