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Posts Tagged ‘social media analysis’

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).

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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.

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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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Teasing Out Opinions About Global Warming From Twitter

by June 24, 2011

snapshot-ca A couple of months ago, we posted results from a quick sampling of mood about global warming in the Twittersphere that was featured in Momentum, the publication of the University of Minnesota’s Institute on the Environment. Along with our work on weather mood and mood about gas prices, we are on the verge of releasing a more in-depth analysis of sentiment about global warming. Here, we explain the method behind our sentiment analysis related to global warming, building off an earlier post that presented some of the details of our methodology on studying global warming chatter. (more…)

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Relevancy and Context are “Critical” with Sentiment Analysis

by May 24, 2011

September 11 Whenever I come across a piece that highlights how tricky sentiment analysis truly is, I tend to be encouraged more often than dissuaded to keep trying to figure it out.

Sentiment analysis is tough—not as in strict, like a teacher is tough, or in resilient, like a marathoner is tough. More like hard, like an AP calculus test is tough.  Not hard, like a block of concrete is hard.  Hard, as in difficult.  Eh, nevermind.

A colleague of mine just sent me a piece from the Miller-McCune site discussing a flawed mood study about September 11 pager text messages.

Researchers from Johannes Gutenberg University in Germany had concluded that there was an escalating level of “anger” words communicated to pagers as time passed on September 11 (here’s the study).  I’ve included the original data graph in this post. (more…)

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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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Have We at Dialogue Earth Broken Free of Randy Olson’s “Nerd Loop”?

by May 9, 2011

nerd-loop-piecePrior to reading Andy Revkin’s post Climate, Communication and the ‘Nerd Loop’ just now, I was unaware of Randy Olson’s newly coined term the “Nerd Loop.” It is a term that he recently gave to in-the-box strategies for communicating science to general audiences (read about it on his blog, The Benshi).

Olson argues passionately that there needs to be more risk taking in the science communication realm. I equate this to needing more out-of-the-box approaches, some of which will fail and some of which will help members of the public to understand a bit more about important issues like global warming, energy, food, water, land use, and so on. There won’t be a single approach that will work in all cases. Nor do I expect that there will be massive uptake of new information. It’ll be a slow, gradual process.

For me, I think the key for out-of-the-box approaches to work is that there needs to be an underlying genuine quality. Is there an effort to change people’s minds, or just to inform? If the goal is ultimately to change people’s minds, I deeply believe that even the most out-of-the-box efforts to raise literacy on a number of key issues connected to the environment will face barriers.

That’s why I’m committed to a non-advocacy approach with Dialogue Earth. We’re advocates for good information being present in societal dialogue and decision making. Period.

I believe that our strategy based in understanding the public dialogue, building credibility by drawing in a wide spectrum of experts, and ultimately delivering highly-engaging, crowd-based multimedia products holds lots of promise.

Ultimately, we can convince ourselves that we’ve stepped outside of the box, but our opinion amounts to very little. What do you think?

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Oil Companies’ Profits to Increase Greatly This Year; People’s Energy-Related Questions to Follow Suit.

by May 5, 2011

The rapid increase in oil prices should equate to the oil industry having its best year since 2008, as reported by Chris Kahn for AP (via ABC). Exxon Mobil Corp., Chevron Corp. and ConocoPhilips are expected to report a combined $18.2 billion in first quarter earnings — a 40% increase from last year and just shy of the $20.2 billion that they earned in the first three months of 2008.

An increase in consumption, the constriction of supply (e.g., Libya’s reserve access is currently limited), and also a weaker US dollar are all speculated to contribute to an increase in oil prices.

While some stand to benefit from the rise in oil prices (shareholders), businesses and consumers will feel the hurt as gasoline prices inflate. Increases in gas prices tend to have ripple effects, increasing the prices of transportation and any good or service that is reliant on transportation — bread, toiletries, DVD players, air plane tickets, etc.

The broad societal effect of an increase in oil prices is precisely what makes this issue of interest to Dialogue Earth.  This will undoubtedly augment expressed sentiment related to energy across social media platforms, such as Twitter. (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

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A Quick Video Tour of Pulse, How We Extract Sentiment from Social Media

by March 31, 2011

Earlier today I gave a talk to the Social Computing Group at the University of Minnesota. The talk featured our approach for teasing out sentiment and pubic opinion from social media, with a focus on data from our recent weather mood and global warming pilots. As a bit of an experiment, I ran through the talk again this evening and recorded a video. We would love to get any and all feedback!

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Global Warming Chatter: A Hot Topic on Twitter?

by March 28, 2011

Some months ago, our research team developed a strategy for inferring opinions about global warming from Twitter for our Pulse platform. We were lucky to be asked last week if we could present such data for the next issue of Momentum, the award-winning publication of the University of Minnesota’s Institute on the Environment. Of course, like all of us on a deadline, they needed it “yesterday.”

Not to be deterred, we rapidly spun up our collection system to grab those Twitter tweets that included the keywords global warming, climate change, and #climate. For a six day period ending on 23 March, we collected about 7600 tweets that had some geo-location information associated with them. Based on our recent experience focused on weather mood (described in this post), and because we had already generated a good number of quality control units (as described here), we posted a major job on the CrowdFlower platform within a day of the request from the Momentum team. Here’s a snapshot of the results:

momentum_dropshadow_300dpi (more…)

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A Sentimental Look at SXSW

by March 21, 2011

 

sxsw-banner South by Southwest (SXSW) is the annual opportunity for startups across art and technology to prove themselves or, more often than not, generate buzz in the attempt. Whether you’re checking out the latest surf rock 3-piece or organizing drinks via group text, SXSW generates chatter – and a lot of it.

We couldn’t resist the lure of participation, albeit from across the country, so we decided to turn our developing Pulse technology, for analyzing social sentiment, on the interactive portion of the event. We focused on something a little different, though…

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Digging into the South-by-Southwest (SXSW) Twitter Traffic on Apple & Google

by March 16, 2011

ssGiven we couldn’t be at South by Southwest (SXSW) this year, we thought it would be interesting to apply our developing Pulse technology to the Twitter chatter connected with the event. Pulse represents our approach to sifting out interesting information from social media dialogue. Our first major application has been in the area of weather mood, a pilot study of which is chronicled here.

The quick overview is that we leverage the power of the crowd using CrowdFlower’s platform to extract a high-quality, nuanced understanding of sentiment from Twitter tweets. Prior to going to the crowd, we develop a strategy to create a survey that we can give to crowd-based workers so that they can make reliable judgments about author sentiment. We then collect a bunch of relevant tweets, do some pre-processing to limit the size of the sentiment coding job sent to the crowd, do some preliminary rounds of coding to ensure quality control, and then run a coding job on a large number of tweets. (more…)

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Teasing Out Weather Mood From Twitter Posts: A Pulse Pilot

by March 8, 2011

In choosing a topic to use as a test case for our Pulse social media analytics tool, we wanted to pick something that is broadly discussed. What better topic to start with than people’s mood about the weather? It is hard to escape having a few thoughts about the weather on a regular basis. Snow storms, sunny warm days, and heatwaves, to mention a few, cross party lines and ideological divides. Plus, people love to discuss the weather, so we figured there would be lots of chatter in the social media—and we haven’t been disappointed. Read more on our weather strategy here.

In this post, I describe our first demonstration of the Pulse platform to describe weather mood across the U.S. using 12,500 tweets collected on February 4th. While our process is a work in progress, there are several key steps: identifying and collecting useful social media posts, getting reliable judgments about the sentiment in these posts made by crowd-sourced workers, publishing the data on our Pulse platform, and finally, combining our sentiment data with external data sources to tease out a story about the drivers of the observed sentiment.

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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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Preparing to Extract Weather Mood from Tweets

by March 3, 2011

weather-tweet1

Yep, it was cold this morning in the Twin Cities. I didn’t need Twitter to tell that. Yet, we can’t always assume that, just because it is cold, people are upset, or that because it is warm, people are happy about the weather. But, we believe tweets will reveal something quite interesting: how people’s emotions are indirectly affected by the weather. For example, are people happy to be inside watching a movie even though it is “super chilly” outside? Or happy that the it is raining because it will help the garden, even though they may not be eager to be out in the rain themselves?

weather-tweet2

Having set the stage for tackling the issue of weather mood on our Pulse platform, here I describe our process for developing weather as a Pulse topic. (more…)

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Why Weather Mood for Pulse?

by February 14, 2011

As we developed our interactive platform for the analysis of dialogue in the social media, we needed to identify a topic to start with. Specifically, we needed to identify a topic that would have a high volume of chatter on Twitter, be of general interest, and present a decent challenge for our research team. Plus, we wanted a topic that was likely to vary geographically, because Pulse is fundamentally a platform for examining trends in dialogue across geographies. (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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Crowd-Sourcing as a Core Business Strategy

by February 11, 2011

A version of this post appeared on The Daily Crowdsource.

I’m not exactly sure when it started, but just about every core strategy of my business now involves crowdsourcing. Or, is it crowd-sourcing? Crowd sourcing? The reason I’m not firmly convinced how to spell something that’s in practically every e-mail and tweet I write is an indication that one, it’s a new concept, and two, it’s something exciting enough to jump into without all the answers.

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