In this ultimate guide to text analysis, we explore the different approaches for unlocking the full potential of your text data.
Every day, around 1.145 trillion MB of data is created across the globe. It’s estimated that up to 90% of this data is unstructured. Unstructured data can include text, videos, images, social media posts, audio and emails. These types of data can reveal people’s thoughts, feelings, frustrations and desires in ways that quantitative analysis can’t.
However, analysing text data can be challenging. Why? Because generating market research, customer and competitor insights from text data can be costly, slow and labour intensive. The market for advanced text analytics solutions is still young, and many organisations are still doing this work manually. But for businesses looking to get a step up on the competition, using text analytics to leverage qualitative data as a source of business intelligence represents a huge opportunity.
This ultimate guide gives you an overview of the key things to know about text analysis. We’ll cover the various approaches, how it works and why it’s an essential part of any businesses intelligence function.
What is text analysis?
Text analysis, often used synonymously with text mining, refers to the process of converting unstructured text into meaningful data. Text analysis transforms text into a more structured format that helps users understand the important elements of a written data set.
Text mining vs Text analytics
Text analysis is often used interchangeably with text analytics. However, there is a subtle difference.
Text analysis pulls out information such as linguistic features that are present within a text data set, through computational analysis.
On the other hand, text analytics focuses on turning text analysis into visualisations, quantifiable metrics and other formats that are business relevant.
For businesses, text analytics could be used to analyse voice of the customer feedback such as review data, social media posts or evaluating open-end survey responses in order to find recurring themes, trends and patterns. Not only does this save hours of manual analysis and erase the degree of human error, but the discovered insights can then be used as a means of business intelligence to inform strategic decision making.
Types of text analysis techniques
With an assortment of text analytics tools on the market, each employs a unique combination of approaches for making sense of text data.
Approaches can range from basic text analysis methods which ultimately serve to summarise the text, to more sophisticated machine learning approaches. Some of the most popular text analytics techniques include:
Frequency of words
A simple way to identify dominant themes in a body of text is to count the words (and in some cases phrases) that are being used. By analysing text data in this way, you can easily identify common issues, problems, and recurring themes. Typically, this form of analysis is used for word clouds and is text analysis in its simplest form.
Text categorisation (or tagging)
Text categorisation, also known as text tagging or classification, enables businesses to make sense of their data in an automated way. By utilising NLP, text classifiers can automatically evaluate any text and assign pre-defined tags based on themes, topics or sentiment.
Sentiment or emotional analysis
Sentiment analysis enables you to uncover insight into customer opinions, and how a person feels about what they are writing about. Sentiment analysis pinpoints whether an interaction is positive or negative, however there is no way to automatically discern, for example, if a negative sentiment is caused by anger, disappointment or sadness.
Emotional analysis uses text categorisation for a deeper evaluation of consumer opinions and feelings, and is able to identify emotional nuances within language. This type of analysis overcomes the limitations of sentiment analysis, and presents a more holistic picture of an audience’s emotions towards your brand.
Topic analysis (or modelling)
Topic modelling is a text analytics technique that identifies dominant themes or topics in an unstructured data set. Through machine learning, topic modelling is able to categorise a text by identifying the keywords within it.
Named entity recognition
Named entity recognition is another text analytics technique used to identify names, places, organisations, and time periods in a written text. It can help you quickly gather specific information in order understand what a text is about, or to collect important information.
What are the benefits of text analytics?
In today’s world, businesses are focused on developing better understandings of their customers, competitors and employees. Having the capability to analyse large volumes of text data is foundational to cultivating this deeper level of understanding. Text analytics can show how people are feeling, whether they like your brand or product, the topics that are trending amongst a target demographic and precisely what is being talked about.
The top benefits of text analytics include:
1. Easily identify key themes, topics, trends and emotions in text
Text analytics tools discover recurring themes within a text, using a wide range of approaches. This can range from basic rule-based methods to more sophisticated models which utilise AI to improve the accuracy of text classifications. Market researchers and insights professionals are then able to understand key trends amongst consumers, competitors and the wider industry.
2. Cut through the noise
Text analytics breaks down language into its constituent parts to discover what really matters within the text. It’s able to separate the good from the bad, the sexy from the boring, which makes it an invaluable tool for cutting through the noise and finding interesting nuggets of information in large piles of unstructured text.
3. Quick and efficient analysis at scale
Text analytics enables you to analyse qualitative information without having to read every single word. It provides an incredibly effective way to analyse vast amounts of data – offering immediate, accurate results without sacrificing efficiency.
4. Understand the how and why behind quant metrics
Text analytics helps users develop a complete picture of an audience, allowing you to spot problems and opportunities, discover trends and patterns and create useful knowledge for your business. Combining text analysis with quantitative metrics will enable you to develop a 360-degree view of consumer trends, business performance and identify opportunities for improvement.
Text analysis use cases
Leading brands, charities and agencies are using text analysis to make sense of their unstructured text data:
Voice of the customer analysis and insights
Topic, theme and trend tracking
Analyse the language around a given topic or brand, tracking how themes change over time or comparing how opinions differ across different demographics.
Competitor intelligence and benchmarking
Use comparison as a new approach to competitor benchmarking and glean sophisticated insights on how your brands and products stack up against the competitive landscape. Discover how customers are talking about your brand in comparison to competitors, or compare brand positioning of other players in the market.
Measuring campaign effectiveness
Utilise comparative text analytics to measure the effectiveness of your marketing efforts. Go above and beyond quantitative metrics and analyse the conversation before and after a campaign implementation.
Analysing text data with Relative Insight
Relative Insight’s text analytics platform compares two or more written data sets to unearth the differences, frequencies and similarities in how audiences, brands and organisations speak.
By combining natural language processing and comparative linguistics, Relative Insight is able to ‘read’ a text and highlight the topics, words, phrases, grammar and emotions that are present. However, it doesn’t stop there.
Our distinct comparative approach to text analytics enables organisations to understand what makes a text unique, as it’s the differences in text that are truly insightful and relevant.
In taking a comparative approach to text analytics, Relative Insight incorporates context into the analysis process in ways that other text analysis techniques do not, isolating what is different or unique about one text in comparison to another.
Knowing the differences in language across demographics, time periods or competitors means that businesses can gain better intel into customer behaviours, how consumer trends change over time, or how brand voice compares to the competition.