compute-tf-idf-vectors 1.0.3.202208310829

Utility to compute sparse TF-IDF vector representation for dataset in the document_tracking_resources format based on a feature file.

Compute TF-IDF weights for texts

Compute TF-IDF weights for tokens, lemmas, entities, etc. of your datasets from a file that contains features.

Installation

pip install compute_tf_idf_weights

Pre-requisites

Dependencies

This project relies on two other packages: document_tracking_resources and document_processing. This code needs to have access to those packages.

Data

spaCy resources

As this package uses the document_processing package, spaCy models are given in order to process the documents of the corpus. You then need to install spaCy models for your languages.

The following mapping is used, and you should download the models if you want to process those languages.

model_names = {
    "deu": "de_core_news_md",
    "spa": "es_core_news_md",
    "eng": "en_core_web_md",
}

The feature files

This program computes TF-IDF vectors according to an external database of document features. You can get them by looking at the two projects below:

• twitter_tf_idf_dataset: used to create a features document to compute vectors using a database of thousands of Tweets from many press agencies or online newspapers.
• news_tf_idf_dataset: used to create a features document to compute vectors using thousands of scrapped Deutsche Welle articles from which the content is extracted.

Those two project will help you produce the document required by the --features_file option of the compute_tf_idf_weights_of_corpus.py program.

Please, note that all the language of the original corpus must be present in the same file, with a lang column to indicate which feature belong to which language.

For each document text content (either title, text or both) should have at least three features: tokens, entities and lemmas. If the corpus contains only a text feature, the features file will contain for instance tokens_text, lemmas_text, entitites_text.

Below is the header of the file of features:

,tokens_title,lemmas_title,entities_title,tokens_text,lemmas_text,entities_text,lang
[…]

The corpus to process

The script can process two different types of Corpus from document_tracking_resources. The one for News (NewsCorpusWithSparseFeatures), the other one for Tweets (TwitterCorpusWithSparseFeatures). The datafiles should be loaded by document_tracking_resources in order to have this project to work.

For instance, below an example of a TwitterCorpusWithSparseFeatures:

                                         date lang                                text               source  cluster
1218234203361480704 2020-01-17 18:10:42+00:00  eng  Q: What is a novel #coronavirus...      Twitter Web App   100141
1218234642186297346 2020-01-17 18:12:27+00:00  eng  Q: What is a novel #coronavirus...                IFTTT   100141
1219635764536889344 2020-01-21 15:00:00+00:00  eng  A new type of #coronavirus     ...            TweetDeck   100141
...                                       ...  ...                                 ...                  ...      ...
1298960028897079297 2020-08-27 12:26:19+00:00  eng  So you come in here WITHOUT A M...   Twitter for iPhone   100338
1310823421014573056 2020-09-29 06:07:12+00:00  eng  Vitamin and mineral supplements...            TweetDeck   100338
1310862653749952512 2020-09-29 08:43:05+00:00  eng  FACT: Vitamin and mineral suppl...  Twitter for Android   100338

And an example of a NewsCorpusWithSparseFeatures:

                              date lang                     title               text                     source  cluster
24290965 2014-11-02 20:09:00+00:00  spa  Ponta gana la prim   ...  Las encuestas...                    Publico     1433
24289622 2014-11-02 20:24:00+00:00  spa  La cantante Katie Mel...  La cantante b...          La Voz de Galicia      962
24290606 2014-11-02 20:42:00+00:00  spa  Los sondeos dan ganad...  El Tribunal  ...                    RTVE.es     1433
...                            ...  ...                       ...               ...                        ...      ...
47374787 2015-08-27 12:32:00+00:00  deu  Microsoft-Betriebssys...  San Francisco...               Handelsblatt      170
47375011 2015-08-27 12:44:00+00:00  deu  Microsoft-Betriebssy ...  San Francisco...               WiWo Gründer      170
47394969 2015-08-27 20:35:00+00:00  deu  Windows 10: Mehr als ...  In zwei Tagn ...                  gamona.de      170

Command line arguments

Once installed, the command compute_tf_idf_vectors can be used directly, as registered in your PATH.

usage: compute_tf_idf_vectors [-h] --corpus CORPUS --dataset-type {twitter,news} --features-file FEATURES_FILE --output-corpus OUTPUT_CORPUS

Take a document corpus (in pickle format) and perform TF-IDF lookup in order to extract the feature weights.

optional arguments:
  -h, --help            show this help message and exit
  --corpus CORPUS       Path to the pickle file containing the corpus to process.
  --dataset-type {twitter,news}
                        The kind of dataset to process. ‘twitter’ will use the ’TwitterCorpusWithSparseFeatures’ class, the ‘NewsCorpusWithSparseFeatures’ class otherwise
  --features-file FEATURES_FILE
                        Path to the CSV file that contains the learning document features in all languages.
  --output-corpus OUTPUT_CORPUS
                        Path where to export the new corpus with computed TF-IDF vectors.