First establish a connection to Exchange Online using the Connect-ExchangeOnline cmdlet. We then retrieve a list of email subject lines using the Get-EXOMailboxMessage cmdlet. Next, we define the Python code that performs sentiment analysis using PyTorch and the Transformers library. The email subject lines are passed to the sentiment analysis model, and the results are stored in the results variable. Run the Python code using the python command and capture the output. The sentiment analysis results are then displayed using the Write-Output cmdlet.

# Install required PowerShell modules if not already installed
if (-not (Get-Module -ListAvailable -Name ExchangeOnlineManagement)) {
    Install-Module -Name ExchangeOnlineManagement -Force -AllowClobber
}

# Import the required modules
Import-Module ExchangeOnlineManagement

# Connect to Exchange Online
Connect-ExchangeOnline -Credential (Get-Credential)

# Retrieve a list of email subject lines
$mails = Get-EXOMailboxMessage -ResultSize 100

# Import the required PyTorch modules (assuming you have Python and PyTorch installed)
$pythonCode = @"
import torch
from transformers import pipeline

# Load the sentiment analysis model
model = pipeline('sentiment-analysis')

# Process email subject lines
subject_lines = [
    " $($mails.Subject -join '" "') "
]

results = model(subject_lines)

# Output the sentiment analysis results
for result in results:
    print(result['label'], result['score'])
"@

# Run the Python code using the Python executable
$output = & python -c $pythonCode

# Output the sentiment analysis results
Write-Output $output

# Disconnect from Exchange Online
Disconnect-ExchangeOnline

You can also classify them into categories.

# Install required PowerShell modules if not already installed
if (-not (Get-Module -ListAvailable -Name ExchangeOnlineManagement)) {
    Install-Module -Name ExchangeOnlineManagement -Force -AllowClobber
}

# Import the required modules
Import-Module ExchangeOnlineManagement

# Connect to Exchange Online
Connect-ExchangeOnline -Credential (Get-Credential)

# Retrieve a list of email subject lines
$mails = Get-EXOMailboxMessage -ResultSize 100

# Import the required PyTorch modules (assuming you have Python and PyTorch installed)
$pythonCode = @"
import torch
from transformers import pipeline

# Load the sentiment analysis model
sentiment_model = pipeline('sentiment-analysis')

# Load the subject classification model
classification_model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
classification_model.eval()

# Process email subject lines
subject_lines = [
    " $($mails.Subject -join '" "') "
]

sentiment_results = sentiment_model(subject_lines)

category_results = []

# Classify subject lines into categories
for subject in subject_lines:
    # Preprocess subject line (e.g., tokenization, normalization)

    # Convert subject line to image representation
    subject_image = convert_subject_to_image(subject)

    # Perform classification using the ResNet model
    output = classification_model(subject_image)

    # Map the output to category labels
    category = map_output_to_category(output)

    category_results.append(category)

# Output the sentiment analysis and category results
for i in range(len(subject_lines)):
    print('Subject:', subject_lines[i])
    print('Sentiment:', sentiment_results[i]['label'], sentiment_results[i]['score'])
    print('Category:', category_results[i])
    print('---')
"@

# Run the Python code using the Python executable
$output = & python -c $pythonCode

# Output the sentiment analysis and category results
Write-Output $output

# Disconnect from Exchange Online
Disconnect-ExchangeOnline