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Emotion-based Recommendation Generator
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Emotion-Based Recommendation Generator (EMORec v1.0)
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README:
=======
A Python library which performs emotion-based analysis and recommendation using a
multiple-instance regression algorithm for a set of multimedia items described by
transcripts. The algorithm is trained over 1200 TED talks using the original human-
made transcripts and the corresponding community emotion labels. The library can be
used in command line or directly in a Python program. It takes as input a JSON file
which contains an array of dictionaries that describe the metadata of multimedia items
and generates an output JSON file which contains the same items augmented with the
following attributes:
emotion_classes The class names of 12 TED community emotion labels
emotion_scores Estimated values for 12 TED community emotion labels
emotion_rec Recommended items based on these emotions
emotion_rec_scores Confidence of the recommended item
emotion_segments Textual segments that were used
text The actual textual content of the segment
start_time Starting time of the segment
end_time Ending time of the segment
relevance_scores Relevance which reveals the contribution of the segment
to the prediction of the 14 emotion dimensions.
FILES:
======
The library co
5ED7
ntains the following files:
ap_weights.py Data class for items (text extraction, preprocessing)
crls.py Vector space class supporting TF-IDF, LSI, RP and LDA
generate.py Main class responsible for generating recommendations
data/ Data to be used for training
models/ Pre-trained regression models on TED for emotion prediction
parameters/ Optimal values obtained from cross-validation to be used
for training and prediction
USAGE:
======
USAGE: python generate.py -input=<path> -output=<path>
-input Path location of the input file in JSON format
-output Path location of the output file in JSON format
EXAMPLE:
========
$ python generate.py --input=input.json --output=output.json --debug
{'--debug': True,
'--display': False,
'--help': False,
'--input': 'input.json',
'--output': 'output.json',
'--version': False}
[+] Loading items:....................................[OK]
[+] Modeling emotions:
-> Unconvincing...............................[OK]
-> Fascinating................................[OK]
-> Persuasive.................................[OK]
-> Ingenious..................................[OK]
-> Longwinded.................................[OK]
-> Funny......................................[OK]
-> Inspiring..................................[OK]
-> Jaw-dropping...............................[OK]
-> Courageous.................................[OK]
-> Beautiful..................................[OK]
-> Confusing..................................[OK]
-> Obnoxious..................................[OK]
[+] Generating recommendations........................[OK]
[+] Saving to output file.............................[OK]
[x] Finished.
DEPENDENCIES:
============
1) Install python: http://www.python.org/getit/
2) Install pip: http://www.pip-installer.org/en/latest/installing.html
3) Then:
$ pip install docopt
$ pip install json
$ pip install pyyaml
$ pip install numpy
$ pip install scipy
$ pip install gensim
$ pip install nltk
$ python
>>> import nltk
>>> nltk.download()
TROUBLESHOOTING:
================
Q: How can I use the library with items stored in other formats than JSON?
A: You have to convert your file to JSON.
Q: How can I use the library directly inside a Python program?
A: Simply import the library in Python and initialize a generator object with
the item dictionary of your preference.
Q: Is there any attribute that is required to be present in the item metadata?
A: Yes the 'id' attribute is mandatory.
CONTACT:
========
Nikolaos Pappas
Idiap Research Institute
Centre du Parc,
CH 1920 Martigny,
Switzerland
E-mail: nikolaos.pappas@idiap.ch
Website: http://people.idiap.ch/npappas/
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Last update:
8 Jul, 2014About
Emotion-based Recommendation Generator
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