| Name | linguistify JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | https://github.com/Jal-ghamdi/linguistify |
| Summary | A unified toolkit for advanced text processing and linguistic analysis. This package offers a comprehensive set of functions to clean, preprocess, and analyze text data, applying sophisticated linguistic techniques to enhance your text analytics workflow. Whether you're looking to clean raw text or extract detailed linguistic features, Linguistify provides the tools to transform and enrich your text data efficiently. |
| upload_time | 2024-08-15 06:50:36 |
| maintainer | None |
| docs_url | None |
| author | Jal-ghamdi |
| requires_python | <4.0,>=3.7 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# linguistify
A unified toolkit for advanced text processing and linguistic analysis. `linguistify` offers a comprehensive set of functions to clean, preprocess, and analyze text data, applying sophisticated linguistic techniques to enhance your text analytics workflow.
The purpose of the `linguistify` package is to provide tools that simplify and enhance the process of text cleaning and analysis. It aims to:
- **Clean Text Data**: Remove unwanted elements from text, such as punctuation, URLs, and special characters, and normalize text for consistent processing.
- **Feature Extraction**: Extract meaningful features from text data, including text length, stop words, and part-of-speech tags, to support various text analysis tasks.
- **Preprocess Text**: Prepare text data for further analysis or modeling by transforming it into a suitable format.
- **Analyze Text**: Apply linguistic techniques to derive insights from text, such as identifying key terms, sentiment analysis, and more.
## Getting Started
To get started with `linguistify`, you need to install it. The package is available on PyPI and can be installed using `pip`. Open your terminal and run the following command:
### Installation
```bash
pip install linguistify
```
### Usage
Here’s a basic example to get you started:
```python
import pandas as pd
from linguistify.cleaning import clean_text
from linguistify.feature_extraction import add_features_to_dataframe
# Sample text data
data = {
'text': [
"Check out our new product launch at https://example.com! We are excited to share it with you. Follow us @CompanyName #ProductLaunch :)"
]
}
df = pd.DataFrame(data)
# Clean the text
df['cleaned_text'] = df['text'].apply(clean_text)
# Extract features
df_with_features = add_features_to_dataframe(df, 'cleaned_text')
```
#### Examples:
Here is an example of a DataFrame processed by the `linguistify` package:
| text | cleaned_text | length_of_text | num_stop_words | num_digits | num_spaces | num_exclamations | num_questions | num_periods | num_adjectives | num_nouns | num_pronouns | num_verbs | num_adverbs |
|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|----------------|----------------|------------|------------|------------------|---------------|-------------|----------------|-----------|--------------|-----------|-------------|
| Check out our new product launch at https://example.com! We are excited to share it with you. Follow us @CompanyName #ProductLaunch :) | check out our new product launch at url we are excit to share it with you follow us mention companynam hashtag productlaunch happi | 130 | 9 | 0 | 22 | 0 | 0 | 0 | 4 | 6 | 5 | 4 | 0 |
### API
### Contribution
Contributions are welcome! If you notice a bug or have suggestions for improvements, please let us know.
### Author
+ **Main Maintainer**: Jawaher Alghamdi
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"description": "# linguistify\nA unified toolkit for advanced text processing and linguistic analysis. `linguistify` offers a comprehensive set of functions to clean, preprocess, and analyze text data, applying sophisticated linguistic techniques to enhance your text analytics workflow.\n\nThe purpose of the `linguistify` package is to provide tools that simplify and enhance the process of text cleaning and analysis. It aims to:\n\n- **Clean Text Data**: Remove unwanted elements from text, such as punctuation, URLs, and special characters, and normalize text for consistent processing.\n- **Feature Extraction**: Extract meaningful features from text data, including text length, stop words, and part-of-speech tags, to support various text analysis tasks.\n- **Preprocess Text**: Prepare text data for further analysis or modeling by transforming it into a suitable format.\n- **Analyze Text**: Apply linguistic techniques to derive insights from text, such as identifying key terms, sentiment analysis, and more.\n\n## Getting Started\n\nTo get started with `linguistify`, you need to install it. The package is available on PyPI and can be installed using `pip`. Open your terminal and run the following command:\n### Installation\n```bash\npip install linguistify\n```\n### Usage\nHere\u2019s a basic example to get you started:\n```python\nimport pandas as pd\nfrom linguistify.cleaning import clean_text\nfrom linguistify.feature_extraction import add_features_to_dataframe\n\n# Sample text data\ndata = {\n 'text': [\n \"Check out our new product launch at https://example.com! We are excited to share it with you. Follow us @CompanyName #ProductLaunch :)\"\n ]\n}\n\ndf = pd.DataFrame(data)\n\n# Clean the text\ndf['cleaned_text'] = df['text'].apply(clean_text)\n\n# Extract features\ndf_with_features = add_features_to_dataframe(df, 'cleaned_text')\n```\n\n#### Examples:\n\nHere is an example of a DataFrame processed by the `linguistify` package:\n\n| text | cleaned_text | length_of_text | num_stop_words | num_digits | num_spaces | num_exclamations | num_questions | num_periods | num_adjectives | num_nouns | num_pronouns | num_verbs | num_adverbs |\n|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------|----------------|----------------|------------|------------|------------------|---------------|-------------|----------------|-----------|--------------|-----------|-------------|\n| Check out our new product launch at https://example.com! We are excited to share it with you. Follow us @CompanyName #ProductLaunch :) | check out our new product launch at url we are excit to share it with you follow us mention companynam hashtag productlaunch happi | 130 | 9 | 0 | 22 | 0 | 0 | 0 | 4 | 6 | 5 | 4 | 0 |\n\n### API \n\n### Contribution\nContributions are welcome! If you notice a bug or have suggestions for improvements, please let us know. \n\n### Author \n+ **Main Maintainer**: Jawaher Alghamdi",
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