As the South by Southwest Interactive conference winds down, the tech-loving attendees are still talking about it on Twitter. This year, enterprise crowdsourcing platform CrowdFlower launched a sentiment analysis project that relied on manpower, not computer analytics, to gauge the mood of the crowd.
Launch a social media campaign that will build your brand and deliver results in our online Social Media Marketing Boot Camp starting June 7. Speakers include Abigail Cusick (Bravo Digital), Gregory Galant (Sawhorse Media), Alex Leo (Thomson Reuters Digital), Jim Tobin (Ignite Social Media), and many more. Read the reviews. 
Guest blogger Dr. Taras Zagibalov of Brandwatch addresses criticisms leveled at automatic sentiment analysis used for social media monitoring. Humans, he posits, often struggle to determine the sentiment of a piece of text because, just like the machine, they do not have the relevant knowledge available.
Have current developments in machine analysis improved the process when compared with human analysis? And what of its own, inherent limitations? One perspective — after the jump.
Strategy consulting firm Altman Vilandrie & Company conducted a proof-of-concept study to test if their really may be predictive value to their customers to measure consumer sentiment in social media. The results warrant further exploration of social media as a proxy for actual sentiment. And the study exposed what might be a skew in news media sentiment.
Details after the jump
Brand marketers and agencies engaged in social media are applying social media “intelligence” using offers from a growing number of application and service providers in an increasing differentiated field. Sentiment analysis is just one set of business intelligence vectors we can discover and analyze in social media data.
Now, lets look at some of the sentiment analysis factors brands and agencies should consider when choosing social intelligence platforms.
It probably goes without saying that complete and accurate data make for better analysis. While good data is relatively easy for a brand to obtain from its owned media, assuring the integrity of data from earned, yet-to-be-earned and just plain “in-the-wild” text documents has a higher degree of difficulty.
Twitter data was specifically cited as problematic by several presenters at the Sentiment Analysis Symposium. After the event, I asked some questions of those who are “in the know” about Twitter data to get some clarity on the issue.
Opinions abound in tweets, wall posts, blog comments and documents on other web and social media platforms. We looked at the text elements of these documents that comprise opinions in the first post in this series.
Today, informed by the discussions and presentations at the recent Sentiment Analysis Symposium, let’s examine the business case for sentiment analysis, as well as some issues related to the discovery and analysis processes applied to those documents to mine actionable information for businesses.
I dreamed about a device called the emotitonimeter. One inputs a bit of web or social media text and the emotitonimeter instantly determines whether the text is relevant to your brand and generates a smiling, frowning or neutral face indicating the emotional tone of the text by reaching back through the ether and tapping directly into the brain of the sender.
Until someone comes along with that invention, let’s look at the current state of sentiment analysis.
Listening to your community of customers is the first, essential step in the social media engagement process. There’s a plethora of monitoring solutions on the market and many brands don’t appreciate their power and potential.
Candidates on the losing side of their midterm election races may see some familiar faces in the unemployment line come November 3rd. Pollsters.
