34 lines
1.5 KiB
TeX
34 lines
1.5 KiB
TeX
\chapter{Methodology}
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% sentiment calculation via vaderlib, write whole paragraph
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% data sets as xml from archive.org
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%cleaning data
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% broken entries, missing user id
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% answers in html -> strip html and remove code sections, not contribution to sentiment
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% calc sentiment for answers
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% about the change
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% https://meta.stackexchange.com/questions/314287/come-take-a-look-at-our-new-contributor-indicator?cb=1
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% https://meta.stackexchange.com/questions/314472/what-are-the-exact-criteria-for-the-new-contributor-indicator-to-be-shown ; change date = 2018-08-21T21:04:49.177
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% new user indicator visible for 1 week ...
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% differences in avg sentiment
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% look at plots and write something that fits
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%interrupted time series
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% ref tutorial paper
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% often used in medical fields to see if changes have an effect
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% used same tensors as describe in paper, show formula and how it works, 3 tensors describe tensors and what they capture
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% explain why i cose this model, captures the change, more complex model would capture more but also get more complicated, these 3 tensors are enough to see the impact
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% fitting every value not aggregated values, aggregated values would have different weights, weights are too far spread, contrary to paper where person years are more or less constant
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% single value fitting is better, no weight issues, as weights are taken care of via more values
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% if one month has more values than another then that month affects its more as more values are present
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