The power of Machine Learning in Communication and PR16th September 2021/in AMEC Member Article, AMEC Tech Hub SIG, News, Special Interest Groups Michel/by Julie WilkinsonManual tagging versus new technologies Automatic text analysis and entity recognition, part of the Machine Learning processes, are important for their instant and quick presence while being aggregated. It can be the key element in discovering the potential of any media topic we would like to cover as PR professionals. Entities are derived from public sources available on the internet so the knowledge behind them is constantly growing and being edited. In the amount of data we are digesting every day such tagging and following aggregation is inevitable. Following the possibility to uncover entities worldwide is opening some opportunities yet to be taken. Why are we letting machines do our job? The moment of surprise or so called “aha” moment is something that drives us to perform the task of media analysis. Automatic text analysis creates these moments in a glimpse extracting key stakeholders, identifying the locations or summarizing actions expressed in the text. Those tags serve as the first instance in discovery process of analysis. You don’t need to dig deep into the vast amount of data, you should open the top tags on top of your data and find out, what relation it has to your specific topic. So we have it, interesting parts of content which will help us generate first pieces into the media puzzle. But there is more to this. Because the automatic text analysis does not only identify for example “Volkswagen” being a German automobile manufacturer, it will tell us the attributes “Volkswagen” is surrounded by. When analyzing the brand itself, VW is looked at as a brand using creative technology, aiming for clean emissions and being a humble company in the positively perceived posts based on the query “Volkswagen”. But when turning the page to the negative ones there is still a large amount of data talking about their emission scandal. Interesting is that VW Coverage about this topic is fading while their new clean future becomes rather trending in longer time period of data. All that ready to be opened by curious eyes of a PR manager. The main benefits and impact on our work: Key improvements hidden behind those technologies lie in their instant and accurate presence in media monitoring or analysis software. By aggregating large amounts of data we can calculate long time periods of data, emphasize stakeholders in seconds and collect the first hints about unknown topics. This has an impact on our approach to current client’s cooperation by leading the way through communication ahead of them. The quick response to any problem is being solved much more immediately. Our reliability opens the door in entering tenders where we face the sample analysis which cost a lot of time to be made. Last but not least our readiness for any crisis communication or campaign evaluation does not take days but hours instead. Wordcloud of the query Volkswagen for last 30 days Overall sentiment of the query Volkwagen for last 30 days Trending vs. fading topics and hashtags Author: Michel Hrones, Customer Care and Innovation Manager, Newton Media https://amec.blazedev.co.uk/wp-content/uploads/2021/09/TECH-HUB-SIG_New-blog-post.png 500 500 Julie Wilkinson https://amec.blazedev.co.uk/wp-content/uploads/2021/01/AMEC-25.png Julie Wilkinson2021-09-16 11:00:222021-09-16 12:21:36The power of Machine Learning in Communication and PR