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

Good News vs Bad News : the sentiment analysis tool that can change buzz monitoring practices

Across news sites, social media platforms, blogs and forums, customers are responding to what companies are doing every minute, but do we really know what they are saying ? As professionals we have all come across buzz monitoring or sentiment analysis tools to help us uncover the weak signals from the strong ones, but how usable are they ? 

You could be a trader looking for a quick and timely overview on the company you’re about to invest in, or a researcher wanting to understand the current landscape around a brand, the requirement for a tool that is usable and time efficient will be the same in most cases. 

That’s where Good News Vs. Bad News can help. "GoodNews Vs BadNews" is a free browser-based app that enables marketers, publishers and businesspeople alike to discover the positive and negative sentiments around their brand. The technology powering the application is comprised of a staggering 8 million lines of code and is used across the world by companies ranging from Microsoft to Motorola.

Source : LangTechNews