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Topic analyser

Project description

# Topican - topic analyzer

from the command line:
Identify topics by assuming topics can be identified from Nouns and a "context" word:
- [spaCy]( is used to identify Nouns (including Proper nouns) in the text
- nltk WordNet and spaCy are used to group similar nouns together (WordNet "hyponyms" are checked first; spaCy similarity is used if a hyponym is not found)
- the top context words are then found for each noun
- Output is a list of noun groups and associated context words, in order of frequency
- The output also indicates the nouns that were grouped together

For example, the text "I like python", "I love Python", and "I like C" would be analysed as having 2 topic groups "_python" and "_C":
'_python', 2: [('like', 1), ('love', 1),] {('python', 2), }
'_C', 1: [('like', 1), ] {('C', 1), }

## Meta
Richard Smith –

Distributed under the MIT license. See ``LICENSE`` for more information.


## Installation

Pre-requisites (Linux and Windows):

pip3 install topican

# Install spaCy's large English language model
# ** Warning: this requires approx 1GB of disk space
python3 -m spacy download en_core_web_lg


Notes: Additional pre-requisites for Windows:
- ```install spacy``` will fail if Microsoft Visual C++ is not already installed
([]( may help in this case)
- ```spaCy download en_core_web_lg``` may be unable to create a symbolic link. This can be manually created if required

## Usage
from the command line:
usage: topican_by_nouns_on_csv [-h]
filepath text_col exclude_words
top_n_noun_groups top_n_words max_hyponyms
max_hyponym_depth sim_threshold

positional arguments:
filepath path of CSV file
text_col name of text column in CSV file
exclude_words words to exclude: list of words | True to just ignore
NLTK stop-words | False | None
top_n_noun_groups number of noun groups to find (0 to find all
noun/'synonym' groups)
top_n_words number of associated words to print for each noun group
(0 to print all words)
max_hyponyms maximum number of hyponyms a word may have before it is
ignored - use this to exclude very general words that may
not convey useful information (0 to have no limit on the
number of hyponyms a word may have)
max_hyponym_depth level of hyponym to extract (0 to extract all hyponyms)
sim_threshold spaCy similarity level that words must reach to qualify
as being similar

optional arguments:
-h, --help show this help message and exit

as a function:
nlp, name, free_text_Series, exclude_words, top_n_noun_groups, top_n_words, max_hyponyms, max_hyponym_depth, sim_threshold)
- nlp: spaCy nlp object - this must be initialised with a language model that includes the word vectors
- name: descriptive name for free_text_Series
- free_text_Series: pandas Series of text in which to find the noun groups and associated words
- exclude_words: to ignore certain words, e.g. not so useful 'stop words' or artificial words.
This should take one of the following values:
<nbsp>- True: to ignore NTLK stop-words and their capitalizations
<nbsp>- A list of words to exclude
<nbsp>- False or None otherwise
- top_n_noun_groups: number of noun groups to find (specify 'None' to find all noun/'synonym' groups)
- top_n_words: number of words that are associated with each noun group (specify 'None' for all words)
- max_hyponyms: the maximum number of hyponyms a word may have before it is ignored (this is used to
exclude very general words that may not convey useful information: specify 'None' for no restriction)
- max_hyponym_depth: the level of hyponym to extract (specify 'None' to find all levels)
- sim_threshold: the spaCy similarity level that words must reach to qualify as being a similar word

## Usage examples
from the command line:
topican_by_nouns_on_csv test.csv text_col None 10 0 100 1 0.7

# Some text to test
import pandas as pd
test_df = pd.DataFrame({'Text_col' : ["I love Python", "I really love python", "I like python.", "python", "I like C but I prefer Python", "I don't like C any more", "I don't like python", "I really don't like C"]})

# Download NLTK stop-words if you want them in exclude_words
import nltk'stopwords')

# Load spaCy's large English language model (the large model is required to be able to use similarity)
# ** Warning: this requires approx 1.8GB of RAM
import spacy
nlp = spacy.load('en_core_web_lg')

import topican
topican.print_words_associated_with_common_noun_groups(nlp, "test", test_df['Text_col'], False, 10, None, 100, 1, 0.7)
![alt text](images/readme_usage_output.png "topican usage example")

## Release History

* 0.0.17
* First release to GitHub
* 0.0.18
* Updates to to note Windows install pre-requisites and the need to download wordnet
* 0.0.19
* Add script topican_by_nouns_on_csv to apply print_words_associated_with_common_noun_groups to a text column of a CSV file
* function get_top_word_groups_by_synset_then_similarity: allow max_hyponyms and n_word_groups to be None to indicate no restriction on them
* function print_words_associated_with_common_noun_groups: do not list words that will be excluded
* 0.0.20
* Update to add a topican_by_nouns_on_csv as an entry_point to console_scripts to be able to call that scipt directly
* 0.0.21
* Update to add the packages required for installation
* 0.0.22
* fix main signature and add param to parser.parse_args so that topican_by_nouns_on_csv can be called from the command line; remove nargs='+' type for exclude_words
* 0.0.23
* if exclude_words is True,'stopwords')
* 0.0.24
* in the usage example for the function, download 'stopwords' not 'wordnet'

## Contributing

1. Fork it (<>)
2. Create your feature branch (`git checkout -b feature/fooBar`)
3. Commit your changes (`git commit -am 'Add some fooBar'`)
4. Push to the branch (`git push origin feature/fooBar`)
5. Create a new Pull Request

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