# PerSent (Persian Sentiment Analyzer)
[](README.fa.md)
## Introduction
PerSent is a practical Python library designed for Persian sentiment analysis. The name stands for "Persian Sentiment Analyzer". Currently in its early testing phase, the library provides basic functionality and is available on [PyPI](https://pypi.org/project/PerSent/). Install it using:
```bash
pip install PerSent
```
Current capabilities include:
- Sentiment analysis of opinions/comments
- Emotion analysis of texts (happiness, sadness, anger, surprise, fear, disgust, calmness)
- Analysis of product/service reviews (recommended/not recommended/no idea)
- Both single-text and batch CSV processing
- Output displayed in terminal or saved to CSV with summary statistics
Initial repository that evolved into this library:
[Click here](https://github.com/RezaGooner/Sentiment-Survey-Analyzer)
We welcome user testing and feedback to improve the library. If you encounter bugs or have suggestions, please:
- [Contribute](#contribution)
For installation issues due to dependency conflicts (especially with mingw-w64), consider using online platforms like DeepNote.com.
## Structure
### Comment Analysis Functions
```train(train_csv, test_size=0.2, vector_size=100, window=5)```
| Parameter | Data Type | Default Value | Description | Optional/Required |
|--------------|-----------|---------------|-----------------------------------------------------------------------------|-------------------|
| `train_csv` | str | - | Path to CSV file containing training data with `body` and `recommendation_status` columns | Required |
| `test_size` | float | 0.2 | Proportion of test data (between 0.0 and 1.0) | Optional |
| `vector_size`| int | 100 | Output vector dimension for Word2Vec model (embedding size) | Optional |
| `window` | int | 5 | Context window size for Word2Vec model | Optional |
recommendation_status must be one of:
- no_idea
- recommended
- not_recommended
Null/NaN values are converted to no_idea, affecting model accuracy.
- Returns test accuracy score.
---
```analyzeText(text)```
| Parameter | Data Type | Description | Optional/Required |
|-----------|-----------|--------------------------------------|-------------------|
| `text` | str | The Persian text to be analyzed | Required |
The core function that analyzes a text and returns one of:
"not_recommended", "recommended", or "no_idea".
---
```saveModel()```
```loadModel()```
Model persistence functions. Models are saved in the model directory.
---
```analyzeCSV(input_csv, output_path, summary_path=None, text_column=0)```
| Parameter | Data Type | Default Value | Description | Optional/Required |
|----------------|-----------------|---------------|-----------------------------------------------------------------------------|-------------------|
| `input_csv` | str | - | Path to input CSV file containing comments to analyze | Required |
| `output_path` | str | - | Path where analyzed results CSV will be saved | Required |
| `summary_path` | str or None | None | Optional path to save summary statistics CSV | Optional |
| `text_column` | int or str | 0 | Column index (int) or name (str) containing the text to analyze | Optional |
Batch processes comments from a CSV file. For single-column files, text_column isn't needed. Otherwise specify column name/index (0-based, negative indices supported). Output contains:
1- Original text
2- Recommendation status
Optional summary_path generates statistics:
- Total count
- Recommended count
- Not recommended count
- No idea count
- Model accuracy (not implemented in current version)
Returns a DataFrame and saves results.
---
### Emotion Analysis Functions
```loadLex(csv_file, word_col=0, emotion_col=1, weight_col=2)```
| Parameter | Data Type | Default Value | Description | Optional/Required |
|----------------|-----------------|---------------|-----------------------------------------------------------------------------|-------------------|
| `csv_file` | str | - | Path to CSV lexicon file | Required |
| `word_col` | int or str | 0 | Column index (int) or name (str) containing words | Optional |
| `emotion_col` | int or str | 1 | Column index (int) or name (str) containing emotion labels | Optional |
| `weight_col` | int or str | 2 | Column index (int) or name (str) containing weight values | Optional |
Loads a CSV with three columns:
1- Keywords
2- Emotion (happiness, sadness, anger, fear, disgust, calmness)
3- Emotion weight (defaults to 1 if unspecified, affecting accuracy)
Column indices are optional.
---
```train(train_csv, text_col='text', emotion_col='sentiment', weight_col='weight')```
| Parameter | Data Type | Default Value | Description | Optional/Required |
|----------------|-----------------|---------------|-----------------------------------------------------------------------------|-------------------|
| `train_csv` | str | - | Path to training CSV file | Required |
| `text_col` | str or int | 'text' | Column name/index containing text data | Optional |
| `emotion_col` | str or int | 'emotion' | Column name/index containing emotion labels | Optional |
| `weight_col` | str or int | 'weight' | Column name/index containing weight values | Optional |
Trains the emotion model using a CSV with specified column names (optional).
---
```saveModel(model_name='weighted_sentiment_model')```
| Parameter | Type | Default Value | Description | Optional/Required |
|--------------|-------|-----------------------------|-----------------------------------------------------------------------------|-------------------|
| `model_name` | str | 'weighted_sentiment_model' | Base filename for saving model (without extension) | Optional |
```loadModel(model_name='weighted_sentiment_model')```
| Parameter | Type | Default Value | Description | Optional/Required |
|--------------|-------|-----------------------------|-----------------------------------------------------------------------------|-------------------|
| `model_name` | str | 'weighted_sentiment_model' | Base filename of model to load (without extension) | Optional |
Model persistence functions (saved in model directory).
---
```analyzeText(text)```
| Parameter | Type | Description | Optional/Required |
|-----------|------|--------------------------------------|-------------------|
| `text` | str | Persian text to analyze | Required |
Analyzes a single text, returning percentage scores for each emotion.
---
```analyzeCSV(input_csv, output_csv, text_col='text', output_col='sentiment_analysis')```
| Parameter | Type | Default Value | Description | Optional/Required |
|---------------------|---------------|------------------------|-----------------------------------------------------------------------------|-------------------|
| `input_csv` | str | - | Path to input CSV file containing text to analyze | Required |
| `output_csv` | str | - | Path to save analyzed results | Required |
| `text_col` | str/int | 'text' | Column name/index containing text to analyze | Optional |
| `output_col` | str | 'sentiment_analysis' | Column name for output results | Optional |
Batch processes texts from CSV. Returns True on success. Requires:
- input_csv path
- output_csv path
Optional column names.
---
## Installation
Install via pip:
```bash
pip install PerSent
```
For specific versions:
```bash
pip install PerSent==<VERSION_NUMBER>
```
## Usage
- Comment Analysis
Basic single-text analysis:
```bash
from PerSent import CommentAnalyzer
analyzer = CommentAnalyzer()
'''
Training (if you have data):
Requires CSV with comments and recommendation status columns
Status must be: recommended/not_recommended/no_idea
'''
analyzer.train("train.csv")
# Load pre-trained model
analyzer.loadModel()
# Predict
text = "کیفیت عالی داشت" # "Excellent quality"
result = analyzer.analyzeText(text)
print(f"Sentiment: {result}") # Output: Sentiment: recommended
```
The included pre-trained model has ~70% accuracy. For better results, you can train with larger datasets. I've prepared a split dataset (due to size):
[Download Here](https://github.com/RezaGooner/Sentiment-Survey-Analyzer/tree/main/Dataset/big_train)
---
Batch CSV processing:
```bash
from PerSent import CommentAnalyzer
analyzer = CommentAnalyzer()
analyzer.loadModel()
# Basic usage (single-column CSV)
analyzer.analyzeCSV(
input_csv="comments.csv",
output_path="results.csv"
)
# Alternative usage patterns:
# 1. Using column index (0-based)
analyzer.analyzeCSV("comments.csv", "results.csv", None, 0)
# 2. Negative indices (count from end)
analyzer.analyzeCSV("comments.csv", "results.csv", None, -1)
# 3. Column name
analyzer.analyzeCSV("comments.csv", "results.csv", None, "نظرات") # "Comments" column
# 4. With summary (single-column)
analyzer.analyzeCSV("comments.csv", "results.csv", "summary.csv")
# 5. With summary and column specification
analyzer.analyzeCSV("comments.csv", "results.csv", "summary.csv", 2)
```
- Emotion Analysis
Single text analysis with pre-trained model:
```bash
from PerSent import SentimentAnalyzer
analyzer = SentimentAnalyzer()
analyzer.loadModel()
sample_text = "امتحانم رو خراب کردم. احساس میکنم یک شکست خوردهی تمام عیارم."
# "I failed my exam. I feel like a complete failure."
result = analyzer.analyzeText(sample_text)
for emotion, score in sorted(result.items(), key=lambda x: x[1], reverse=True):
print(f"{emotion}: {score:.2f}%")
```
output :
```bash
غم: 36.00% #Sadness
عصبانیت: 36.00% #anger
ترس: 28.00% #fear
شادی: 0.00% #happiness
تنفر: 0.00% #disgust
شگفتی: 0.00% #surprise
آرامش: 0.00% #calmness
```
To train your own model:
``` bash
analyzer.train('emotion_dataset.csv')
```
Required CSV columns:
1- Keywords
2- Emotion (happiness, sadness, anger, disgust, fear, calmness)
3- Emotion weight
Model persistence:
```bash
analyzer.saveModel("custom_model_name")
analyzer.loadModel("custom_model_name")
```
Batch CSV processing:
```bash
analyzer.analyzeCSV("input.csv", "output.csv")
```
## Contribution
As mentioned, this library needs community collaboration. Please share suggestions, bugs, or feedback via:
- [Fork Repository & Pull Request](https://github.com/RezaGooner/PerSent/fork)
- [Create Issue](https://github.com/RezaGooner/PerSent/issues/new)
- Email: ```RezaAsadiProgrammer@gmail.com```
- Telegram: ```@RezaGooner```
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"description": "# PerSent (Persian Sentiment Analyzer) \r\n\r\n[](README.fa.md)\r\n\r\n\r\n\r\n## Introduction\r\nPerSent is a practical Python library designed for Persian sentiment analysis. The name stands for \"Persian Sentiment Analyzer\". Currently in its early testing phase, the library provides basic functionality and is available on [PyPI](https://pypi.org/project/PerSent/). Install it using:\r\n\r\n```bash\r\npip install PerSent\r\n```\r\n\r\nCurrent capabilities include:\r\n\r\n- Sentiment analysis of opinions/comments\r\n\r\n- Emotion analysis of texts (happiness, sadness, anger, surprise, fear, disgust, calmness)\r\n\r\n- Analysis of product/service reviews (recommended/not recommended/no idea)\r\n\r\n- Both single-text and batch CSV processing\r\n\r\n- Output displayed in terminal or saved to CSV with summary statistics\r\n\r\nInitial repository that evolved into this library:\r\n[Click here](https://github.com/RezaGooner/Sentiment-Survey-Analyzer)\r\n\r\nWe welcome user testing and feedback to improve the library. If you encounter bugs or have suggestions, please:\r\n\r\n- [Contribute](#contribution)\r\n\r\nFor installation issues due to dependency conflicts (especially with mingw-w64), consider using online platforms like DeepNote.com.\r\n\r\n## Structure\r\n### Comment Analysis Functions\r\n\r\n```train(train_csv, test_size=0.2, vector_size=100, window=5)```\r\n\r\n| Parameter | Data Type | Default Value | Description | Optional/Required |\r\n|--------------|-----------|---------------|-----------------------------------------------------------------------------|-------------------|\r\n| `train_csv` | str | - | Path to CSV file containing training data with `body` and `recommendation_status` columns | Required |\r\n| `test_size` | float | 0.2 | Proportion of test data (between 0.0 and 1.0) | Optional |\r\n| `vector_size`| int | 100 | Output vector dimension for Word2Vec model (embedding size) | Optional |\r\n| `window` | int | 5 | Context window size for Word2Vec model | Optional |\r\n\r\nrecommendation_status must be one of:\r\n\r\n- no_idea\r\n\r\n- recommended\r\n\r\n- not_recommended\r\n\r\nNull/NaN values are converted to no_idea, affecting model accuracy.\r\n\r\n- Returns test accuracy score.\r\n\r\n---\r\n\r\n ```analyzeText(text)```\r\n \r\n| Parameter | Data Type | Description | Optional/Required |\r\n|-----------|-----------|--------------------------------------|-------------------|\r\n| `text` | str | The Persian text to be analyzed | Required |\r\n\r\nThe core function that analyzes a text and returns one of:\r\n\"not_recommended\", \"recommended\", or \"no_idea\".\r\n\r\n---\r\n\r\n```saveModel()```\r\n\r\n```loadModel()```\r\n \r\n Model persistence functions. Models are saved in the model directory.\r\n\r\n---\r\n\r\n```analyzeCSV(input_csv, output_path, summary_path=None, text_column=0)```\r\n\r\n| Parameter | Data Type | Default Value | Description | Optional/Required |\r\n|----------------|-----------------|---------------|-----------------------------------------------------------------------------|-------------------|\r\n| `input_csv` | str | - | Path to input CSV file containing comments to analyze | Required |\r\n| `output_path` | str | - | Path where analyzed results CSV will be saved | Required |\r\n| `summary_path` | str or None | None | Optional path to save summary statistics CSV | Optional |\r\n| `text_column` | int or str | 0 | Column index (int) or name (str) containing the text to analyze | Optional |\r\n \r\n\r\nBatch processes comments from a CSV file. For single-column files, text_column isn't needed. Otherwise specify column name/index (0-based, negative indices supported). Output contains:\r\n\r\n1- Original text\r\n\r\n2- Recommendation status\r\nOptional summary_path generates statistics:\r\n\r\n- Total count\r\n\r\n- Recommended count\r\n\r\n- Not recommended count\r\n\r\n- No idea count\r\n\r\n- Model accuracy (not implemented in current version)\r\n\r\nReturns a DataFrame and saves results.\r\n\r\n---\r\n\r\n### Emotion Analysis Functions\r\n\r\n```loadLex(csv_file, word_col=0, emotion_col=1, weight_col=2)```\r\n\r\n| Parameter | Data Type | Default Value | Description | Optional/Required |\r\n|----------------|-----------------|---------------|-----------------------------------------------------------------------------|-------------------|\r\n| `csv_file` | str | - | Path to CSV lexicon file | Required |\r\n| `word_col` | int or str | 0 | Column index (int) or name (str) containing words | Optional |\r\n| `emotion_col` | int or str | 1 | Column index (int) or name (str) containing emotion labels | Optional |\r\n| `weight_col` | int or str | 2 | Column index (int) or name (str) containing weight values | Optional |\r\n\r\nLoads a CSV with three columns:\r\n\r\n1- Keywords\r\n\r\n2- Emotion (happiness, sadness, anger, fear, disgust, calmness)\r\n\r\n3- Emotion weight (defaults to 1 if unspecified, affecting accuracy)\r\n\r\nColumn indices are optional.\r\n\r\n---\r\n\r\n```train(train_csv, text_col='text', emotion_col='sentiment', weight_col='weight')```\r\n\r\n| Parameter | Data Type | Default Value | Description | Optional/Required |\r\n|----------------|-----------------|---------------|-----------------------------------------------------------------------------|-------------------|\r\n| `train_csv` | str | - | Path to training CSV file | Required |\r\n| `text_col` | str or int | 'text' | Column name/index containing text data | Optional |\r\n| `emotion_col` | str or int | 'emotion' | Column name/index containing emotion labels | Optional |\r\n| `weight_col` | str or int | 'weight' | Column name/index containing weight values | Optional |\r\n\r\nTrains the emotion model using a CSV with specified column names (optional).\r\n\r\n---\r\n\r\n```saveModel(model_name='weighted_sentiment_model')```\r\n\r\n| Parameter | Type | Default Value | Description | Optional/Required |\r\n|--------------|-------|-----------------------------|-----------------------------------------------------------------------------|-------------------|\r\n| `model_name` | str | 'weighted_sentiment_model' | Base filename for saving model (without extension) | Optional |\r\n\r\n\r\n```loadModel(model_name='weighted_sentiment_model')```\r\n\r\n| Parameter | Type | Default Value | Description | Optional/Required |\r\n|--------------|-------|-----------------------------|-----------------------------------------------------------------------------|-------------------|\r\n| `model_name` | str | 'weighted_sentiment_model' | Base filename of model to load (without extension) | Optional |\r\n\r\n\r\nModel persistence functions (saved in model directory).\r\n\r\n---\r\n\r\n```analyzeText(text)```\r\n\r\n| Parameter | Type | Description | Optional/Required |\r\n|-----------|------|--------------------------------------|-------------------|\r\n| `text` | str | Persian text to analyze | Required |\r\n\r\nAnalyzes a single text, returning percentage scores for each emotion.\r\n\r\n---\r\n\r\n```analyzeCSV(input_csv, output_csv, text_col='text', output_col='sentiment_analysis')```\r\n\r\n| Parameter | Type | Default Value | Description | Optional/Required |\r\n|---------------------|---------------|------------------------|-----------------------------------------------------------------------------|-------------------|\r\n| `input_csv` | str | - | Path to input CSV file containing text to analyze | Required |\r\n| `output_csv` | str | - | Path to save analyzed results | Required |\r\n| `text_col` | str/int | 'text' | Column name/index containing text to analyze | Optional |\r\n| `output_col` | str | 'sentiment_analysis' | Column name for output results | Optional |\r\n\r\nBatch processes texts from CSV. Returns True on success. Requires:\r\n\r\n- input_csv path\r\n\r\n- output_csv path\r\nOptional column names.\r\n\r\n---\r\n\r\n## Installation\r\nInstall via pip:\r\n\r\n```bash\r\npip install PerSent\r\n```\r\n\r\nFor specific versions:\r\n\r\n```bash\r\npip install PerSent==<VERSION_NUMBER>\r\n```\r\n\r\n## Usage\r\n- Comment Analysis\r\n\r\nBasic single-text analysis:\r\n\r\n```bash\r\nfrom PerSent import CommentAnalyzer\r\n\r\nanalyzer = CommentAnalyzer()\r\n\r\n'''\r\nTraining (if you have data):\r\nRequires CSV with comments and recommendation status columns\r\nStatus must be: recommended/not_recommended/no_idea\r\n'''\r\nanalyzer.train(\"train.csv\")\r\n\r\n# Load pre-trained model\r\nanalyzer.loadModel()\r\n\r\n# Predict\r\ntext = \"\u06a9\u06cc\u0641\u06cc\u062a \u0639\u0627\u0644\u06cc \u062f\u0627\u0634\u062a\" # \"Excellent quality\"\r\nresult = analyzer.analyzeText(text)\r\nprint(f\"Sentiment: {result}\") # Output: Sentiment: recommended\r\n```\r\n\r\nThe included pre-trained model has ~70% accuracy. For better results, you can train with larger datasets. I've prepared a split dataset (due to size):\r\n\r\n[Download Here](https://github.com/RezaGooner/Sentiment-Survey-Analyzer/tree/main/Dataset/big_train)\r\n\r\n---\r\n\r\nBatch CSV processing:\r\n\r\n```bash\r\nfrom PerSent import CommentAnalyzer\r\nanalyzer = CommentAnalyzer()\r\nanalyzer.loadModel()\r\n\r\n# Basic usage (single-column CSV)\r\nanalyzer.analyzeCSV(\r\n input_csv=\"comments.csv\",\r\n output_path=\"results.csv\"\r\n)\r\n\r\n# Alternative usage patterns:\r\n# 1. Using column index (0-based)\r\nanalyzer.analyzeCSV(\"comments.csv\", \"results.csv\", None, 0)\r\n\r\n# 2. Negative indices (count from end)\r\nanalyzer.analyzeCSV(\"comments.csv\", \"results.csv\", None, -1)\r\n\r\n# 3. Column name\r\nanalyzer.analyzeCSV(\"comments.csv\", \"results.csv\", None, \"\u0646\u0638\u0631\u0627\u062a\") # \"Comments\" column\r\n\r\n# 4. With summary (single-column)\r\nanalyzer.analyzeCSV(\"comments.csv\", \"results.csv\", \"summary.csv\")\r\n\r\n# 5. With summary and column specification\r\nanalyzer.analyzeCSV(\"comments.csv\", \"results.csv\", \"summary.csv\", 2)\r\n```\r\n\r\n- Emotion Analysis\r\n\r\nSingle text analysis with pre-trained model:\r\n\r\n```bash\r\nfrom PerSent import SentimentAnalyzer\r\n\r\nanalyzer = SentimentAnalyzer()\r\nanalyzer.loadModel()\r\n\r\nsample_text = \"\u0627\u0645\u062a\u062d\u0627\u0646\u0645 \u0631\u0648 \u062e\u0631\u0627\u0628 \u06a9\u0631\u062f\u0645. \u0627\u062d\u0633\u0627\u0633 \u0645\u06cc\u200c\u06a9\u0646\u0645 \u06cc\u06a9 \u0634\u06a9\u0633\u062a \u062e\u0648\u0631\u062f\u0647\u200c\u06cc \u062a\u0645\u0627\u0645 \u0639\u06cc\u0627\u0631\u0645.\"\r\n# \"I failed my exam. I feel like a complete failure.\"\r\n\r\nresult = analyzer.analyzeText(sample_text)\r\nfor emotion, score in sorted(result.items(), key=lambda x: x[1], reverse=True):\r\n print(f\"{emotion}: {score:.2f}%\")\r\n```\r\n\r\noutput :\r\n\r\n```bash\r\n\u063a\u0645: 36.00% #Sadness\r\n\u0639\u0635\u0628\u0627\u0646\u06cc\u062a: 36.00% #anger\r\n\u062a\u0631\u0633: 28.00% #fear\r\n\u0634\u0627\u062f\u06cc: 0.00% #happiness\r\n\u062a\u0646\u0641\u0631: 0.00% #disgust\r\n\u0634\u06af\u0641\u062a\u06cc: 0.00% #surprise\r\n\u0622\u0631\u0627\u0645\u0634: 0.00% #calmness\r\n```\r\n\r\nTo train your own model:\r\n\r\n``` bash\r\nanalyzer.train('emotion_dataset.csv')\r\n```\r\n\r\nRequired CSV columns:\r\n\r\n1- Keywords\r\n\r\n2- Emotion (happiness, sadness, anger, disgust, fear, calmness)\r\n\r\n3- Emotion weight\r\n\r\nModel persistence:\r\n\r\n```bash\r\nanalyzer.saveModel(\"custom_model_name\")\r\nanalyzer.loadModel(\"custom_model_name\")\r\n```\r\n\r\nBatch CSV processing:\r\n\r\n```bash\r\nanalyzer.analyzeCSV(\"input.csv\", \"output.csv\")\r\n```\r\n\r\n## Contribution\r\nAs mentioned, this library needs community collaboration. Please share suggestions, bugs, or feedback via:\r\n\r\n- [Fork Repository & Pull Request](https://github.com/RezaGooner/PerSent/fork)\r\n\r\n- [Create Issue](https://github.com/RezaGooner/PerSent/issues/new)\r\n\r\n- Email: ```RezaAsadiProgrammer@gmail.com```\r\n\r\n- Telegram: ```@RezaGooner```\r\n",
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JSON
