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arabica-1.0.2
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مشاهده بیشترتوضیحات
A Python package for exploratory analysis of text data
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | arabica-1.0.2 |
| نام | arabica |
| نسخه کتابخانه | 1.0.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Petr Koráb |
| ایمیل نویسنده | Petr Korab <xpetrkorab@gmail.com> |
| آدرس صفحه اصلی | https://github.com/PetrKorab/Arabica |
| آدرس اینترنتی | https://pypi.org/project/arabica/ |
| مجوز | - |
# Arabica
**A Python package for exploratory analysis of text data**
Text data is often recorded as a time series with significant variability over time. Some examples of time-series text data include Twitter tweets, product reviews, and newspaper headlines. Arabica provides functions to make the exploratory analysis of such datasets simple.
Arabica provides these methods:
* **arabica_freq**: calculates unigram, bigram, and trigram frequencies over a period (year, month, day)
It can apply all or a selected combination of the following cleaning operations:
* Remove digits from the text
* Remove punctuation from the text
* Remove standard list of stopwords
* Remove an additional specific list of words
`arabica` uses `clean-text` for punctuation cleaning and `nltk` corpus of stopwords.
Arabica works with **texts** of languages based on the Latin alphabet and enables stopword removal for languages in the ntlk corpus of stopwords.
It reads **dates** in standard date and datetime formats (e.g., 2013–12–31, 2013/12/31, 09-Feb-2009, 2013–12–31 11:46:17, 09/02/2009 09:26).
It is preferable to use the US-style dates (MM/DD/YYYY) rather than the European-style date format (DD/MM/YYYY) since there might be a mismatch between months and days in small datasets.
## Installation
Arabica requires [Python 3](https://www.python.org/downloads/),
[NLTK](http://www.nltk.org/install.html),
[clean-text](https://pypi.org/project/cleantext/#description), and
[numpy](https://pypi.org/project/numpy/) to execute. To install using pip, use:
`pip install arabica`
## Usage
* **Import the library**:
``` python
from arabica import arabica_freq
```
* **Choose a method:**
**arabica_freq** returns a dataframe with aggregated unigrams, bigrams, and trigrams frequencies over a period.
To remove stopwords, select aggregation period, and choose a specific set of cleaning operations:
``` python
def arabica_freq(text: str, # Text
time: str, # Time
stopwords: [], # Languages for stop words
skip: [], # Strings to be skipped
punct: bool = False, # Remove all punctuation
lower_case: bool = False, # Make all text lowercase before n-gram calculation
max_words: int ='', # Max number for unigrams, bigrams and trigrams displayed
time_freq: str ='', # Aggregation period: 'Y'/'M'/'D', if no aggregation: 'ungroup'
numbers: bool = False # Remove all digits
)
```
A list of available languages for stopwords is printed with:
``` python
from nltk.corpus import stopwords
print(stopwords.fileids())
```
It is possible to remove more sets of stopwords at once by `stopwords = ['language 1', 'language2','etc..']`
## Examples
### Time-series n-gram analysis
Returns a table with unigram, bigram, and trigram frequencies over a period of time.
``` python
import pandas as pd
from arabica import arabica_freq
```
``` python
data = pd.DataFrame({'text': ['The ordering process was very easy & straight forward. They have great customer service and sorted any issues out very quickly.',
'So far seems to be the wrong product for me :-/ grrrrr...',
'Excellent, service, thank you really, really, really much!!!'],
'time': ['2013-08-8', '2013-09-8','2014-10-8']})
```
``` python
arabica_freq(text = data['text'],
time = data['time'],
time_freq = 'M', # Calculates monthly n-gram frequencies
max_words = 2, # Displays only the first two most frequent unigrams, bigrams, and trigrams
stopwords = ['english'], # Removes English set of stopwords
skip = ['grrrrr'], # Excludes string from n-gram calculation
numbers = True, # Removes numbers
punct = True, # Removes punctuation
lower_case = True) # Makes all text lowercase before n-gram calculation
```
### Descriptive n-gram analysis
Returns unigram, bigram, and trigram frequencies without period aggregation.
``` python
arabica_freq(text = data['text'],
time = data['time'],
time_freq = 'ungroup', # No aggregation made
max_words = 2,
stopwords = ['english'],
skip = ['grrrrr'],
numbers = True,
punct = True
lower_case = True)
```
## Tutorial
For more examples of coding, read these tutorials:
**Text as Time Series: Arabica 1.0.0 Brings New Features for Exploratory Text Data Analysis** [here](https://towardsdatascience.com/text-as-time-series-arabica-1-0-brings-new-features-for-exploratory-text-data-analysis-88eaabb84deb?sk=229ec0602d0b8514f25bce501ed9ecb9)
**Arabica: A Python Package for Exploratory Analysis of Text Data** [here](https://towardsdatascience.com/arabica-a-python-package-for-exploratory-analysis-of-text-data-3bb8d7379bd7?sk=cc91cabb56d44e0f285825d9a666b064)
## License
##### MIT
For any questions, issues, bugs, and suggestions, please visit [here](https://github.com/PetrKorab/arabica/issues).
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| >3.6.1 | nltk |
| >=1.23.1 | numpy |
| - | regex |
| >=1.1.4 | cleantext |
| >=7.26.0 | ipython |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl arabica-1.0.2:
pip install arabica-1.0.2.whl
نصب پکیج tar.gz arabica-1.0.2:
pip install arabica-1.0.2.tar.gz