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Posts Tagged ‘Crowdflower’

Tracking the Mood About Gas Prices on Twitter: A Case Study

by January 25, 2012

As another test of our strategy for teasing out public opinion from social media, we explored measuring mood about gas prices on Twitter. This post summarizes the findings from this case study. Incidentally, we are set up to measure mood from Twitter on an ongoing basis, although we would need to find a partner to help defray the ongoing costs of crowdsourcing the sentiment judgments. (See this post to read more about our decision to examine the discussion about gas prices on Twitter.)

The sentiment we mapped was culled from tweets gathered from four weeks’ worth of data starting on May 22nd, 2011. This time period was chosen to coincide with Memorial Day, a holiday during which many Americans travel by car. Our team was curious to see whether there would be an uptick in either the volume of tweets about gas prices during this period or a noticeable change in sentiment about these prices. (more…)

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Capturing Mood About Daily Weather From Twitter Posts

by September 29, 2011

After considerable preparation, we’ve just launched a version of our interactive tool, Pulse. Using Pulse, users can explore feelings about the weather as expressed on Twitter.

We began the process by choosing a topic that would yield a substantial volume of discussion on Twitter as well as be of general interest. Once we settled on weather, we wrote a survey designed to gauge Twitter users’ sentiments about the topic. With the help of workers from the “crowd” accessed through CrowdFlower, we had tens of thousands of relevant tweets coded as to the expressed emotion about the weather. These results were then used to create an “instance” of the Pulse tool, which manifests as a map of the United States that at a glance reveals Twitter users’ sentiments about the weather in their region on a given day. (You can read more about the coding process here and our choice of weather as a topic here.)

For our launch of Pulse for weather, we chose to feature tweets published over a month beginning in late April, 2011, a period in which many extreme weather events occurred—the devastating tornado in Joplin, MO; widespread drought throughout the South; and flooding of the Mississippi River, among others. The image below is from May 25, three days following the Joplin tornado (jump to the interactive map here).

may-25-pulse

We gathered tweets from all 50 states as well as for about 50 metro areas. Here you can see a zoom up on several states centered on Missouri.

zoom-may-25-pulse

The interactive map tells part of the story, namely a state’s or city’s overall sentiment about the weather, while the content under the “Analysis” and “Events” tabs reveal some of the “why” behind this sentiment: what were some of the most notable weather events occurring on a given day? [Note: our "events" feature has a bug in it and is currently turned off. In the future, icons will show up on the map to highlight out-of-the-ordinary weather events, like outbreaks of tornadoes, persistent flooding or drought, etc.] To what extent did the weather deviate from normal conditions? Why were tweets from, say, the South, uniformly negative during a certain time? What was happening when we saw a single positive state amidst a region that was otherwise negative?

We hope that weather is just the beginning. We envision using the Pulse tool to visualize nationwide sentiments about more complex, nuanced topics in the future—a sample of emotions about gas prices is just around the corner, and see our preliminary work on opinions about global warming. For now, you can explore the Pulse tool here, and let us know what you think!

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Sentiment Analysis Milestone: More Than One Million Human Judgments

by June 27, 2011

judgment-shot We have developed a process, dubbed Pulse, to extract nuanced sentiment from social media, like Twitter. We recognized early on that tools weren’t available to adequately answer specific questions, such as: “What’s the mood about today’s weather?” or “What portion of Twitter authors who discuss global warming believe that it is occurring?” or “Did Apple or Google have a more favorable buzz during this year’s South-by-Southwest Interactive?” Specifically, we concluded that it was necessary to get humans involved in the process—especially for Twitter posts, or tweets, which are often cryptic and have meaning that might be missed by a computer algorithm.

So, we turned to crowdsourcing.

However, successfully leveraging the power of the crowd for our sentiment analyses required cultivating the crowd, which we have achieved by working with partner CrowdFlower. In short, CrowdFlower offers an approach where we can access various work channels (we have relied mostly on Amazon’s Mechanical Turk), yet do so by layering on a quality control filter. Specifically, we intersperse within jobs what CrowdFlower terms “gold” units—in our case, tweets for which we already know the sentiment.  Workers build trustworthiness scores by getting the gold units correct. If they miss a gold unit, they get some feedback from us that has been tailored to that unit, such as “This person is happy that their garden is getting rain, so this should be marked as a positive emotion about the weather.”

We have been running a lot of jobs through CrowdFlower, but only recently did I step back and add up the tweets processed. For more than 200,000 individual tweets, we have received more than 1,000,000 trusted, human judgments from the CrowdFlower workforce! I know our research team, who had to do a bunch of judgments early on as we worked out a viable strategy, are grateful that we could get help from the crowd.

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Hope for Human Sentiment Analysis Coding

by May 13, 2011

I just read an interesting blog post on Social Times discussing the advantages of machine-based sentiment analysis. In the piece, author Dr. Taras Zagibalov challenges the critics of “automatic” sentiment analysis, who claim that humans can better determine than computers the sentiment of social media text. He asserts that, with the proper tuning of a system’s classifier—creating specific classifiers for each domain (subject matter) and keeping them current—a machine-based sentiment analysis system can outperform human accuracy.

The discussion of human vs. machine sentiment is core to our work at Dialogue Earth, where we are developing Pulse—a social media analytics tool to help tease out nuances in the social media dialogue about key societal topics. Pulse social media analytics tool (more…)

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Just Around the Corner: A Longer-Running Pilot On Weather Emotions

by April 27, 2011

This week the weather in the U.S. has been pretty unusual. We set a record for rainfall here in the Twin Cities, which is really a footnote to the week compared to the violent extreme weather in the Southeast and beyond. While understanding how people are feeling about the weather day-to-day won’t change the weather, we see it as a great starting point for developing our Pulse system for tracking public opinion on issues discussed in the social media.

As a follow-on to our first weather pilot, we are gearing up to monitor mood about the daily weather across the U.S. for weeks at a time. In fact, we are just completing a run of about 8000 Twitter tweets through our “crowd-based sentiment engine” using the CrowdFlower platform. Once we have double-checked the results, we are set up now to collect tweets continuously, automatically send them over to CrowdFlower for sentiment judgments, have the judgments returned to our database automatically, and then publish the data on our interactive Pulse display. We expect to be analyzing several thousand tweets through CrowdFlower on a daily basis in order to create a detailed map of weather mood for the U.S. (see more here about our data sampling strategy). Look for more on this in the coming days. The image below is a sneak peek at our interactive platform, which our team has overhauled in recent weeks. It should prove to be a much-improved user experience!

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Cultivating the Crowd for Social Media Analysis

by April 22, 2011

In a recent post on Crowdsourcing.org, Panos Ipeirotis writes that Amazon Mechanical Turk today is a “market for lemons,” referencing economist George Akerlof concept of quality uncertainty. For those who aren’t familiar with Mechanical Turk, it’s a distributed workforce platform that allows one to crowdsource small tasks. For a relatively low cost, those requesting work can get their tasks quickly accomplished by a large pool of anonymous workers.

This post resonates with us at Dialogue Earth, where we are leveraging a crowdsourced workforce to help us analyze social media dialogue. Our Pulse tool relies on crowdsourced workers to determine the sentiment of Twitter tweets on topics like the U.S. mood about weather.

Pulse, by Dialogue Earth

(more…)

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In Search of Quality Control with Crowd-Based Sentiment Judgments

by March 4, 2011

In a previous post, I described our evolving approach for developing a question that can be addressed on our Pulse platform. We’ve also described previously why we think crowdsourcing is a smart way to get lots of judgments made about sentiment expressed in the social media. But, what about quality control? How can we maintain an acceptable level of quality control while relying on the crowd to make thousands and thousands of judgments?

Quality through known answers and feedback to workers. We were drawn to CrowdFlower because of their approach for ensuring quality control using what they call “gold”. In a typical “assignment” set up on the CrowdFlower platform, a worker needs to make judgments for a group, or assignment, of “units” (a unit in our case would be an individual Twitter tweet). Within every assignment, CrowdFlower includes a gold unit for which we have indicated the correct answer. By setting an assignment to include 15 tweets, it means that a worker will be presented with a gold unit within each new batch of 15 tweets. (more…)

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Contemplating Content Crowd-Sourcing: an Interview

by February 18, 2011

Dialogue Earth’s associate director, Tom Masterman, was recently interviewed by Crowdsourcing.org about recent projects to crowd-source the creation of Dialogue Earth videos. In this post, the first of a two-part series, Tom talks with Crowdsourcing.org’s Carl Esposti about the issues of control, quality, cost and timing.

Carl Esposti: Why did Dialogue Earth consider crowdsourcing a video project?

Tom Masterman: Just the other day, I realized that crowdsourcing now pervades most aspects of the business strategy for our start-up nonprofit, Dialogue Earth. Given some personal experiences producing corporate videos, I truly wanted to avoid the unfortunate situation where you develop something in-house (or with a single contractor or agency) that you and the executive team love, but that falls flat with your target viewers. Since our goal is to communicate science in ways easily understood by a variety of audiences, it seemed worthwhile to test how the crowd would explain our key points.

(more…)

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A Journey to Understand Social Media Sentiment

by February 14, 2011

Brand Bowl 2011

Chrysler stood atop the final standing for Brand Bowl 2011.

On Super Bowl Sunday, 106.5 million viewers were watching the big game—the largest TV audience ever, according to Nielsen. Many tuned in to witness the Packers battle the Steelers; even more, I imagine, were watching to see emerging brand Groupon face off against fan-favorite Go Daddy and advertising stalwarts Pepsi, Doritos and Volkswagen.

Millions were simultaneously browsing the Web, monitoring game stats and their Super Bowl pools, and checking out the brands advertised on the TV spots. A much smaller group of advertising and social media junkies were simultaneously glued to “Brand Bowl 2011,” a venture between ad agency Mullen and social media monitor Radian6 to monitor and rank the sentiment of Twitter references of Super Bowl advertisers. (more…)

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