Classifying Tweets, a Path to Wisdom…
Twitter is truly ubiquitous. Twitter presence spans industries, and within companies it spans departments. It’s used both by companies and by customers to express their opinions about those companies. Customers are influencing other potential customers by sharing their experiences and Tweeting to each other. Everyone is talking!
Twitter’s “About Us” page quoted them as having 175 million registered users as of last March. (The number has since been removed from the site.) Even if those users are not all active, the unpublished count of active users is still massive, and is growing.
This all adds up to a staggering amount of raw data in the Twitter ecosystem. Even with TweetRoost’s excellent search monitor feature to automatically find and select search terms about your company, brand, industry or competitors - and if you’re reading all of them, it can be very difficult to extract meaning from so much data.
According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories:
1. Data: symbols
2. Information: data that are processed to be useful; provides answers to “who”, “what”, “where”, and “when” questions
3. Knowledge: application of data and information; answers “how” questions
4. Understanding: appreciation of “why”
5. Wisdom: evaluated understanding.
How can we extract the meaning of a Tweet into short symbols so we can be on the road to wisdom? The use of tags (keywords) can help us in this effort. Tags are nothing new to web-based applications, but they are not something that is provided by Twitter itself. (The ‘tag’ being discussed is not to be confused with the #hashtag imbedded in many Tweets). When you are editing a saved item (which can be a Tweet, Mention, Message, Retweet, etc.) in TweetRoost, you can assign tags to it. These tags are only seen and used internally within TweetRoost, and allow you to classify what you read. For example, a customer rant about your product might get the tag “dislike”. A rave could be tagged “like.” Highlighted in yellow below is the interface for adding and removing TweetRoost tags:
When reading Tweets in TweetRoost, think about the meaning of what you are reading, and how it should be classified. Are you supporting a product? Use tags like ‘feature request’, ‘bug’, ‘happy customer’ or ‘complaint’ to categorize the information. Since everything is saved in the permanent TweetRoost database, you can always pull up all saved Tweets about a given topic by querying that tag. TweetRoost also provides a ‘tag cloud’ (TweetRoost Items -> Tag Cloud in the navigation bar), so you can see which tags are being used the most (and least) over time. What might you learn from this?
Think about how tagging can be used in your Twitter workflow, and how you can start to gain knowledge from seeing your Tweets organized in this way. Your team can agree in advance that certain tags will represent different meanings, and then begin consistently using those tags – there are a lot of possibilities.
Please let us know in a comment, would tagging be more valuable to you if TweetRoost could automatically tag saved items with terms that it thought pertained to the Tweet, instead of you doing it on your own?
