Issue 8. The Evolution of Natural Language Processing (NLP)

In recent years, Natural Language Processing (NLP) has emerged from the shadows of academic research and established itself as a key element in the technological landscape. Terms like "Large Language Models" (LLMs), "ChatGPT," and "Embeddings" are now common concepts used in a variety of applications by technology companies worldwide. These advancements enable machines not only to process text but also to understand its nuances and generate context-aware responses. The practical utility of NLP is wide-ranging, from improving customer interactions through chatbots capable of real-time responses to analyzing and summarizing extensive document sets, a task that was previously unimaginably time-consuming. Furthermore, NLP plays a crucial role in automating and enhancing the efficiency of business processes. Companies employ NLP to gain deeper insights from their data, leading to more informed decisions and better strategy development.

Here are 5 examples of business applications of NLP:

Text Analysis

We all know it. Long, complex texts that are difficult to grasp. For years, this has been a major issue in media analysis. Monitoring services have employed hundreds of people solely to read newspaper articles and evaluate them for keywords. This allowed them to group articles according to their topics. However, this approach had two weaknesses: firstly, the enormous personnel and therefore cost involved. Secondly, the volume of articles to be assigned, as only keywords were examined.

Today's Large Language Models (LLMs) search texts for patterns and classify them accordingly. This means being independent of keywords (queries) and possibly discovering patterns (clusters) that were not predefined but make sense in terms of segmentation. A larger portion of texts can thus be categorized.

The era of keyword-based text analysis is coming to an end. The future lies in language models that independently classify texts according to predefined patterns or even independently of them.

Text Summary

Imagine this: You have a large study and now need to consolidate the most important things from it. For a presentation, for an exam, or even for a professional article. Maybe you just have to read a lot of studies to get an overall picture. Or you have a new tax law and now need to see how this affects your area. What would you wish for? Probably someone who summarizes these texts for you and presents the most important things quickly and clearly. That's exactly a task for NLP.

Sentiment Analysis

Sentiment analysis, which involves analyzing moods and opinions, plays a crucial role in evaluating social interaction. By utilizing NLP, text data from various sources such as social networks, review platforms, and internal feedback systems can be examined. This not only helps organizations monitor the public perception of their brand but also understand user engagement in depth and identify trends. Want to know which new features of an app significantly influence user satisfaction? Want to understand what exactly triggers negative employer reviews? Want to know which topics users in the social web are most likely to react positively to? All of this becomes possible with NLP – and in a novel depth and quality.

Survey Evaluation

Survey evaluation through NLP revolutionizes the way survey data is analyzed. Open-ended questions often provide the most qualitative responses in surveys but are not easily categorizable. Instead of manual analysis, NLP tools enable quick categorization of responses and deeper insights into the data. This is particularly useful for organizations that regularly collect large amounts of survey data, such as market research firms or public institutions. Automated analysis of survey responses leads to faster and more accurate results, helping decision-makers develop more informed strategies.

Customer Service Automation

The first hype surrounding chatbots has passed. They often failed to substantially answer even the simplest of queries. With NLP, the next evolutionary step seems to have been reached. However, the automation of customer service using NLP technology now leads to a significant increase in efficiency and customer satisfaction. Chatbots and virtual assistants equipped with NLP can understand customer inquiries in natural language and provide intelligent, relevant responses. This reduces waiting times and relieves employees by automatically answering frequently asked questions. Furthermore, continuous analysis of customer interactions allows for ongoing improvement of the service offering.

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