Python command line application to add text features to a CSV or TSV dataset.
Project description
texturizer
Status - Functional
This is an application to add features to a dataset that are derived from processing the content of existing columns of text data. It is specifically designed for adding somewhat bespoke and unusual features that are not particularly well identified by n-gram or word embedding approaches.
It will accept a CSV, TSV or XLS file and output an extended version of the dataset with additional columns appended. When run with default settings it will produce a small number of very simple numerical summaries.
Additional feature flags unlock features that are more computationally intensive and generally domain specific.
Released and distributed via setuptools/PyPI/pip for Python 3.
Additional detail available in the documentation
TODO
Current features are all derived from single records. Future development will add these
in some sense relative to a corpus.
* Add capacity to generate features relative to corpus averages
* Add capacity for comparison features to be generated relative to reference text(s)
* Investigate functionality for working with unix shell pipes and streams
Distribution
Released and distributed via setuptools/PyPI/pip for Python 3.
Resources & Dependencies
For Part of Speech Tagging we use spacy
Note: After install you will need to get spaCy to download the English model.
sudo python3 -m spacy download en
For string based text comparisons we use jellyfish and textdistance
Features
Each type of feature can be unlocked through the use of a specific command line switch:
- -topics. Indicators for presence of words from common topics.
- -topics=count. Counts of all word matches from common topics.
- -pos. Part of speech proportions in the text.
- -literacy. Checks for common literacy markers.
- -traits. Checks for common stylistic elements or traits that suggest personality type.
- -rhetoric. Checks for rhetorical devices used for persuasion
- -profanity. Profanity check flags.
- -sentiment. Sentiment word counts and score.
- -scarcity. Word scarcity scores.
- -emoticons. Emoticon check flags.
- -comparison. Cross-column comparisons using edit distance metrics
Usage
You can use this application multiple ways
Use the runner without installing the application. The following example will generate all features on the test data.
./texturizer-runner.py -columns=question,answer -pos -literacy -traits -rhetoric -profanity -emoticons -sentiment -comparison -topics=count data/test.csv > data/output.csv
This will send the time performance profile to STDERR as shown below:
Computation Time Profile for each Feature Set
---------------------------------------------
simple 0:00:00.580910
comparison 0:00:00.490972
profanity 0:00:00.507172
sentiment 0:00:03.611817
emoticons 0:00:00.387556
topics 0:00:02.778537
traits 0:00:00.262633
rhetoric 0:00:02.107620
pos 0:00:22.130724
literacy 0:00:00.488886
As you can see the part of speech (POS) features are the most time consuming to generate. It is worth avoiding them on very large datasets.
Alternatively, you can invoke the directory as a package:
python -m texturizer -columns=question,answer data/test.csv > data/output.csv
Or simply install the package and use the command line application directly
Installation
Installation from the source tree:
python setup.py install
(or via pip from PyPI):
pip install texturizer
You will then need to run the POST INSTALL SCRIPT to install the required Spacy Model (otherwise the POS features cannot be calculated).
Now, the texturizer
command is available::
texturizer -columns=question,answer -topics data/test.csv > data/output.csv
This will take the Input CSV, calculate some simple summary features and produce an Output CSV with features appended as new columns.
For more complicated features see the additional options (outlined above).
Acknowledgements
Python package built using the bootstrap cmdline template by jgehrcke
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