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توضیحات

A package for processing large corpora and detecting passive voice.

ویژگی	مقدار
سیستم عامل	OS Independent
نام فایل	PassivePy-0.2.3
نام	PassivePy
نسخه کتابخانه	0.2.3
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Mitra Mirshafiee
ایمیل نویسنده	mitra.mirshafiee@gmail.com
آدرس صفحه اصلی	https://github.com/mitramir55/PassivePy
آدرس اینترنتی	https://pypi.org/project/PassivePy/
مجوز	-

# PassivePy: A Tool to Automatically Identify Passive Voice in Big Text Data 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. To achieve these aims, we introduce PassivePy, a fully transparent and documented Python library. For accessing the datasets in our paper, please click on [this link](https://osf.io/j2b6u/?view_only=0e78d7f4028041b693d6b64547b514ca). If you haven't used Python before or need detailed instructions about how to use this package please visit [our website](https://intense-hamlet-85007.herokuapp.com/). First we have to install the requirements in the following way (all requirements are needed for spaCy or other libraries we use.): ``` !pip install -r https://raw.githubusercontent.com/mitramir55/PassivePy/main/PassivePyCode/PassivePySrc/requirements_lg.txt !pip install PassivePy==0.2.20 ``` Then, import PassivePy and initiate the analyzer: ``` from PassivePySrc import PassivePy passivepy = PassivePy.PassivePyAnalyzer(spacy_model = "en_core_web_lg") ``` Use passivepy for single sentences: ``` # Try changing the sentence below: sample_text = "The painting has been drawn." passivepy.passivepy.match_text(sample_text, full_passive=True, truncated_passive=True) ``` The output will be: ``` sentence : the input sentence binary : Whether any passive voice is detected passive_match(es) : The span of passive form in text raw_passive_count : Number of passive voices ``` You can set the full_passive or truncated_passive to true if you want the same sort of output for these two types of passive. (truncated is a passive without an object of preposition, while a full passive is one with the object of preposition - e.g., this was broken by him.) For processing datasets, we have can either analyze the records sentence- or corpus-level. Your dataset can be in any format (e.g., CSV, XLSX or XLS).; however, make sure to that it has at least one column with the text that needs to be analyzed. In case of large datasets, you can also add `batch_size = ...` and `n_process=...` to speed up the analysis (the default for both is 1). ``` # sentence level: df_detected_s = passivepy.match_sentence_level(df, column_name='documents', n_process = 1, batch_size = 1000, add_other_columns=True, truncated_passive=False, full_passive=False) # corpus level df_detected_c = passivepy.match_corpus_level(df, column_name='sentences', n_process = 1, batch_size = 1000, add_other_columns=True, truncated_passive=False, full_passive=False) ``` In the output you will have a data frame with the following columns: ``` # corpus level 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 total number of sentences # Sentence level docId : Initial index of the record in the input file sentenceId : The ith sentence in one specific record sentence : The detected sentence binary : Whether a 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 ``` If you needed to analyze each token of a sentence, i.g., print out the `DEP` (dependency), `POS` (coarse-grained part of speech tags), `TAG` (fine-grained part of speech tags), `LEMMA` (canonical form) of a word, you can use the `parse_sentence` method of passivepy in the following way: ``` sample_sentence = "She has been killed" passivepy.parse_sentence(sample_sentence) ``` And the output will be like the sample below: ``` word: She pos: PRON dependency: nsubjpass tag: PRP lemma: she ... ``` If you do not need any columns to be appended to the main dataset, simply add `add_other_columns = False`, or if you don't what the percentages to show up add `percentage_of_passive_sentences = False` in any of the following functions. Accuracy on the CoLA dataset: 0.97 Accuracy on the CrowdSource Dataset: 0.98 This repository is the code of the following paper, so if you use this package, please cite the work as: Sepehri, A., Markowitz, D. M., & Mir, M. (2022, February 3). PassivePy: A Tool to Automatically Identify Passive Voice in Big Text Data. Retrieved from psyarxiv.com/bwp3t

نیازمندی

مقدار	نام
-	termcolor
-	tqdm
>=3.0.0	spacy

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl PassivePy-0.2.3:

pip install PassivePy-0.2.3.whl

نصب پکیج tar.gz PassivePy-0.2.3:

pip install PassivePy-0.2.3.tar.gz