Project: Paul, Octopus for Instagram

Demo video




This is a web application that informs an Instagram user of taste group of his/her followers, and predicts how many people will like the post.

Technical Stack

  • PythonFlask
    • numpy
    • pickle
    • Konlpy
  • dj3.js
  • Selenium
  • ResNet




Project Detail

Data Collection

Crawling target user’s whole paged Instagram page for an initial request using selenium web driver and cache it on disk using pickle.


  • Used Konlpy – mecab to perform morpheme analysis on posted articles. Only the words tagged with nouns are judged to be a specific keyword rather than the grammatical elements of the article that affect the number of likes.
  • Calculated the conditional probabilities to be used for the nib base probability based on features analyzed in noun units.
  • Use Keras’ pre-trained ResNet model to get a list of objects and animals in the posting photos and use them as features.


Taste group clustering: K-Means clustering

Set the square root of the number of engaged users that are the subject of clustering to the number of clusters K of K-means clustering.

Distance(A, B)=HammingDistance(VA, VB)
    But. VA={wher User A likes Post i}

=<0, 1, 1,0, 0, 0, 0, 1>

Likes prediction: Regression with Naive bayes

We use a mixture of Regression Model and Naive Bayes model to predict the number of likes. Naive bayes calculates the likelihood that a user will click on the likes of a post containing a specific word and object picture through probability calculation, and then multiplies this probability by the parameter to regress the likelihood of an expectation.

스크린샷 2017-09-01 오전 12.53.22


  • R-Square: 0.36853623771555538
  • MSE: 192.87811680608883


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