texturizer 0.2.0
Python command line application to add text features to a CSV or TSV dataset.
License: MIT

texturizer
----------

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```
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](https://texturizer.readthedocs.io)

### 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 and Word Embeddings we use [spacy](https://spacy.io/usage/spacy-101)

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](https://pypi.org/project/jellyfish/) and
[textdistance](https://pypi.org/project/textdistance/)

## Features

Each type of feature can be unlocked through the use of a specific command line switch:

```
  -topics            Default: False. Indicators for words from common topics.
  -topics=count                      Count matching words from common topics.
  -topics=normalize                  Count matching topic key words and normalize over topics.
  -traits            Default: False. Word usage for personality traits.
  -reason            Default: False. Word usage for reasoning: premises, conclusions.
  -rhetoric          Default: False. Word usage for rhetorical devices.
  -pos               Default: False. Part of speech proportions.
  -literacy          Default: False. Checks for simple literacy markers.
  -profanity         Default: False. Profanity check flags.
  -sentiment         Default: False. Words counts for positive and negative sentiment.
  -scarcity          Default: False. Word scarcity scores.
  -emoticons         Default: False. Emoticon check flags.
  -embedding         Default: False. Aggregate of Word Embedding Vectors.
  -embedding=normalize               Normalised Aggregate of Word Embedding Vectors.
  -comparison        Default: False. Cross-column comparisons using edit distances.

```

## Usage

You can use this application multiple ways

### Runner

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 -reason -rhetoric -profanity -emoticons -embedding -sentiment -scarcity -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               0:00:00.498634
comparison           0:00:00.536637
profanity            0:00:00.496018
sentiment            0:00:03.310224
scarcity             0:00:00.523863
emoticons            0:00:00.219341
embedding            0:00:43.456939
topics               0:00:05.285120
traits               0:00:00.298902
reason               0:00:00.305391
rhetoric             0:00:02.988197
pos                  0:00:40.981175
literacy             0:00:00.371007
```  

As you can see the part of speech (POS) features and word embeddings
are the most time consuming to generate. In both instances these rely on the 
SpacY package to process the text block. For the moment it would be advised to
avoid using them on very large datasets.

TODO: improve performance of these feature generators. 

### Directory as package 

Alternatively, you can invoke the directory as a package:
 
```
python -m texturizer -columns=question,answer data/test.csv > data/output.csv
```

### From Install

Or you can simply install the package and use the command line application directly

```
texturizer -h
```
Will print out the help


# 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](https://github.com/john-hawkins/texturizer/blob/master/POST_INSTALL.sh) 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](https://github.com/jgehrcke/python-cmdline-bootstrap)
 by [jgehrcke](https://github.com/jgehrcke)