twendz Helps Make Sense of the Conversation With Real Time Sentiment Analysis

Mar

11

posted at: 7:29 AM

Since last December, I've been exploring possibilities of ways to make sense out of massive amounts of data, particularly conversations in social media. What started as a personal side-project in creating an automated sentiment analysis algorithm using natural language processing, and my own secret sauce, grew into a fun service prototype called Happytweets.

Happytweets was interesting, and gained some momentum, but I realized that I needed to think bigger. I needed to create something with this technology I now had that solved a real business problem. So one weekend I did, and my employer took notice. Together, we have vastly improved my intial prototype by enhancing the user experience, improving and testing the heck out of my sentiment algorithm, and today we are releasing it together as a free service offering from Waggener Edstrom Worldwide.

It's called twendz, and it helps you generalize the conversation

twendz header

twendz leverages the power of Twitter search to quickly make a generalization about a conversation using real-time, automated sentiment analysis. With twendz, you can search for any topic, product, brand -- even yourself, and immediately generalize the attitudes and feelings expressed in Twitter conversations for your query. This all happens in real-time, so as the conversations changes and evolves, so does twendz. There's only been one other company that has ever done something like this in real-time effectively, and after their acquisition by Twitter last year, they seem to have lost interest (see: it's broken).

Get a quick pulse on your product

The void left by Summize is filled in a whole new way with twendz, as twendz was designed as a Twitter monitoring tool with sentiment. It changes and evolves with the conversation, showing you the most frequently discussed subtopics for your query, and analyzes the sentiment for those. If what's happening now isn't valuable or relevant for what you searched for, you can view a brief history up to this point for an added perspective.

Monitor your personal brand on Twitter

In just a minute or two, you can discover how people feel about that new pepsi logo right now, and asess the impact based on how much it has been talked about lately. Or, if you want to dive deeper and see just the negative things people are saying about you (assuming you are talked about a lot, like say, Steve Jobs), then you can filter on just negative sentiment to draw attention to only the negative tweets you need to respond to.

Get real-time news that is relevant, or just have fun

bacon topics

twendz pre-populates the latest top Twitter trends in the header so it's easy to jump-in to what's popular now, but it works great for discovering different perspectives on hot current events. I often find myself monitoring fun topics for my own personal amusement. Do people really like bacon as much as it seems like? Now you can find out the answer.

If you have questions or comments about twendz, I encourage you to visit our page on Get Satisfaction where I respond to most questions and comments directly, but I'm also really interested in your direct feedback, as I'm constantly looking for ways to improve the twendz as an offering, as well as the sentiment algorithm.

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Comments

  • 1

    @steveplunkett says... Mar 12 | 07:29 AM

    Very neat application. Good work. I'm guessing you have a dictionary of words with a point value system with varying degrees of sentiment? I noticed one thing, since twitter is a 140 character medium, abbrv. exist.. like FTW (For The Win) but also could be.. (omitted), also nom nom nom was trending neutral. I would assume you have considered txt language, like lol, etc.. Yay!!! vs. meh.. OMG?!!! vs. omg!! =) vs. =( I didn't want to pollute your stream by testing but thought i might drop you a line. thanks for creating something useful. =)

  • 2

    Tim says... Mar 13 | 09:05 AM

    Hi Steve, Thanks for the feedback. You're pretty close, it does use something similar to the traditional bag of words approach. We also use machine-learning for other forms of media, but because it has to be trained, it's very difficult to do in real-time. Glad you like twendz! :)

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