SoDash has won a Real Business / Wonga "Future 50" award for it's NLP-powered social media analysis.

Guest post from Winterwell Associates, member of the LT-Innovate Network.

Firms need to interact with social media such as Facebook and Twitter. But it’s a hell of a chore. Much of the chatter on these networks is, well, just that.

So how to winnow the wheat from the chaff?

Two Scottish PhDs have developed an algorithm-powered Artificial Intelligence social-media dashboard that offers some truly extraordinary tools for firms seeking to improve their performance.

They explain it thus:

“The SoDash AI can be trained to distinguish between what’s relevant to your business or campaign, and what’s just pointless chatter. While other tools determine sentiment solely based on generic positive or negative words, SoDash is trained to decipher elements such as a message's structure, content, and who’s sending it. For example, ‘Irn Bru is a sick drink!’ means something very different to ‘Irn Bru tastes like sick!’. SoDash puts all this into context to build up a deep understanding of why certain messages mean certain things to each client.”

“SoDash clients define their own categories and enjoy powerful automation and analysis tailored to their specific definitions. The AI automatically tags messages with bespoke labels, delivers market information, ghost-writes responses, provides exceptionally accurate reports and much more. With SoDash, clients can quickly make sense of the countless conversations flowing across social media platforms and turn them into valuable, performance-defining research.”

Virgin, Universal and Phones4U are all using it. SoDash revenues are growing fast. Well worth following.

More information about this news.

Author: Dr. Daniel Winterstein, Winterwell Associates.

Bristol Social Media analytics company track Scottish Independence

As we know, social media monitoring or buzz monitoring is used in many sectors such as marketing, after-sales services or opinion polls. In 2010, Tweetminster has realized an analysis on buzz generated on Twitter about the British national elections. With a margin of error of 1.75%, the tool has shown a more accurate results than wellknown companies like YouGov145. This event had launched a debate on the reliability of measured data through social medias and on the possible correlation between the buzz generated and people's opinions.

Today, Bristol Social Media analytics company track Scottish Independence. What are people saying about Scottish Independence on social media? This has been an issue since the Act of Union was signed in 1707 – or even before that, going back to the Declaration of Arbroath in 1320 or the Battle of Bannockburn in 1314. Now the Scottish National Party Government in Edinburgh is planning to hold a referendum on Independence in 2014.

But is this relevant to people in Bristol? Churnbar, a Bristol-based social media monitoring and text analytics company, have produced a report on seven weeks of conversation in social media and online news, which shows that the issues raised by Scottish Independence could affect people south of the border.

More information about Bristol Social Media analytics company

Challenges of sentiment analysis tools in Buzz monitoring sector

(...) If topics can be automatically classified in a relatively simple way, sentiment analysis (positive, negative or neutral) gets harder as it requires a global analysis of the monitored text. This may be particularly difficult in cases of coordination between different parts of a sentence, of an anaphora or of a coreference (the recovery of an argument later in this document). 

Another difficulty of natural language for automatic analysis of sentiments are intentional contexts, for which the expression of opinion is not a true feeling. Thus, the phrase" I thought the trams were more modern" may be identified as positive when the intention is, however, negative. If the analysis of conversations are established by "word packets", the sentences "I like this model not only because of ..." and "I did not like this model only because of ... "a real problem. They are composed of the same packet of words but represent an opposite feeling. Lucas Dini et al. "Showed the relationship between syntactic and semantic structures of a sentence and the expression of the opinion that vehicle". 

The algorithm must be able to differentiate a neutral comment (eg: this car announces a fuel consumption of 3.3 l/100km) from an opinion (eg: this car announces a fuel consumption of only 3.3 l/100km!). It should also detect spelling errors, simplified syntaxes, slang or smileys. Finally it may be that a comment has positive and negative elements. Are they equal in value or is it that the positives outweigh? The phrase "I think this car is the best in its segment ... but also the most expensive "is an example of such ambiguity. What did the author say ? It is normal that its price is more high because of its beauty or the contrary, the price is not worth the candle? Even a human would have difficulty assigning a tone here. (...)

Source : Master Thesis "Mobility sector in Belgium : social media monitoring to built an efficient marketing strategy" 2011, ULB.

Language = Intelligence