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analysis of Touhou project Vote 東方Project人気投票データの解析

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Touhou Vote Analysis

東方プロジェクト人気投票の解析

Bayesian analysis of Touhou project vote data

data source

China(optional)

related information

related articles

method

summary of elections

Open In Colab

scraping

Open In Colab

EDA

Open In Colab

correcation between title/character/music ranking

Open In Colab

relation between stages and votes

Open In Colab

bayesian analysis

run

 python analysis_char.py 18 18 sum

Open In Colab

Bayesian analysis by using pystan/gensim

python3 analysis.py 18 5 modelsuffix

18 is election number, 5 is duration of time effect

model

basic models

power(charm) model

for normalized vote numbers

$$ normVote_{i,t} \sim Dir(\sum_{l=1}^{TM} main_{j(i,t-l),t-l} \sigma_{j,l} \ + \sum_{l=1}^{TM} boss_{j(i,t-1),t-l}Lv_i b_{j(i,t-l),l} \ +\sum_{l=1}^{TM} sub_{j(i,t-l)} s_{j,l}+\epsilon_i ) $$

table flags (1 or 0)
  • $main_{j(i,t),t}$ i is main character of subtitle just before tth election
  • $boss_{j(i,t),t}$ i is boss character of subtitle just before tth election
  • $Lv_i$ boss level of character i
  • $sub_{j(i,t),t}$ i is character of noninteger subtitle just before tth election
parameters
  • $\sigma$ coef of integer title main characters
  • b coef of integer title bosses
  • s coef of noninteger title members
  • $\epsilon_i$ indivisual term
index
  • t index of time(election)
  • l index of realtive time
  • i index of characters
  • j index of mainchar,boss,subtitle table

subtitle model(topic model)

$\theta_m \sim Dir(\alpha)$ K dimension

$\phi_m \sim Dir(\beta)$ V dimension

$z_n \sim Cat(\theta_m)$ K dimension

$w_n \sim Cat(\phi_{z_n})$ V dimension

  • V num. of characters
  • K num. of titles
  • N num. of total votes
  • M num. of elections
  • $\alpha, \beta$ hyperparameter

caluculate by using gemsim Open In Colab

Visualize result posterior distribution of parameters

Open In Colab

Reference

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