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All Projects → SenticNet → Personality Detection

SenticNet / Personality Detection

Licence: mit
Implementation of a hierarchical CNN based model to detect Big Five personality traits

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python
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machine-learningconvolutional-neural-networkscnnlstmsentiment-analysistheanolstm-neural-networkscnn-keras

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Deep Learning-Based Document Modeling for Personality Detection from Text

This code implements the model discussed in Deep Learning-Based Document Modeling for Personality Detection from Text for detection of Big-Five personality traits, namely:

  • Extroversion
  • Neuroticism
  • Agreeableness
  • Conscientiousness
  • Openness

Requirements

Preprocessing

process_data.py prepares the data for training. It requires three command-line arguments:

  1. Path to google word2vec file (GoogleNews-vectors-negative300.bin)
  2. Path to essays.csv file containing the annotated dataset
  3. Path to mairesse.csv containing Mairesse features for each sample/essay

This code generates a pickle file essays_mairesse.p.

Example:

python process_data.py ./GoogleNews-vectors-negative300.bin ./essays.csv ./mairesse.csv

Training

conv_net_train.py trains and tests the model. It requires three command-line arguments:

  1. Mode:
    • -static: word embeddings will remain fixed
    • -nonstatic: word embeddings will be trained
  2. Word Embedding Type:
    • -rand: randomized word embedding (dimension is 300 by default; is hardcoded; can be changed by modifying default value of k in line 111 of process_data.py)
    • -word2vec: 300 dimensional google pre-trained word embeddings
  3. Personality Trait:
    • 0: Extroversion
    • 1: Neuroticism
    • 2: Agreeableness
    • 3: Conscientiousness
    • 4: Openness

Example:

python conv_layer_train.py -static -word2vec 2

Citation

If you use this code in your work then please cite the paper - Deep Learning-Based Document Modeling for Personality Detection from Text with the following:

@ARTICLE{7887639, 
 author={N. Majumder and S. Poria and A. Gelbukh and E. Cambria}, 
 journal={IEEE Intelligent Systems}, 
 title={{Deep} Learning-Based Document Modeling for Personality Detection from Text}, 
 year={2017}, 
 volume={32}, 
 number={2}, 
 pages={74-79}, 
 keywords={feedforward neural nets;information filtering;learning (artificial intelligence);pattern classification;text analysis;Big Five traits;author personality type;author psychological profile;binary classifier training;deep convolutional neural network;deep learning based method;deep learning-based document modeling;document vector;document-level Mairesse features;emotionally neutral input sentence filtering;identical architecture;personality detection;text;Artificial intelligence;Computational modeling;Emotion recognition;Feature extraction;Neural networks;Pragmatics;Semantics;artificial intelligence;convolutional neural network;distributional semantics;intelligent systems;natural language processing;neural-based document modeling;personality}, 
 doi={10.1109/MIS.2017.23}, 
 ISSN={1541-1672}, 
 month={Mar},}
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