If you use this package, please cite it as: Sepehri, Amir, David M. Markowitz, and Mitra Mir. 2022. “Passivepy: A Tool to Automatically Identify Passive Voice in Big Text Data.” PsyArXiv. February 3. doi:10.31234/osf.io/bwp3t.
Objective
Our aim with this work is to create a reliable (e.g., passive voice judgments are consistent), valid (e.g., passive voice judgments are accurate), flexible (e.g., texts can be assessed at different units of analysis), replicable (e.g., the approach can be performed by a range of research teams with varying levels of computational expertise), and scalable way (e.g., small and large collections of texts can be analyzed) to capture passive voice from different corpora for social and psychological evaluations of text.Disclaimer: This website is provided solely for the purpose of testing this package. As a result, it can handle a few thousand (up to 10,000) one-line sentences or a few hundred paragraphs in each file.
Single sentence analysis
Corpus-level analysis
| Column Name | Description |
|---|---|
| document | Records in the input data frame |
| binary | Whether a passive was detected in that document |
| passive_match(es) | Parts of the document detected as passive |
| raw_passive_count | Number of passive voices detected in the sentence |
| raw_passive_sents_count | Number of sentences with passive voice |
| raw_sentence_count | Number of sentences detected in the document |
| passive_sents_percentage | Proportion of passive sentences to the total number of sentences |
Sentence-level analysis
| Column Name | Description |
|---|---|
| docId | Initial index of the record in the input file |
| sentenceId | The ith sentence in one specific record |
| sentence | The detected sentence |
| binary | Whether passive was detected in that sentence |
| passive_match(es) | The part of the record detected as passive voice |
| raw_passive_count | Number of passive forms detected in the sentence |
To try this package on your own system please visit
this page. Just keep in
mind that you need to install Python and the required packages on your system.
With this Google Colab environment, you can run the package in an environment like
your system, but without having to install Python or any packages.
(link to notebook)