5 Text Analysis (NLP) Buzzwords for Market Research
Read and learn about the key concepts behind natural language processing (NLP)
Text analysis is becoming an essential element of market research, especially when it comes to understanding your customer segments. Businesses of all kinds can benefit from the added efficiency of text analysis to improve the customer experience and move one step ahead of the competition. One key element that powers text analysis is Natural Language Processing (NLP).
NLP is an umbrella term for text analysis that describes a systematic process of breaking down human thoughts into a format the computer can understand. There are now broad applications of NLP that include the way search engines process data to understand our questions, to customer support chatbots, and to voice activated assistants. Simply put, NLP is a smarter way of quantifying words.
If you want to understand NLP’s role further, here are five key text analysis buzzwords you must know.
1. Bag of Words
“Bag of Words” is the method of organizing words found in your customers’ feedback from the most to the least popular. Rather than focusing on grammatical structures, this method is only taking account of the most popular words or “grams”.
I love the food and decor.
The food was delicious.
The food was so-so.
While we can now see the following three sentences are related to food, a drawback of this method is that the understanding of the order of the words is lost in the process. Therefore, the next step in quantifying words is to use N-grams.
As we’ve just demonstrated, quantifying words by themselves will identify the most popular words, but context is everything. With N-grams (Bi-gram, Tri-gram analysis), we can also look at combinations with adjacent words, commonly in both directions. This way, N-grams are used in text analysis to help clarify the meaning surrounding a word of high-frequency.
For example, look at this sentence.
“I love the food.”
The bi-grams you can find in this sentence are:
The tri-grams you can find are:
“I love the”
“love the food”
Simply put, an “n-gram” is n amount of words put into one gram. N-grams are important for helping to analyze comments quickly by identifying commonly used sets of words. We can use this information to identify a general meaning, but we still need another method to confirm the extremity or deeper meanings of the customers’ comments. This is where we can continue by using Sentiment Analysis.
3. Sentiment Analysis
Do you want to know how much your customer liked or hated something? This is where sentiment analysis can be applied. It gives each word or phrase a positive, neutral, or negative value by assessing the sentiment of each word or set of words. The score, or polarity of each word is then aggregated across a scale from negative to neutral to positive. This allows us to rate the feedback, but we still need a way to confirm what we know about the comments by double-checking the parts of speech present in the feedback. At this point, we can move to Parts-of-Speech Tagging.
4. Parts-of-Speech (POS) Tagging
Parts-of-speech tagging is a process that aims to recognize different parts of sentences. The main purpose is to identify the grammatical components of a written sentence so they can be easily identified. Knowing the rules of a language is essential to communication, and these tags help fine-tune the previously mentioned methods used in text analysis.
If you want to further break down this process and identify new entities, the final step is to employ Named Entity Recognition.
5. Named Entity Recognition (NER)
Named Entity Recognition (NER) builds on POS Tagging and is a very useful application of NLP. The goal of NER is to accurately extract real-world entities from a sentence. This means knowing whether a certain word like “Kevin” is a person’s name, “Canada” is a country, “smartphone” is a consumer good, “Microsoft” is a company, and so forth. A simple job for a human, but given thousands upon thousands of customer feedback, it’ll quickly help you categorize your words into major themes.
Text analysis, otherwise known as NLP, is a smarter way to quantify qualitative data. Hopefully these five buzzwords will give you the foundation to get the most out of your customers' insight.
Got questions about what text analysis approach is suitable for your customer base? Feel free to post them below or contact us here . We'd love to hear from you.
Kai Analytics provides text analysis services to help businesses understand their customers' insight. We leverage natural language processing and machine learning techniques to quickly clean, summarize and categorize thousands upon thousands of customer feedback and reviews into major themes.
No software subscription or coding ability is required. You have the information, we make it useful.