Finding the sentiment of online...

One of the most important aspects of online conversations is the sentiment of what the author is saying. Are they positive about you, negative or apathetic? The difference is vitally important, but very hard to determine due to the complexity of language.

Let's look at what I mean by complexity of language. Most services that are out there take a look at a post and try to identify what is being said by looking the total range words. They have lists of positive words like "great", "awesome", "l33t" (for the hacker crowd) as well as negative words like "sucks", "terrible", etc. If neither group of words is found the post is considered neutral.

I'm sure you can see the error in this. A post could be negative overall, but avoid these words. It could also use one negative word, but be positive overall. What is needed is true contextual language processing (which is expensive and requires a lot of development).

Here are a few examples of sentiment analysis.

Collective Intellect is a social media monitoring solution that we work with. Part of their analysis is of language within conversations and the sentiment that is displayed there. The sentiment is then tracked over time and can be a key metric in the success of a campaign. Their formula for extracting the sentiment is not publicly accessible so I am not sure how they calculate it.

Summize is a Twitter search engine. In their labs section is a sentiment analyzer that lets you enter a keyword and get the real time sentiment. If you play with this for a while you will see some issues as I found out when I sent this link out on Twitter.

Here is a sample of the output for the term "marketing".

Another service that uses Twitter as the basis to create an engaging experience around sentiment is Twistori. Twistori takes a few key terms like "love", "hate", "feel" and "wish" and creates a dynamic timeline based on the use of the terms. It's very cool to watch the service extract the terms and after a few minutes you see how difficult it is to get sentiment right.

So, do you look at the sentiment of online conversations? There is still no better filter than to read back through a blogger's posts to get their real feeling at this point. Technology is evolving quickly, but so is language.

How are you tracking sentiment online? Is there a tool that I missed? Let me know!

Technorati Tags: conversations, marketing, Matt Dickman, search, social media, Techno//Marketer, Twitter, sentiment