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Show HN: London Feels – Sentiment-analysis of Londoners Tweets on a Map

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Re: Show HN: London Feels – Sentiment-analysis of Londoners Tweets on a Map

#31

Post by radiodario »

ripb wrote: post #26

Nice site, although it does have issues with the analysis as outlined by others here. One suggestion I would have is some sort of filtering based on the content of the tweets. This tweet returned "feeling good": "New post featuring: @NewLookPRTeam @nextofficial @Matalan @hmunitedkingdom @uoeurope @Accessorize @ASOS http://t.co/nGsFj306xT #fbloggers" This one returned "feeling average": "@dannykobe17 @DanielRacheter @…

thanks! that's definitely a good idea, how would you go about doing this? counting @mentions vs "tokens"/workds and setting a threshold ratio to remove tweets?

Re: Show HN: London Feels – Sentiment-analysis of Londoners Tweets on a Map

#33

Post by tormeh »

radiodario wrote: post #30
kriro wrote: post #29

Very cool but before clicking on some dots I was wondering why everyone feels the same. The colors are not ideal for red/green colorblind people (is it blue and purple?) Maybe include a feature to select the colors for happy/sad/average with a button to return to defaults? Black for sad, light grey for neutral, something like a medium bright green for happy would be my picks.

cool idea - i tried to pick perceptually separate colours and thought blue and red would work, but turns it's confusing for some people. I might add "colorblind" mode and use your colour range.

How about having numbers in the circles?

Re: Show HN: London Feels – Sentiment-analysis of Londoners Tweets on a Map

#34

Post by lstyls »

Some of these are unintentionally hilarious without context. Here's a real gem: https://imgur.com/c7Ly6Qm

All in all, though, impressive. Sure some are misclassified but it seems like a significant majority are not, including a lot of the hard ones. Good work!

Re: Show HN: London Feels – Sentiment-analysis of Londoners Tweets on a Map

#35

Post by pimlottc »

radiodario wrote: post #30
kriro wrote: post #29

Very cool but before clicking on some dots I was wondering why everyone feels the same. The colors are not ideal for red/green colorblind people (is it blue and purple?) Maybe include a feature to select the colors for happy/sad/average with a button to return to defaults? Black for sad, light grey for neutral, something like a medium bright green for happy would be my picks.

cool idea - i tried to pick perceptually separate colours and thought blue and red would work, but turns it's confusing for some people. I might add "colorblind" mode and use your colour range.

"colorblind mode" is an antipattern. Using more contrasting colors and using other distinguishing features such as shape and texture benefits all users.

Re: Show HN: London Feels – Sentiment-analysis of Londoners Tweets on a Map

#37

Post by cl42 »

chippy wrote: post #18

I once did a map of the UK using sentiment analysis of the text of geotagged Flickr photos, hoping to find the areas which were more happier than others. Turned out there was no geographical pattern from that data. Geographical analysis tools should be used in these types of analyses, apart from just looking at blobs on a map. I used k-means based cluster analysis to find groups of happy and sad areas but again the g…

What sort of accuracy did you have in your sentiment analysis algorithms? I'm curious because I find the error rate in such algorithms is typically higher than any sort of variance you are seeking, which causes significant problems in terms of any sort of pattern recognition.

When you're using things as short as Tweets, and as broad as "general sentiment", you're probably making accuracy even worse, to the point that simpler demographic analysis or bag-of-words clustering (i.e., cluster areas by diction rather than by sentiment) yields more reliable results, even for sentiment.

Re: Show HN: London Feels – Sentiment-analysis of Londoners Tweets on a Map

#39

Post by chippy »

cl42 wrote: post #37
chippy wrote: post #18

I once did a map of the UK using sentiment analysis of the text of geotagged Flickr photos, hoping to find the areas which were more happier than others. Turned out there was no geographical pattern from that data. Geographical analysis tools should be used in these types of analyses, apart from just looking at blobs on a map. I used k-means based cluster analysis to find groups of happy and sad areas but again the g…

What sort of accuracy did you have in your sentiment analysis algorithms? I'm curious because I find the error rate in such algorithms is typically higher than any sort of variance you are seeking, which causes significant problems in terms of any sort of pattern recognition. When you're using things as short as Tweets, and as broad as "general sentiment", you're probably making accuracy even worse, to the point that…

Accuracy was pretty bad in sentiment analysis. Especially how people talk on tweets. Loads of false positives.
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