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The Singletrack Forum is really quite a thing to study. In the last 14 years its accrued more than 10 million posts generated by more than 120k registered users (In 2021 we averaged 1.5 million visitors per month). In that time every topic and reply has been stored in our database from both the Chat and Bike forums. As has always been the case the Chat forum edges out the Bike forum in terms of popularity which means overall our forum tends to reflect every thing from the first economic crash in 2008 to the latest global crisis in eastern Europe.
More recently we’ve been monitoring what we like to call the ‘mood’ of the forum using some natural language machine learning tools created by Google. What this essentially boils down to is looking at every post in the forum and assigning it a score based on it’s perceived sentiment. Sentiment just means is the post generally positive or negative. The score that comes back is in the range of +1 to -1 with 0 representing a neutral sentiment.
There’s lots of clever stuff going on with the analysis like ignoring any post that’s too short and the like. It’s all statistics and it’s obviously down to the individual how that is interpreted.
Here is the result of the last 18 months of forum activity all melted down and converted in to a sentiment analysis. Each data point on this graph represents the average sentiment score for the forum posts made in that week, of which there are typically thousands.
The data is anonymous so there’s no telling who is responsible for anything on this chart and I’m posting it here essentially for fun, interest, curiosity.
We’d love to here your theories on what’s happening and if you want to check out more of the technology behind this then this is good place to start for the coders out there.
And just for fun you can actually try it out for yourself here.
The graph below represents the daily sentiment score of the forum over the last 90 days. You can clearly see that the forum was most happy on the 25th December.
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Home › Forums › Forum Sentiment Analysis: The mood of the ‘nation’ in 1 graph
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