Natural Language Processing and why it is important for media intelligence

In the era of information overload, media intelligence is crucial for organizations to navigate the vast landscape of news, social media, and digital content. With the rapid growth of data and the need for efficient analysis, organizations are increasingly turning to technology solutions to make sense and extract meaningful insights and actionable information from unstructured text. Natural Language Processing (NLP) has emerged as a powerful technology that enables organizations to process, understand, and derive valuable insights from textual data.

NLP is a subfield of Artificial Intelligence that focuses on the interaction between computers and human language. NLP algorithms are designed to understand, analyze, and generate natural language text, enabling machines to comprehend and respond to human language patterns. It encompasses various tasks such as text classification, sentiment analysis, named entity recognition, topic modeling, machine translation, and more.

According to Gartner, unstructured data represents an estimated 80% to 90% of all new enterprise data and it is growing three times faster than structured data. To this, Gartner has also found that businesses increasingly prefer data-driven decision-making to intuition-based decision-making

What organizations need is a unified AI platform that can scale to process millions of data items, integrate results with applications, and enable adaptive analytics. Here are some of the NLP modules that DataScouting offers in its media intelligence software solutions:

Sentiment Analysis: understands the sentiment behind textual data (news articles, online content and social media posts). Our sentiment analyzers specialize in creating customer centric sentiment analysis by creating models that identify tonality. It can be trained with your data according to your definition of positive, neutral or negative, while our research team updates the models on a bimonthly basis.

Summarizer: uses deep neural networks to understand the most important parts of a document and create a short summary. Our summarizer offers maximum performance and works with cloud installations or on-premises.

Hate Speech Recognition: our deep neural network models identify hate speech in social media in multiple languages. Our hate speech recognizer provides toxicity scores, allows users to create reports with harmful comments and accounts and comes with an early warning mechanism alerting users every time hateful content is being detected to act promptly and take countermeasures.

Entity Recognition: it extracts and classifies named entities such as people, organizations, locations, and dates, from text.  It also enriches the text metadata and helps users to identify hidden connections between documents. Our entity recognizer uses neural networks that identify the syntax behind each language.

Machine Translation: facilitates language translation, enabling organizations to analyze and comprehend textual content from different languages from TV, radio, print, online and social media.

Topic Modeling: using Machine Learning technologies to automate the annotation process to extract key topics and identify patterns to generate meaningful insights by performing document-based contextual analysis.

And we keep exploring further integrations of NLP models into media intelligence platforms. We have been working on large language models (LLMs) to improve language-related tasks such as language translation, summarization and answering questions based on text. At the recent FIBEP Tech Day that took place in Rome on 21st of April, we presented a client use case about “Unlocking the secrets of language using LLMs: link analysis and summarization”.

Specifically, in this presentation, we presented the outcomes of a proof-of-concept project for a government entity regarding link analysis and summarization focusing on:

  • the importance of data normalization and retention;
  • newly developed LLMs and challenges solved;
  • integration of LLMs and ChatGPT into DataScouting’s MediaScouting platforms;
  • adapting the business value proposition to media intelligence companies and other organizations.

And as NLP models continue to evolve, media intelligence platforms will become more sophisticated in their ability to analyze and interpret textual data, empowering users/clients with a deeper understanding of media landscapes and making data-driven decisions in an increasingly complex information ecosystem.

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