Expansive Applications of Text Analytics using Amazon Comprehend

Sanam Malhotra | 14th January 2020

Global digitization has attached greater value to business data and analytics. AI development services such as Natural Language Processing and sentiment analysis are significantly improving business capabilities for in-depth data analytics. Amazon Comprehend is a text-processing tool that is gaining momentum across industries and channels for accurate and effective insight generation. In this article, we are discussing some business-oriented use cases of text analytics using Amazon Comprehend for global businesses.


Amazon Comprehend: Business Applications for Text Analytics

1) Classifying Complex Databases

The data revolution has transformed the way businesses store, access, and process critical documents and data. However, traditional methodologies such as Optical Character Recognition (OCR) are failing to match the growing complexities of organizational data and operations.

Artificial intelligence and machine learning technologies together are empowering businesses to automate their data processing capabilities with ‘Data Analytics’. Amazon Comprehend combines ML algorithms and Natural Language Processing (NLP) techniques to extract valuable insights from complex data structures. Organizations can support their big data requirements with Amazon Comprehend in the following ways-

a) Automate Data Organization and Categorization

Amazon Comprehend can automate the data organizing and categorizing processes for diverse business documents such as research papers, articles, etc.

b) Streamline Discovery of Documents

NLP and ML algorithms enable Amazon’s Comprehend to ease the discovery of documents from large and complex datasets.

c) Personalize Content Recommendations

In addition to data classification, Amazon Comprehend also works to improve the personalized content recommendations for the end-user.

Also read- Applications of Natural Language Processing (NLP) in Recruitment


2) Analyzing Social Media Interactions

In a social media dominated world, it is indispensable for businesses to approach their social media audiences proactively. With over 2.65 billion active users and continuous interaction streams, social media data can contribute invaluable insights to an organization’s marketing strategies.

Amazon Comprehend augments the social media marketing efforts of businesses with abilities to analyze customer interactions across channels, emails, forums, etc. The NLP and ML algorithms underlying Amazon Comprehend can evaluate posts and comments to extract key phrases, entities, and sentiment analytics for-

a) A better understanding of consumer behavior and feedback towards certain services

b) In-depth analysis of consumer pain points to enhance the customer services and user experience

c) Developing and improving customer relations across channels

Amazon Comprehend along with Redshift enables an efficient, automated, and accelerated identification of business activities that lead to positive customer responses.

Also read- Potential Business Applications of Sentiment Analysis Across Industries


3) Understanding Customer Grievances

Customer grievances are a vital source of business improvement and development. However, a lack of standard procedures and delayed escalation of customer complaints to appropriate departments prevent businesses to satiate disappointed customers.

AI’s conversational abilities are fast evading business infrastructures to handle cust2) Analyzing Social Media Interactionsomer grievances with lower response times and better results.

An estimate by Business Insider suggests that 67% of 5000 consumers surveyed preferred chatbots to avail of customer support services.

Amazon Comprehend is an effective platform that integrates with business chatbot development services to automate the categorization of customer support tickets across channels. In addition, the ML algorithms underlying Comprehend can be used to-

a) Direct queries, requests, and issues, etc. to the respective teams equipped to best resolve them for quick and accurate redressal.

b) Classify inbound customer support documents like online feedback forms, customer support tickets, and product reviews based on their content.


Expansive Text Analytics using Amazon Comprehend with Oodles AI

The AI team at Oodles has experiential knowledge in deploying machine learning algorithms for in-depth text analysis to extract actionable insights. We use natural language processing techniques and tools to analyze unstructured big data including organizational data, social media interactions, customer responses, and more.

Konfer is our most recent project built on Amazon Textract and Comprehend to automate the classification and discovery of online research papers. We used Comprehend’s  NLP and NLU APIs to analyze complex research document sources to identify key concepts and themes for greater insights. 

amazon comprehend for text analytics

With Comprehend, we classified over a million publications and research documents curated from renowned universities. In addition, the system accurately captures the sentiment of each copy for an automated summarization of specific papers.

To know more about our AI and machine learning services, please contact our AI development team.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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