
How We're Feeling: Tweet sentiment - epaga
http://jenniferdewalt.com/node/how_were_feeling
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epaga
Source code for anyone interested :
[https://github.com/jendewalt/jennifer_dewalt/search?q=feelin...](https://github.com/jendewalt/jennifer_dewalt/search?q=feeling&ref=cmdform)

This is all part of Jennifer Dewalt's "180 web sites in 180 days" experiment:
[http://jenniferdewalt.com](http://jenniferdewalt.com)

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joeblau
I worked at a company where we built something similar, but far more advanced
that used NLP to gauge the sentiment of a tweet with the volume of the full
firehose (350-400 million messages a day). I like this approach better because
it's not actually trying to gauge the sentiment of the tweet, it's just
looking for hash tags. Great concept.

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hooande
Great, simple concept. It might help a little if the tweet counts were
normalized to control for the relative frequency of the terms. ie, people use
the word "love" more often than "hopeless" in general. [1]

I'm not trying to nitpick, this is an inspired UI. It's just that a spike in
something like "joyful" or "scared" could be interesting to see in relative
terms, especially when calculating the positivity index at the top. I know
that statistical accuracy isn't necessarily the point here, but working with
twitter data can be tricky. I'm sure that engineering it was hard enough. What
an interesting visualization.

[1] For example when looking at a standard corpora like the NLTK movie reviews
data set, the term "love" appears 1,588 times vs "helpless" appearing 47
times.

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warcode
The information on the page says this is directly related to counting
#hashtags, and not any natural language analysis.

I do not know if normalization would help in this instance as people are
taking a deliberate action in hash-tagging with the related feeling.

I've done some sentiment analysis and relationship/influence mapping on
twitter data and the 140 character limit often means you have to create a
specialized training set for your classifiers based on the group of people you
are targeting. Simply weighting existing training sets offer very small
benefits in accuracy.

~~~
choult
Clearly NLP/sentiment analysis on discrete units of 140 characters with no
extra context is problematic. However, the state of the art is accurate enough
that it's useful at higher volumes (eg. Twitter decahose scale).

Still, love the simplicity of this UI and concept.

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gnarmis
You can also use emoticons as a sort of simple, multi-lingual signal about the
emotion expressed in a tweet. :) -> happy, :( -> sad, and so on. I found out
about this while researching sentiment analysis techniques last summer; here's
a good paper:
[http://dl.acm.org/citation.cfm?id=1628969](http://dl.acm.org/citation.cfm?id=1628969).

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PavlovsCat
I really like how this looks, but when I see these things I just can't help
thinking of sentences that would be misdetected, e.g. "I haven't felt
angry/bored/jealous for ages", "my partner does not love me anymore", "hate is
such a waste of energy", and so on.

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nkozyra
As mentioned this doesn't appear to use NLP so that's moot. Most of the many
applications that do sentiment analysis handle such qualifiers and reduce
confidence in any given detected emotion.

Another common issue is context ... ie detecting "I feel awful ... Need a
coke" as negative sentiment against coke.

This is far more rudimentary.

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k-mcgrady
It's a simple page but I really like the design of this. The flashing of the
circles when the count is updated is a nice touch. I feel like I should be
able to click the hashtag to do a twitter search on it though.

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jsmcgd
Pretty cool. Is it possible to have the sentiments ranked by frequency?

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jes5199
Is there any evidence that sentiment analysis scores are correlated to
anything we already know about emotions? I find the whole concept to be
extremely speculative and dubious.

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gb
There's a similar project from 2005, based on blog posts:
[http://www.wefeelfine.org](http://www.wefeelfine.org)

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gerjomarty
We seem to be feeling a bit lovely and very sexy at the moment. Makes you
wonder in what context people are using these hashtags.

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rushabh
Bored is a positive feeling. Specially in 2014. I would love to get bored on
summer afternoons.

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fractalsea
It's funny; that's how I feel a lot of the time. It seems like so much of my
life is reacting to whatever is next on my "priority list". I miss the days
when I was younger and had the afternoon with nothin to do.

I feel like righ now I would want to relax and do nothing, but I know tat if I
had the oppertunity I would get bored, but from the boredness I would start
doing something interesting and creative.

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vincentperes
This is nice. Could we have different size of circle according to how frequent
it is?

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imikushin
definitely missing #nerdy

