How businesses are taking advantage of text analytics
As businesses become increasingly digitalized, the amount of information generated escalates exponentially. Companies produce a lot of data helpful in understanding:
- What customers think
- How well employees work.
- How people see products, services, and brands online.
This is where text analytics comes in.
In this blog post, we’ll explore everything you need to know about text analytics and how it can be used in business contexts.
What is text analytics
Despite how powerful data management can be for organizations, many people do not know the benefits of text analytics for businesses. What is text analytics? Text analytics is the process of transforming unstructured text into orderly and usable information. It can also be understood as an “umbrella term” referring to AI-empowered methods of giving meaning to unstructured data.
Text analytics breaks sentences and phrases into components to evaluate their meaning through complex algorithms and software rules. Additionally, text analytics combines statistical, linguistic, and machine-learning techniques to establish patterns, topics, events, subjects, and feelings.
This is usually done through:
- Sentiment analysis
- Topic modeling
- Named entity recognition (NER)
- Term frequency
- Event extraction
Consequently, data analytics has not only been implemented in businesses but also in governments, universities, and mass media to make critical decisions.
Healthcare industry: Text analytics has been applied in several industries, fields, and activities. For example, text analytics has been used in the healthcare industry to identify doctors’ reports and medical records patterns to improve diagnosis and prevent disease outbreaks.
Media and journalism: Media and journalism research has applied text analytics to establish trends and patterns in complex datasets. This field of social research has utilized this technology to recognize information trends related to significant events. These events include armed conflicts, recessions, inflation impacts on small businesses, and the COVID-19 global expansion.
Marketing and product development: AI-textual analysis has allowed specialists to identify trends in customer sentiments. The information gained through this analysis has allowed companies to improve customer service and innovate products according to users’ feedback.
Text analytics and text mining: What sets them apart
Text analytics and text mining are two closely related fields. However, there are distinct differences between them.
Text mining: it generates qualitative insights from unstructured texts. Also, it is more focused on exploratory analysis and intends to uncover hidden patterns and correlations in data.
Text analytics: it produces quantitative insights and develops sentimental analysis, text classification, and topic modeling through simple rule-based methods.
Moreover, text analytics is more concerned with understanding texts’ content and usability.
How does text analytics work?
Text analytics develops in two phases: data gathering and preparation.
Textual information is initially dispersed in organizations’ internal databases. Companies typically find this data in customer service chats, surveys, reviews, and users’ comments on social media. This information is collected through text analytics, and a language identification process begins.
In text analytics, language identification allows you to identify the number of languages in a given text. This is helpful in many circumstances, including when businesses need to analyze texts in multiple languages or target a specific language market.
Machine learning algorithms can undergo various preparatory steps before analyzing textual information. Here we recap these steps:
- Tokenization: in this step, sentences and phrases are broken apart into their component pieces.
- Sentence breaking: longer texts require sentence breaking to separate statements with punctuation marks.
- Part-of-speech tagging (PoS tagging): this step entails determining the part of speech token in a specific document and tagging it. PoS tagging is crucial for reliable sentiment analysis for organizations.
- Chunking: this is the term given to various sentence-breaking systems that divide a sentence into its component phrases. PoS-tagged tokes are assigned to phrases through this chunking step.
- Syntax parsing: parsing refers to analyzing how a sentence is formed.
- Sentence chaining: in this final step of data preparation, lexical chaining is used to connect individual sentences and create meaningful associations.
Text analytics techniques for business intelligence
Businesses can use specific techniques to analyze textual data, including sentiment analysis, topic modeling, NER, term frequency, and event extraction.
When it comes to text analytics, sentiment analysis is one of the most popular techniques. Sentiment analysis allows you to understand the emotions conveyed in a text, whether it can be a review, a social media post, or even an email.
Sentiment analysis aims to see if textual data depicts positive, negative, or neutral sentiments. With this information, you can better interpret what users or customers say about a particular topic, like a brand, product, or service.
There are different ways to perform sentiment analysis. However, one of the most common is using a lexicon, a list of words with assigned emotional values. Then, the overall sentiment is established based on the number of positive and negative words in a text corpus.
Text analytics is a method for extracting information from text data. It can also be used for various purposes, including market research, sales strategy enhancement, and employee performance in the workplace. Usually, you can uncover the hidden structure in a collection of text documents through topic modeling.
NER is a text analytics technique based on artificial intelligence (AI). This technique helps identify and categorize name entities in textual data, such as people, places, organizations, products, services, or brands. You can monitor, analyze, and utilize this text data in marketing, consumer behavior research, and customer service initiatives.
Term frequency is a text analytics technique that counts the number of times a particular term appears in a text corpus. Also, “frequency” refers to how often a particular word or phrase occurs in textual data.
Term frequency helps identify the most important terms in a text and develop sentiment analysis. For instance, if text data contains more positive words, it is likely to have a positive sentiment. Conversely, if a set of texts has many negative words, it is likely to depict a negative sentiment.
Event extraction involves identifying and obtaining relevant events from text data. Likewise, event extraction can help you understand customer sentiment or monitor market insights by identifying when events occurred and why.
Event extraction is applied to link analysis to determine which actors are significant in specific events. Additionally, it can involve geospatial research since interpreting particular events requires knowing where these occurred.
Moreover, event extraction is relevant in business risk analysis. By exploring sets of events in text data from its databases, you can identify potential risks for its operations. You can also take steps to avoid costly surprises and maintain your business’ productivity based on text analytics.
Business applications of text analytics
Text analytics is often used in customer experience, brand experience, product experience, and employee experience to make better decisions in contemporary companies.
Here are four business applications of text analytics you may not have known existed!
Business owners, marketers, and sales specialists can use text analytics to interpret feelings from reviews or comments on social media. They will be able to understand how people feel about a product or service thanks to this technology.
The knowledge gained from text analytics can be utilized to improve the company’s customer service experience. For instance, you can make significant changes in online interactions with clients through chats, email, or social media platforms from what they identify from text analytics.
Text analytics can be used to track brand mentions across social media and the internet. The insights from text data can help improve the brand experience by creating targeted marketing campaigns or addressing negative sentiment.
User experience researchers or product designers can utilize text analytics to interpret how customers use a product. With this data, businesses improve user experience by adding features or redesigning the product. As a result, companies can enhance the customer’s relationship with their products by implementing text analytics.
Text analytics can be used to explore employee sentiment by interpreting satisfaction surveys for positive and negative emotions associated with the workplace.
After business owners, HR specialists, and recruiters look at what employees need, they can make their company a better workplace. This can be done by changing company policies or providing benefits that the workers have asked for.
Additionally, they can implement specific HR management initiatives to create inclusive, diverse, and equitable workplaces. Consequently, businesses will be able to attract top talent while promoting life-work harmony.
Taking advantage of text analytics for your business today
Businesses are taking advantage of text analytics to gain a competitive edge by improving customer, brand, product, and employee experience. Therefore, companies can use text analytics to enhance customer engagement, monitor market trends, and identify business risks.
In addition, techniques such as sentiment analysis, topic modeling, NER, term frequency, and event extraction can help companies improve their efficiency and productivity levels.
Text analytics is an evolving field with great potential for businesses. As this technology continues to develop, it will become increasingly powerful and valuable for organizations.