text analytics
  1. Understanding text analytics 
  2. Applications of text analytics 
  3. How AI-powered chatbots use text analytics and NLP
  4. Conclusion 

Contributed by: Chetan Alsisaria

Understanding Text Analytics

Did you know that a large part of data which is available to enterprises is available in the form of text? When analysed using text mining/ text analytics, it can help unpack valuable business insights such as product sentiment and customer perception. A lot of information is hidden in unstructured data such as documents, PDFs, speech data, emails, proposals, reviews, texts, logs, social media posts, internal and external surveys – information which doesn’t enter the realm of decision making and which is of valuable importance.

With the use of advanced analytics technologies such as Machine Learning (ML) and Artificial Intelligence (AI), it is possible to make sense of unstructured data. Deriving value from data helps enterprises save costs, provide accurate insights, help with customer-centricity and predict the future.

Text analytics helps discover insights, key phrases/ words and entities to understand the topic or trend. For example, food delivery platforms in India, which have recently started making significant investments in data analytics and technologies like AI, have started benefiting from them by improving overall customer experience. Artificial Intelligence can help food-delivery platforms make dish/ cuisine classifications, enable customers to chat with a bot to keep a tab of their order status, and additionally, help improve search quality by interpreting colloquial language when customers are searching for a dish/ cuisine.

Here are some of the techniques used in text analytics: 

  • Categorise concepts: Text analytics helps classify categories and entities in texts, such as people, places, time/seasons, objects and things. 
  • Language detection: Provides access to evaluating and interpreting a vast multitude of languages, dialects, etc.  
  • Extract keywords in unstructured text: Understands specific text patterns and evaluates key phrases to derive meaning from it. 
  • Understand customer opinion and sentiment: Evaluates negative and positive sentiment and provides enterprises with honest comments derived from platforms such as social media or customer reviews to better understand the pulse of the brand. For example, a consumer business can use text analytics to get insights into customer perception/ sentiment by analysing customer feedback forms or reviews. 

Applications of text analytics 

In the 2000s, social media and other digital mediums exploded. Enterprises started realising that there was potentially valuable unstructured data which could be used in their analytics workflows, to result in business benefits. This is what thrust natural language processing (NLP) – an application of artificial intelligence to the forefront, allowing businesses to quickly/ seamlessly ingest data that is in an unstructured format. 

One major application of text analytics is chatbots, which can evaluate text patterns, language, etc. and then formulate appropriate responses to customers’ questions. Chatbots are being used to serve as the first line of communication between businesses and consumers – often helping customers with support requests, inquiries and complaints by sorting and routing requests to appropriate teams. While chatbots provide customers with valuable information, they also help enterprises automate mundane and repetitive tasks or skills.  

Other applications of text analytics and NLP are email filters, social media monitoring, automated ads, real-time competitive intelligence among others. For example, spam filtering for emails can be done using text analytics. It can help improve effectiveness by using statistical-based filtering techniques. 

Text analytics is tremendously effective; time and time again, we have seen its applications and use in the business world to reveal insights and trends from large volumes of unstructured data. Its adoption ranges from knowledge management to cybercrime and customer care to fraud detection. For instance, the rise of internet-based crimes can be curbed by law enforcement or intelligence agencies using text mining intelligence.

How AI-powered chatbots use text analytics and NLP

Chatbots are one of the most popular implementations of NLP. However, despite its popularity companies face challenges with implementing AI-powered chatbots; here’s why: 

Last-mile of analytics: AI-based chatbots are handy only if trained on quality data, both internal as well as external. If data silos exist, then the quality of response provided by the chatbots will suffer. Asking the right questions, generating insights and making data-driven decisions can be done using conversational AI. Insights include interpretations, auto-generated charts, period-over-period calculations, key drivers, predictions for measures, and even comparative analysis. But such programs will work well only if trained on high-quality data. 

User value: It is imperative to understand the intent behind the launch of a chatbot for enterprises. On the other hand, it is imperative that users, too, understand the value of the chatbot. Change management is crucial to get people on board with using the technology regularly.

Conversational flow: Despite the hype, there are too many instances of chatbots not being able to replace human interactions and conversations. If companies can’t deliver on their promise of an authentic, free-flowing conversation with a bot, then users end up feeling disappointed. Going forward, how can companies fix issues with AI-powered chatbots? 

Seamless functionality: AI-chatbots should be able to function seamlessly across devices, operating systems and importantly, should be secure with user authentication, authorization, encryption, and access controls. The easy-to-function aspect of chatbots is what makes it a better alternative to navigating sophisticated BI tools. Besides, chatbots if implemented correctly, are hassle-free, provide speed to decision making and are accessible anywhere, on-the-go. 

Conclusion

An organisation needs to identify the problem statements and work towards a particular goal coupled with having quality data and a great team in place to understand and deliver conversational flow. Sometimes an enterprise might end up creating an intelligent product, but it doesn’t end up delivering the right end-goal for customers. The right team can help carve a technology landscape and digital transformation journey to help deploy cutting-edge, customisable solutions for business growth. Analytics technology tends to be a long-term and high-capital investment. The market is so dynamic since the technology keeps evolving that it can be hard to keep up and understand which would be best suited to your unique technology architecture and your subject matter needs. Here, a reliable subject matter and technology partner can play a vital role.

About the author: 
Chetan Alsisaria – CEO & Co-Founder, Polestar Solutions & Services Pvt Ltd

An excellent business leader and technologist, Chetan has, over the past 17 years, led many technology-driven business transformation engagements for clients across the globe for Fortune 500 companies, large/mid-size organizations, new-age companies as well as in the government sector. Chetan’s area of expertise lies in identifying strategic growth areas, forming alliances, building high potential motivated teams and delivering excellence in the areas of data analytics and enterprise performance management. 

If you found this blog helpful and wish to learn more such concepts, join Great Learning Academy’s free online courses today.

2

LEAVE A REPLY

Please enter your comment!
Please enter your name here

nine − 7 =