How To Monitor Sentiment And Benefit From The Insight This Offers

Marshall Sponder
Webmetricsguru.com
March 31, 2010
London
How to Monitor Sentiment & Benefit from the Insight This Offers

Building your own Social Monitoring Dashboard
I have written a draft version of an eBook available on Scribd - http://www.scribd.com/doc/29039169/How-to-Build-Your-Own-Social-Media-Monitoring-Service-Marshall-Sponder-Webmetricsguru-dot-com-3-31-2010-V2
I hope to have a clean version of Social Influence Monitoring On a Shoestring in a few months for those of you here today .
Feel free to download a copy to continue and enhance what your are learning today at #msmb10

How to Monitor Sentiment & Benefit from Insights This Offers
Automated Sentiment Analysis is Flaky – same exact query – different results from every vendor.
Radian6
Biz360id
BrandWatch
Techrigy
Sysomos
Refer to pages 78-84 of Social Influence Monitoring on a Shoestring

Radian6 is getting better with sentiment but there’s too much that has to be done on a configuration level to get sentiment right
Single word and isolated phrase sentiment analysis is almost useless - there is little insight to be gained from the analysis of emotion around single words.
However, the more carefully you select terms to measure around the more useful Radian6 Sentiment analysis will be.
Refer to page 83 of Social Influence Monitoring on a Shoestring.

Alterian/Techrigy/SM2
Alterian has several types of sentiment it measures including Brand References and tonality, but it also breaks down emotions in each mention online, a unique feature no other vendor offers. You can use SM2 to segment online audiences by complex emotions such as “sophistication”
Refer to pages 120-122 of Social Influence Monitoring on a Shoestring.

Sysomos MAP appears to be more accurate than most of the platforms I have worked with.
Sysomos MAP has segmentation capabilities using filters - sentiment analysis works best when you refine your query.
Refer to pages 156-160 of Social Influence Monitoring on a Shoestring.

Crimson Hexagon and Adaptive Semantics offer Opinion Mining and more accurate Sentiment Analysis though machine learning algorithms
Crimson Hexagon’s Summarizer provides an interpretation of what a query means (query = health care reform). Machine learning can be more accurate but the algorithms must be properly trained, and results depend largely on the analyst who trains them, the categories they create and the methodology they use.
Refer to pages 118-120, 145 of Social Influence Monitoring on a Shoestring.

Examples of Sentiment Analysis gone wrong
Most Sentiment Analysis is incapable of connecting what you’re monitoring (the subject for which you want to know about) and what the platform picks up and rates for sentiment.
One example was a monitor I set up using a monitoring platform (Crimson Hexagon Buzz Monitor) for employees of a well known PR firm that went on TV to talk about the economy.
The person said the Economy was going through a “rough patch” and the sentiment analysis was ranked as “negative” but what we were monitoring is not the economy, but reputation of the PR firm – in that context – the remark was neutral (certainly not negative).
Refer to pages 68of Social Influence Monitoring on a Shoestring.

No standards for Sentiment Analysis – each platform processes the same exact queries and comes up with different sentiment scores – the “quantum” nature of Sentiment Analysis today make it too unreliable to depend on without human review.
You can’t get your ducks lined up in a row.

Social Media is dominated by people who have often express complex emotions such as sarcasm, especially in Tweets – and blog posts such as Hugh McCloud of Gapingvoid – are known for their wit –
Most platforms can not deal with wit, sarcasm and complex emotions which make them unsuitable for many of the questions we have.

Typical posts from bloggers are often incorrectly scored because the algorithm is focused on words instead of overall meaning.
Source: Sysomos Map
Refer to pages 70 of Social Influence Monitoring on a Shoestring.

Most “negative” statements online turn out to be positive while many social mentions considered to positive turn out to be negative. when a human examines them.
Source: Sysomos Map

Humans don’t often say exactly what they mean (and don’t know what they mean half the time) –meanwhile search engines are guessing what people really mean.
If Google can’t serve the content people are looking for in many cases (because they are unable or unwilling to articulate it) then we might end up with a situation of a dog chasing its tail .
We’re doing Sentiment Analysis on content that doesn’t even represent what it’s authors really felt – and then with the noise created by the inaccurate machine interpretation – it’s almost as if there is a total disconnect between what a person says, what they express, and what a social media monitoring platform interprets it to be.

What do we do now?
Spend more time choosing the terms we search for – malformed questions make for very difficult analysis.
2. Allow Analysts much more time to work with social monitoring data in order to surface it’s real meaning.
3. Employ machine learning algorithms whenever possible and affordable to help with analysis.
4. Help form an independent organization willing to enact Social Media Monitoring standards.
5. Choose one platform to work with in terms of sentiment – don’t mix platforms for the same feature in any presentation or analysis.
6. Wait 20 years more for the Singularity to occur –computers will be more powerful and mimic the human mind using Quantum Computing.

Contact me:
Marshall Sponder
Webmetricsguru.com
@webmetricsguru
Now.seo@gmail.com

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How To Monitor Sentiment And Benefit From The Insight This Offers

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