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# PROJECT: DEPRESSION-DETECTION-USING-TWEETS
# AUTHORS: AMEY THAKUR & MEGA SATISH
# GITHUB (AMEY): https://github.com/Amey-Thakur
# GITHUB (MEGA): https://github.com/msatmod
# REPOSITORY: https://github.com/Amey-Thakur/DEPRESSION-DETECTION-USING-TWEETS
# RELEASE DATE: June 5, 2022
# LICENSE: MIT License
# DESCRIPTION: Utility for predicting depression levels in tweets using SVM.
# ==============================================================================
import argparse
import pickle
import warnings
import numpy as np
import pandas as pd
import spacy
import en_core_web_lg
import clean_utilities as CU
# Suppression of non-critical runtime warnings to maintain output integrity
warnings.filterwarnings("ignore")
def main():
"""
Main entry point for the prediction utility.
This script encapsulates the end-to-end inference pipeline:
1. Argument Parsing: Captures input text file and model selection.
2. Text Preprocessing: Normalization via clean_utilities.
3. Feature Extraction: Generating centroid embeddings via spaCy.
4. Classification: Binary sentiment analysis via pre-trained SVM.
"""
# Initialize the CLI argument parser with a descriptive header
parser = argparse.ArgumentParser(
description="Twitter Depression Detection: Machine Learning Inference Utility"
)
# Positional argument for the target tweet content (text file)
parser.add_argument(
'filename',
help="Path to the text file containing the tweet for classification"
)
# Positional argument for the classification model type
parser.add_argument(
'model',
help="Target model architecture (currently optimized for 'SVM')"
)
# Execution of the parsing logic
args = parser.parse_args()
# Pipeline validation: Ensuring input availability and model compatibility
if args.filename is not None and args.model == "SVM":
print(f"Loading input source: {args.filename}")
try:
# Step 1: Data Acquisition
with open(args.filename, 'r', encoding='utf-8') as file:
raw_test_tweet = file.read()
print(f"Captured Content: \"{raw_test_tweet}\"")
# Step 2: Linguistic Preprocessing
# Normalizes raw discourse into a tokenizable semantic format
print("Executing linguistic cleaning pipeline...")
cleaned_input = [CU.tweets_cleaner(raw_test_tweet)]
print(f"Normalized Form: {cleaned_input}")
# Step 3: Feature Space Transformation
# Utilizing dense word embeddings (spaCy 'en_core_web_lg' model)
print("Transforming text to 300-dimensional semantic vectors...")
nlp_engine = en_core_web_lg.load()
# Generating the centroid vector representing the tweet's linguistic context
semantic_features = np.array([
np.array([token.vector for token in nlp_engine(s)]).mean(axis=0) * np.ones((300))
for s in cleaned_input
])
# Step 4: Model Artifact Loading
# Loading the serialized SVM classifier from the assets directory
model_artifact_path = "../assets/models/model_svm1.pkl"
with open(model_artifact_path, 'rb') as model_file:
classifier = pickle.load(model_file)
# Step 5: Algorithmic Inference
# The SVM determines the classification boundary for the semantic vector
print("Performing binary classification...")
prediction_bin = classifier.predict(semantic_features)
# Step 6: Result Interpretation and User Communication
is_depressive = prediction_bin[0]
if is_depressive == 1:
print("\n>>> CLASSIFICATION RESULT: The analyzed content exhibits depressive characteristics.")
else:
print("\n>>> CLASSIFICATION RESULT: The analyzed content is classified as non-depressive.")
except FileNotFoundError:
print(f"Error: The input file {args.filename} could not be located.")
except Exception as e:
print(f"An error occurred during the inference process: {e}")
else:
print("Usage Error: Please provide an input file and specify 'SVM' as the target model.")
if __name__ == '__main__':
main()
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