napkin-text-analysis

Napkin is a Python tool to produce statistical analysis of a text.

Analysis features are :

Verbs frequency
Nouns frequency
Digit frequency
Labels frequency such as (Person, organisation, product, location) as defined in spacy.io named entities
URL frequency
Email frequency
Mention frequency (everything prefixed with an @ symbol)
Out-Of-Vocabulary (OOV) word frequency meaning any words outside English dictionary

Verbs and nouns are in their lemmatized form by default but the option --verbatim allows to keep the original inflection.

Intermediate results are stored in a Redis database to allow the analysis of multiple text files.

requirements

Python >= 3.6
spacy.io
redis (a redis server running on port 6380 is required)
pycld3
tabulate

how to use napkin

usage: napkin.py [-h] [-v V] [-f F] [-t T] [-s] [-o O] [-l L] [--verbatim]
                 [--no-flushdb] [--binary] [--analysis ANALYSIS]
                 [--disable-parser] [--disable-tagger]
                 [--token-span TOKEN_SPAN] [--table-format TABLE_FORMAT]

Extract statistical analysis of text

optional arguments:
  -h, --help            show this help message and exit
  -v V                  verbose output
  -f F                  file to analyse
  -t T                  maximum value for the top list (default is 100) -1 is
                        no limit
  -s                    display the overall statistics (default is False)
  -o O                  output format (default is csv), json, readable
  -l L                  language used for the analysis (default is en)
  --verbatim            Don't use the lemmatized form, use verbatim. (default
                        is the lematized form)
  --no-flushdb          Don't flush the redisdb, useful when you want to
                        process multiple files and aggregate the results. (by
                        default the redis database is flushed at each run)
  --binary              set output in binary instead of UTF-8 (default)
  --analysis ANALYSIS   Limit output to a specific analysis (verb, noun,
                        hashtag, mention, digit, url, oov, labels, punct).
                        (Default is all analysis are displayed)
  --disable-parser      disable parser component in Spacy
  --disable-tagger      disable tagger component in Spacy
  --token-span TOKEN_SPAN
                        Find the sentences where a specific token is located
  --table-format TABLE_FORMAT
                        set tabulate format (default is fancy_grid)

example usage of napkin

Generate all analysis for a given text

A sample file "The Prince, by Nicoló Machiavelli" is included to test napkin.

python3 ./bin/napkin.py -o readable -f samples/the-prince.txt -t 4

Example output:

╒══════════════════╕
│ Top 4 of verb    │
╞══════════════════╡
│ 207 occurences   │
├──────────────────┤
│ will             │
├──────────────────┤
│ 137 occurences   │
├──────────────────┤
│ can              │
├──────────────────┤
│ 116 occurences   │
├──────────────────┤
│ make             │
├──────────────────┤
│ 106 occurences   │
├──────────────────┤
│ may              │
├──────────────────┤
│ 102 occurences   │
├──────────────────┤
│ would            │
╘══════════════════╛
╒══════════════════╕
│ Top 4 of noun    │
╞══════════════════╡
│ 206 occurences   │
├──────────────────┤
│ prince           │
├──────────────────┤
│ 120 occurences   │
├──────────────────┤
│ man              │
├──────────────────┤
│ 108 occurences   │
├──────────────────┤
│ state            │
├──────────────────┤
│ 90 occurences    │
├──────────────────┤
│ people           │
├──────────────────┤
│ one              │
╘══════════════════╛
╒═════════════════════╕
│ Top 4 of hashtag    │
╞═════════════════════╡
╘═════════════════════╛
╒═════════════════════╕
│ Top 4 of mention    │
╞═════════════════════╡
╘═════════════════════╛
╒═══════════════════╕
│ Top 4 of digit    │
╞═══════════════════╡
│ 1 occurences      │
├───────────────────┤
│ 99775             │
├───────────────────┤
│ 84116             │
├───────────────────┤
│ 750175            │
├───────────────────┤
│ 6221541           │
├───────────────────┤
│ 57037             │
╘═══════════════════╛
╒═════════════════════════════════════════╕
│ Top 4 of url                            │
╞═════════════════════════════════════════╡
│ 5 occurences                            │
├─────────────────────────────────────────┤
│ www.gutenberg.org                       │
├─────────────────────────────────────────┤
│ 2 occurences                            │
├─────────────────────────────────────────┤
│ www.gutenberg.org/donate                │
├─────────────────────────────────────────┤
│ 1 occurences                            │
├─────────────────────────────────────────┤
│ www.gutenberg.org/license               │
├─────────────────────────────────────────┤
│ www.gutenberg.org/contact               │
├─────────────────────────────────────────┤
│ http://www.gutenberg.org/5/7/0/3/57037/ │
╘═════════════════════════════════════════╛
╒═════════════════╕
│ Top 4 of oov    │
╞═════════════════╡
│ 9 occurences    │
├─────────────────┤
│ Sforza          │
├─────────────────┤
│ 7 occurences    │
├─────────────────┤
│ Fermo           │
├─────────────────┤
│ 6 occurences    │
├─────────────────┤
│ Vitelli         │
├─────────────────┤
│ Pertinax        │
├─────────────────┤
│ Orsinis         │
╘═════════════════╛
╒════════════════════╕
│ Top 4 of labels    │
╞════════════════════╡
│ 339 occurences     │
├────────────────────┤
│ PERSON             │
├────────────────────┤
│ 305 occurences     │
├────────────────────┤
│ GPE                │
├────────────────────┤
│ 197 occurences     │
├────────────────────┤
│ CARDINAL           │
├────────────────────┤
│ 189 occurences     │
├────────────────────┤
│ ORG                │
├────────────────────┤
│ 131 occurences     │
├────────────────────┤
│ NORP               │
╘════════════════════╛
╒═══════════════════╕
│ Top 4 of punct    │
╞═══════════════════╡
│ 3440 occurences   │
├───────────────────┤
├───────────────────┤
│ 144 occurences    │
├───────────────────┤
├───────────────────┤
│ 32 occurences     │
├───────────────────┤
├───────────────────┤
│ 26 occurences     │
├───────────────────┤
├───────────────────┤
│ 11 occurences     │
├───────────────────┤
│ 1.F.3             │
╘═══════════════════╛
╒═══════════════════╕
│ Top 4 of email    │
╞═══════════════════╡
│ 1 occurences      │
├───────────────────┤
│ gbnewby@pglaf.org │
╘═══════════════════╛

Extract the sentences associated to a specific token

python3 ./bin/napkin.py -o readable -f samples/the-prince.txt -t 4 --token-span "Vitelli"

╒═════════════════════════════════════════════════════════════════════════╕
│ Top 4 of span for Vitelli                                               │
╞═════════════════════════════════════════════════════════════════════════╡
│ 1 occurences                                                            │
├─────────────────────────────────────────────────────────────────────────┤
│ This duke entered                                                       │
│ Romagna with auxiliary troops, leading forces composed entirely of      │
│ French soldiers, and with these he took Imola and Forli; but as they    │
│ seemed unsafe, he had recourse to mercenaries, and hired the Orsini and │
│ Vitelli; afterwards finding these uncertain to handle, unfaithful and   │
│ dangerous, he suppressed them, and relied upon his own men.             │
├─────────────────────────────────────────────────────────────────────────┤
│ The Florentines appointed Paolo Vitelli their captain,                  │
│ a man of great prudence, who had risen from a private station to the    │
│ highest reputation.                                                     │
├─────────────────────────────────────────────────────────────────────────┤
│ Nevertheless, Messer Niccolo Vitelli has been seen in                   │
│ our own time to destroy two fortresses in Città di Castello in order    │
│ to keep that state.                                                     │
├─────────────────────────────────────────────────────────────────────────┤
│ And the                                                                 │
│ difference between these forces can be easily seen if one considers     │
│ the difference between the reputation of the duke when he had only the  │
│ French, when he had the Orsini and Vitelli, and when he had to rely     │
│ on himself and his own soldiers.                                        │
├─────────────────────────────────────────────────────────────────────────┤
│ And that his foundations were                                           │
│ good is seen from the fact that the Romagna waited for him more than a  │
│ month; in Rome, although half dead, he remained secure, and although    │
│ the Baglioni, Vitelli, and Orsini entered Rome they found no followers  │
│ against him.                                                            │
╘═════════════════════════════════════════════════════════════════════════╛

Specify table format

In readable output, the format can be set to any of the tabulate format supported. If you want the top 10 of out-of-vocabulary words from the text in GitHub markdown format.

python3 ./bin/napkin.py -o readable -f samples/the-prince.txt -t 10 --analysis oov --table-format github

Top 10 of oov
9 occurences
Sforza
7 occurences
Fermo
6 occurences
Vitelli
Pertinax
Orsinis
Colonnas
Bentivogli
Agathocles
5 occurences
Oliverotto
Cæsar
Commodus

overview of processing in napkin

what about the name?

The name 'napkin' came after a first sketch of the idea on a napkin. The goal was also to provide a simple text analysis tool which can be run on the corner of table in a kitchen.

LICENSE

napkin is free software under the AGPLv3 license.

Copyright (C) 2020 Alexandre Dulaunoy
Copyright (C) 2020 Pauline Bourmeau

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