Suppose you are a social media analyst, and you have to analyze the media coverage related to something really hot like FIFA’s latest corruption scandal, using nothing but a spreadsheet.
That’s because you don’t expect a lot of data, and you don’t have enough budget to use a Social Analytics platform.
What if we could have a computer do all the heavy lifting for us – like, say, extracting the main topics expressed inside the article?
That way, you can stay focused on the content, to better understand the context. Moreover, you have these automatically extracted topics that you can use to produce useful hashtag suggestions to follow other related news. Last, but not least, you can select the most relevant topics to enrich your analysis with contextual information taken from Wikipedia.
Learn how to perfom this Text Analysis reading the tutorial on blog.dandelion.eu!
Last week we flew all the way to New York City to attend the 8th Sentiment Analysis Symposium, which we also supported with a bronze sponsorship.
Thanks to the symposium many people had the opportunity to meet the Dandelion API, and we could share many thoughts and ideas about our brand new sentiment analysis API and more in general about our knowledge-graph approach, with many experts in the text-analytics area.
Read our thoughts on the event on our Dandelion API blog!
Food-borne illness often shows itself as flu-like symptoms, so many people may not recognize the illness is caused by bacteria or other pathogens on food.
So the question is: which are the most prominent types of bacteria involved in foodborne illness?
We can start from the European alert system, the RASFF (Rapid Alert System for Food and Feed), which collects all the notifications of any serious health risks deriving from food:
European legislation ensures a consistent, high level of food and feed safety. The European Commission collects and publishes notifications of any serious health risks deriving from food or feed and any measures taken, e.g. withdrawing or recalling food or feed from the market in order to protect consumers. Rapid alerts are also exchanged and published when any food or feed presenting a serious health risk is on the market and when rapid action is required. The alert is triggered by the identifier of the problem and published to enable all markets to withdraw the product or take any necessary measures in order to protect consumers.
Every notification contains different kind of data: the “Subject” column is extremely interesting, because it contains citations of bacteria and other useful contextual informations.