Well, we’ve spent almost ten months working and reworking our Levee conversation analysis dashboard, and we’re finally ready to release it to the world. Loudpixel is now accepting beta partners for its Levee brand analysis dashboard.
The dashboard is set up to accommodate the two sides of social media monitoring—day-to-day tracking of individual issues and opportunities and deeper insights and measurement based on overall conversations and trends. It’s important to note that Levee was built to make human analysis easier; it works with existing data aggregators, rather than competing against them.
If you’re interested in Levee, learn more about the launch, watch a Levee demo and get in touch to learn more.
Posted in Tools by Allie | No Comments
In a perfect world, everything can be accomplished by the touch of a button. Alas, this world is far too complicated to rely on buttons—it requires the human touch in order to keep things running smoothly.
This is the case with social media analysis. While computerized monitoring tools can provide solid quantitative data, they are often unable to pick up on the humanistic side of social media responses. For example, a data source may read, “I’m so glad I spent 10 bucks on this awesome movie….right…” as a positive post. However, most humans would classify this as a sarcastic comment. Monitoring software can easily misread sentiment in sarcasm, ambiguity, and may likely incorrectly categorize simple positive or negative posts.
Sentiment is just one aspect of social media analysis that can skew product perception when it is solely computerized. As we’ve reiterated time and time again, human review is necessary in order to guarantee accuracy of information and to fully utilize the capabilities of whatever software you choose to employ. These online tools are essential for securing data quickly and efficiently, but in order to get the most out of your monitoring program, you’ll also need a good pair of eyes.
Posted in Tools by Lesley | No Comments
The race is on to find the perfect social media monitoring tool. A number of big players, including SAS, IBM and Microsoft, have entered the playing field to compete with some of the original players, such as Radian6, Biz360 and Scout Labs.
But the fact is, no single tool is perfect. Whether dealing with the difficulties of Facebook privacy issues, capturing accurate sentiment, filtering out spam or trying to pull in the most relevant conversation data for the brand or organization, no tool can do it all. The perfect technology solution for social media monitoring simply does not exist.
This means that users of such tools need to be extremely critical of the conversation data they’re using. While the tools are powerful—and a great start to drawing insightful conclusions—users need to be checking for irrelevant posts, constantly updating keyword search strings, considering the best approach to tracking sentiment (human vs. automated), keeping an eye out for content duplication and watching some conversations (brand Facebook walls, for example) more closely on a manual basis.
Don’t blindly trust your conversation data. To use a simple metaphor, post data should be approached like a garden—weed daily and apply regular maintenance to avoid becoming overrun with content you don’t want.
Posted in Tools by Allie | No Comments
When it comes to social media measurement and reporting, automated sentiment is often viewed as a miracle technology—one that saves time by replacing the need for any human intervention. But it’s important to look at this technology with a critical eye before accepting automated sentiment results as fact.
There are a few problems with the nature of natural language processing, which is behind automated sentiment. Even if a technology claims to truly understand the English language, it is still difficult for it to understand sarcasm, misspellings or slang—which are all prevalent across social media posts. I should also note that Biz360, one tool that tracks social media posts, pulled down it’s automated sentiment tracking for Twitter because there are not enough characters to give context for proper processing.
To show a firsthand look at how posts are being categorized through natural language processing, I took a quick look at posts related to “girl scout cookies” (a timely topic). The following are just a few examples of posts that ended up with the wrong categorization.
Marked as Negative:
Marked as Positive:
While the tools for conversation analysis are improving at a rapid pace, it’s important to remember that human analysis is still an important part of the research process.
Posted in Tools by Allie | 1 Comment
