Waggener Edstrom (who do plenty of work for Microsoft) has released a Twitter topic/sentiment tool called Twendz (other coverage here and here). The site shows both a tag cloud of related terms and attempts to surface positive, negative and neutral posts on the topic. Thus far, I’m not too impressed with the sentiment analysis on a per tweet level. Here are some postitive false positives tweets about Jim Cramer:
- The most important video you'll watch this year: Jon Stewart ends Jim Cramer's career Video
- oh wow, John Stewart probably ended Jim Cramer's career last night, it was amazing.
- Please take 21 minutes and 12 seconds to watch this video. http://is.gd/nahG Jim Cramer and John Stewart discuss financial markets.
Here’s some of the negative false positives for Jim Cramer:
- Jim Cramer is not at fault! He'll agree to be blamed, but YOU PEOPLE are the ones investing! Be accountable! (still love J. Stewart, though)
Here’s some negative false positives or watchmen:
- Fair point But that is intensely faithful to the original graphic novel. It's dark, dark socio-political commentary. #watchmen
- back to designer mode and crazy to see Watchmen!
- Saw #Watchmen. Must watch for anyone interested in politics/visual effects. The complex philosophical plot kept me at the edge of my seat.
It is interesting to see this in the context of recent posting about the twittersphere making things easier for sentiment mining. While the system should be doing the right thing with valence shifters (negation, etc.) these examples also demonstrate the problem of topic association. In addition, the examples with Watchmen demonstrate the problems between the nature of the plot and the evaluation of the film. And, of course, like nearly every tool in this space, by providing a direct link to the raw data, the errors get surfaced and the user begins to question the value of the results.
Summary: I’m really excited to see that WE is building tools in this space. However, it looks like they are starting at the shallow end when it comes to sentiment mining.