Create setup_and_run.ps1

Sentiment Analysis/setup_and_run.ps1

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+# PowerShell script to set up and run the Sentiment Analysis project on Windows
+
+Write-Host "Starting the Sentiment Analysis setup and execution process..." -ForegroundColor Green
+
+# Step 1: Check if Python is installed
+if (-not (Get-Command python -ErrorAction SilentlyContinue)) {
+    Write-Host "Python is not installed. Please install Python from https://www.python.org/downloads/ and try again." -ForegroundColor Red
+    exit 1
+}
+Write-Host "Python is installed." -ForegroundColor Green
+
+# Step 2: Create a virtual environment
+Write-Host "Creating a virtual environment..." -ForegroundColor Cyan
+python -m venv sentiment_env
+if (-not (Test-Path "sentiment_env")) {
+    Write-Host "Failed to create virtual environment. Exiting..." -ForegroundColor Red
+    exit 1
+}
+Write-Host "Virtual environment created." -ForegroundColor Green
+
+# Step 3: Activate the virtual environment
+Write-Host "Activating the virtual environment..." -ForegroundColor Cyan
+& .\sentiment_env\Scripts\Activate.ps1
+if (-not $?) {
+    Write-Host "Failed to activate virtual environment. Exiting..." -ForegroundColor Red
+    exit 1
+}
+Write-Host "Virtual environment activated." -ForegroundColor Green
+
+# Step 4: Create requirements.txt and install dependencies
+Write-Host "Installing dependencies..." -ForegroundColor Cyan
+@"
+nltk==3.8.1
+textblob==0.17.1
+transformers==4.34.0
+torch==2.0.1
+pandas==2.0.3
+numpy==1.24.4
+matplotlib==3.8.0
+seaborn==0.12.2
+wordcloud==1.9.2
+"@ > requirements.txt
+pip install -r requirements.txt
+
+# Step 5: Download NLTK data
+Write-Host "Downloading NLTK data..." -ForegroundColor Cyan
+python -c "import nltk; nltk.download('vader_lexicon')"
+Write-Host "NLTK data downloaded." -ForegroundColor Green
+
+# Step 6: Write the Python script
+Write-Host "Writing the Sentiment Analysis Python script..." -ForegroundColor Cyan
+@'
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from wordcloud import WordCloud
+from nltk.sentiment import SentimentIntensityAnalyzer
+from textblob import TextBlob
+from transformers import pipeline
+
+import nltk
+nltk.download("vader_lexicon")
+
+class SentimentAnalysis:
+    def __init__(self, dataset=None):
+        self.dataset = dataset
+        self.nlp_model = None
+
+    def load_dataset(self, file_path=None):
+        if file_path:
+            self.dataset = pd.read_csv(file_path)
+        else:
+            self.dataset = pd.DataFrame({
+                "Text": [
+                    "I love this product! It's amazing.",
+                    "This is the worst thing I've ever bought.",
+                    "It's okay, not the best but not the worst.",
+                    "Absolutely fantastic experience!",
+                    "Terrible, I hate it.",
+                    "The quality is great, but the price is too high.",
+                    "Decent product for the price.",
+                    "I wouldn't recommend it to anyone.",
+                    "It's perfect, just what I needed!",
+                    "Not bad, but it could be better."
+                ]
+            })
+        print("\nDataset Loaded:\n", self.dataset)
+
+    def preprocess_data(self):
+        self.dataset["Text"] = self.dataset["Text"].str.lower()
+        print("\nPreprocessed Data:\n", self.dataset)
+
+    def analyze_with_nltk(self):
+        print("\nSentiment Analysis Using NLTK:")
+        sia = SentimentIntensityAnalyzer()
+        self.dataset["NLTK_Sentiment"] = self.dataset["Text"].apply(lambda x: sia.polarity_scores(x)["compound"])
+        self.dataset["NLTK_Label"] = self.dataset["NLTK_Sentiment"].apply(
+            lambda x: "Positive" if x > 0.05 else ("Negative" if x < -0.05 else "Neutral")
+        )
+        print(self.dataset[["Text", "NLTK_Sentiment", "NLTK_Label"]])
+
+    def analyze_with_textblob(self):
+        print("\nSentiment Analysis Using TextBlob:")
+        self.dataset["TextBlob_Sentiment"] = self.dataset["Text"].apply(lambda x: TextBlob(x).sentiment.polarity)
+        self.dataset["TextBlob_Label"] = self.dataset["TextBlob_Sentiment"].apply(
+            lambda x: "Positive" if x > 0 else ("Negative" if x < 0 else "Neutral")
+        )
+        print(self.dataset[["Text", "TextBlob_Sentiment", "TextBlob_Label"]])
+
+    def analyze_with_huggingface(self):
+        print("\nSentiment Analysis Using Hugging Face Transformers:")
+        self.nlp_model = pipeline("sentiment-analysis")
+        self.dataset["HuggingFace_Sentiment"] = self.dataset["Text"].apply(lambda x: self.nlp_model(x)[0]["label"])
+        print(self.dataset[["Text", "HuggingFace_Sentiment"]])
+
+    def visualize_sentiments(self):
+        plt.figure(figsize=(12, 6))
+        sns.countplot(x="NLTK_Label", data=self.dataset, palette="coolwarm")
+        plt.title("Sentiment Distribution (NLTK)")
+        plt.show()
+
+        plt.figure(figsize=(12, 6))
+        sns.countplot(x="TextBlob_Label", data=self.dataset, palette="viridis")
+        plt.title("Sentiment Distribution (TextBlob)")
+        plt.show()
+
+        wordcloud = WordCloud(width=800, height=400, background_color="white").generate(" ".join(self.dataset["Text"]))
+        plt.figure(figsize=(10, 5))
+        plt.imshow(wordcloud, interpolation="bilinear")
+        plt.axis("off")
+        plt.title("Word Cloud of Sentences")
+        plt.show()
+
+sa = SentimentAnalysis()
+sa.load_dataset()
+sa.preprocess_data()
+sa.analyze_with_nltk()
+sa.analyze_with_textblob()
+sa.analyze_with_huggingface()
+sa.visualize_sentiments()
+'@ > sentiment_analysis.py
+Write-Host "Python script written." -ForegroundColor Green
+
+# Step 7: Run the Python script
+Write-Host "Running the Sentiment Analysis script..." -ForegroundColor Cyan
+python sentiment_analysis.py
+
+# Step 8: Deactivate virtual environment and clean up
+Write-Host "Deactivating virtual environment..." -ForegroundColor Cyan
+.\sentiment_env\Scripts\Deactivate.ps1
+Write-Host "Sentiment Analysis process completed!" -ForegroundColor Green