Extracts the coefficients from a classification model and turns them into a lexicon.
Argument and Default Value¶
Name of the lexicon to be created.
Use this switch to create a lexicon from a classification model. Either create the lexicon from a previously created model (using --load_model) or create a model using --train_classifiers. The name of the lexicon will be dd_ARGUMENT ; dd indicates the lexicon was data driven.
Only use classification algorithms that have linear coefficients (i.e by choosing the right --model), because the lexicon extraction equation won't make sense otherwise. This functionality hasn't totally been validated with advanced feature selection, so beware. Also note that the coefficients won't be efficient to distinguish what features best characterize the outcomes looked at, use --correlate or other univariate techniques to get at that type of insight.
- This only works with binary classification, so if you want to do this with multiple classes (>2), create variables that are binary.
- When creating the model, use --no_standardize or the model will not make any sense.
- Multiple feature tables are allowed.
- Lexica can be created with any word level feature (such as ngrams, dictionaries and topics) whose group norm is a relative frequency. Dictionaries and topics will be unrolled to the word level.
- You cannot combined features with different group norm encodings (for example, binary 1grams and tf-idf 1grams).
Needs one of these two switches:
This command will train a logistic classification model to predict gender for users from 1grams (without standardizing) and create a lexicon called dd_genderLex1grams.
dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1gram$msgs$user_id$16to16$0_01' --outcome_table blog_outcomes --outcomes gender --train_classifiers --no_standardize --classification_to_lexicon genderLex1grams --model lr
This command will train a logistic classification model to predict age for users from 1grams and topics and create a lexicon called dd_ageLex1gramsTopics.
dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1gram$msgs$user_id$16to16$0_1' 'feat$cat_met_a30_2000_cp_w$msgs$user_id$1gra' --outcome_table blog_outcomes --outcomes gender --train_classifiers --no_standardize --classification_to_lexicon ageLex1gramsTopics --model lr
- Sap, M., Park, G., Eichstaedt, J., Kern, M., Stillwell, D., Kosinski, M., ... & Schwartz, H. A. (2014, October). Developing age and gender predictive lexica over social media. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1146-1151).