Neurodevelopmental disorders according to the dsm 5 tr
AINL 2016: Moskvichev
1. Data Augmentation Method for the
Image Sentiment
Analysis
Alexander Rakovsky1, Arseny Moskvichev2, Andrey Filchenkov1
1ITMO University
Saint Petersburg, Russia
2Saint Petersburg State University
Saint Petersburg, Russia
str1t3r@gmail.com, arseny.moskvichev@gmail.com, afilchenkov@corp.mail.ru
3. Why is it important?
Two words:
Social networks
4. How do we approach it?
1.Collect lots of labeled images
2.Train a convolutional neural network
3.???
4.Profit
5. How do we approach it?
1.Collect lots of labeled images
2.Train a convolutional neural network
3.???
4.Profit
Problem!
6. Solution
Data augmentation.
1.Get a few manually labeled images with
corresponding hashtags
2.Learn to reconstruct labels from hashtags
3.Collect as much labeled data as you need!
7. Details
• Collecting data through FLICKR API (using
keywords)
• Assessors evaluate the emotional colouring
(positiveness) of each image
• Converting hashtags to vector representation
(word2vec), and averaging them
• Using machine learning to predict assessors’
estimation
8. (Preliminary!) Results
• kNN accuracy on classification task: 0.95
• Average correlation between assessors: 0.86
• Between the kNN regression and assessors:
0.83
• Using this algorithm is almost as good as
hiring one more assessor!
• Suspiciously good...
9. Details
• Collecting data through FLICKR API (using
keywords)
• Assessors evaluate the emotional colouring
(positiveness) of each image
• Converting hashtags to vector representation
(word2vec), and averaging them
• Using machine learning to predict assessors’
estimation
Nonrepresentative sample!
10. Pros
• Easy to use (no word preprocessing)
• Good results* (compared to dictionary -
based solutions)
12. Conclusions
• The proposed method affords a simple and
efficient hashtag-based data augmentation
solution for image sentiment analysis.
• More work is to be done to estimate the
method’s performance on a general set of
images.