A Deep Dive into NER, Topic Modeling, and Dependency
The digital age has given us a plethora of information. With businesses communicating non-stop through emails, press releases, and social media, there's an abundance of data waiting to be deciphered. Enter Natural Language Processing (NLP) - a game changer in extracting actionable insights from vast swathes of unstructured text data.
In this post, we'll explore three powerful NLP techniques: Named Entity Recognition (NER), Topic Modeling, and Dependency Parsing.
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We'll delve into what they are, how they function, and why they matter in the business context. Let’s begin
Named Entity Recognition (NER)
What is it?
NER is a sub-task of information retrieval that classifies named entities into predefined categories like persons, organizations, locations, and more.
Practical Example
Imagine a company wants to understand its media presence. By using NER on news articles, the company can identify every time its name is mentioned, see who or what is associated with it, and extract sentiments around those mentions.
Relevance for Business
NER can help businesses monitor brand reputation, track competitors, and even spot potential business opportunities. If, for instance, a competitor is frequently associated with negative news, it might be an opportune moment to push a positive marketing campaign.
To have some more information about NER, we recommend the following article: Uma Visão Geral sobre Named Entity Recognition (NER)
Topic Modeling
What is it?
Topic Modeling involves identifying topics in a large volume of text. Algorithms like Latent Dirichlet Allocation (LDA) can break down texts into sets of keywords, revealing the underlying topics.
Practical Example
Let's say a multinational corporation wants to understand concerns from employees worldwide. By applying Topic Modeling on internal communication channels, they could identify recurring themes, like "work-life balance" or "career progression."
Relevance for Business
By understanding the key themes circulating in internal or external communications, businesses can prioritize areas needing attention, be it in employee satisfaction, customer feedback, or market trends. It's a proactive approach to problem-solving and strategy formulation.
To delve deeper into Topic Modelling, we suggest the following article:
Dependency Parsing
What is it?
Dependency Parsing maps out the grammatical structure of a sentence, identifying relationships between words. It unveils how different parts of a sentence relate to one another.
Practical Example
Suppose an e-commerce platform wants to improve its product search functionality. By using Dependency Parsing on search queries, the platform can better understand the intent behind each search, leading to more accurate results. For example, parsing the query "shoes for running in rain" would highlight the importance of "running" and "rain," refining the search results.
Relevance for Business
Understanding the intricate details of how words relate can greatly improve user experience in digital platforms. It also aids in sentiment analysis, making sense of feedback or reviews by revealing the underlying relationships in a sentence.
To have a further glimpse into Dependency Parsing, we suggest the following article: Dependency Parsing in Natural Language Processing with Examples
In Conclusion
The business landscape is evolving, and so are the tools we use to understand it. NLP, with techniques like NER, Topic Modeling, and Dependency Parsing, offers a lens to decode the vast amounts of textual data businesses encounter daily. Whether it's refining user experiences, understanding employee concerns, or tracking brand reputation, the potential applications are vast. In the ever-competitive business world, staying ahead means understanding not just numbers, but words too. And with NLP, we've got a potent tool to do just that.
Ethical Disclosure
While the power of NLP techniques offers immense potential for businesses, it's imperative to approach their application with a keen ethical lens. Privacy and consent should be at the forefront of any data analysis. Before processing any form of communication, ensure that the data has been ethically sourced, and the parties involved are aware and have given consent. It's not just about what the law permits, but about respecting individual rights and the trust people place in businesses. Furthermore, while NLP can provide insights, it's essential to avoid making blanket decisions solely based on algorithmic outputs. Human judgment, empathy, and a broader understanding of context are irreplaceable. In essence, while we embrace the advantages of NLP, we must remain committed to the principles of fairness, transparency, and respect.
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Suggested reading on this topic:
Dependency Parsing in Natural Language Processing with Examples | Prashant Sharma | Analytics Vidhya
The Lexical Approach to Human Resources and Team Management Insights | Adelino Gala | The AI Academy
Content Curation: Adelino Gala at The AI Academy
Adelino Gala specializes in digital journalism, cognitive science and natural language processing, with a PhD and Master's in Technologies of Intelligence and Digital Design from the Pontifical Catholic University of São Paulo. Experienced in new technologies of communication through post-doctoral work at the University of Aveiro and various projects such as European PAgES. Bachelor's degree in Business Administration. Has also imparted knowledge as a guest professor at esteemed institutes in São Paulo and University of Aveiro. With a publication portfolio spanning journals and conferences, the author is a confluence of academia, research, and practical industry insights.
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