In today’s fast-paced digital era, the volume and complexity of media content have reached unprecedented heights. As a result, the need for effective media intelligence and conversational agents has become more crucial than ever.
Modern media intelligence platforms utilize sophisticated techniques such as natural language processing (NLP), machine learning, and sentiment analysis to comprehend and categorize media content. By monitoring news articles, tv and radio, online, social media posts, and other OSINT sources, these systems enable businesses, organizations, government bodies, in-house communication teams, and others to gain real-time insights, track trends, and make data-driven decisions.
The emergence of conversational agents
AI-powered conversational agents simulate human-like conversations, offering personalized assistance and delivering information in a conversational manner. Initially used for simple tasks like answering FAQs, conversational agents have evolved into sophisticated tools capable of handling complex queries and engaging in meaningful discussions.
Conversational agents leverage natural language understanding (NLU) and natural language generation (NLG) to interpret user inputs and generate appropriate responses. They can be found across various platforms, including websites, messaging apps, and voice-enabled devices.
The synergy of media intelligence and conversational agents
The integration of media intelligence with conversational agents presents a transformative synergy that revolutionizes the way we interact with information. By combining the capabilities of both technologies, organizations can create powerful tools for media monitoring, analysis, and engagement.
At DataScouting we are constantly looking at ways to improve our clients’ experience of our media intelligence platforms and communication evaluation tools and to keep them accurate and relevant, based on real-time information.
We have integrated conversational agents and specifically ChatGPT into our MediaScouting Core to help users drill down to the information, automate repetitive tasks (and help save time and lower costs), improve quality, enhance decision making and personalize customer experience.
- Real time news and trend monitoring: users can use AI to source content from wherever it is being covered, check it for relevance, summarize and code it, create the data, analyze and evaluate it and create reports.
- Sentiment analysis: includes a) a sentiment classification model (tasked to classify sentiment) during a dialogue, b) a reply sentiment prediction model (leverages the context and comes with a prediction of an appropriate sentiment), and c) a text generation model (produces a reply that is both context and sentiment appropriate).
- Improved entity recognition and filtering: the system can interactively detect entities on media content, filter on demand and provide comparisons based on multilingual sentiment analytics.
- Summarization: our NLG model helps users create high quality summaries of lengthy news articles based on specific criteria. This can be managed across different media sources (including print, online, TV or social media posts) and different countries offering cross-channel comparison on a global scale. The model also provides suggestions with insights generation.
At the recent FIBEP Tech Day that took place in Rome on 21st of April, we presented the outcomes of a proof-of-concept project for a government entity, in a talk titled: “Unlocking the secrets of language using LLMs: link analysis and summarization”.
To gain a deeper understanding of how MediaScouting Core actually works, just click here and sign up for a free demo. At the recent AMEC Global Summit on Measurement and Evaluation in Miami (15-17 May), we outlined the different technologies, including analytics and AI tools, that are required to create a state-of-the-art PR and monitoring software stack to manage and create actionable information from different data sources.
Check out all our media monitoring software solutions:
