Summary2

 To summarize the comments (worknotes) for each RTSK and assignee using Python, you can use Natural Language Processing (NLP) techniques. The key steps in the process would include cleaning the text data, grouping by assignees and tickets, and then generating summaries for each combination. You can use models like BART, T5, or simple techniques such as TF-IDF with clustering. Here’s a high-level breakdown of how to approach this.

Steps to Summarize Comments for Each RTSK Using Python

1. Loading and Preparing the Dataset

First, load the dataset and preprocess the worknotes (comments). You’ll need to clean the text data by removing unnecessary characters, handling missing values, and grouping by RTSK and assignee.

python
import pandas as pd

# Load the dataset
df = pd.read_csv('servicenow_tickets.csv')

# Example of relevant columns
# 'RTSK Number', 'RTSK State', 'RTSK Assigned to', 'Worknotes'

# Fill missing worknotes with empty strings (if any)
df['Worknotes'] = df['Worknotes'].fillna('')

# Group comments by Assignee and RTSK Number
grouped_df = df.groupby(['RTSK Assigned to', 'RTSK Number'])['Worknotes'].apply(lambda x: ' '.join(x)).reset_index()

2. Cleaning the Worknotes (Text Preprocessing)

Clean the text data to remove special characters, stopwords, and perform other preprocessing steps. You can use nltk or spaCy for this.

python
import re
import spacy

# Load spacy language model
nlp = spacy.load("en_core_web_sm")

# Function to clean text
def clean_text(text):
    # Remove special characters and numbers
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    # Lowercase the text
    text = text.lower()
    # Remove stopwords and lemmatize
    doc = nlp(text)
    text = ' '.join([token.lemma_ for token in doc if not token.is_stop])
    return text

# Apply the cleaning function
grouped_df['Cleaned_Worknotes'] = grouped_df['Worknotes'].apply(clean_text)

3. Summarizing the Worknotes

You can use pre-trained models like BART or T5 from Hugging Face to summarize the worknotes. Here, we’ll use the transformers library to generate summaries.

bash
# Install transformers if you haven't already
!pip install transformers

python
from transformers import pipeline

# Load pre-trained model for summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

# Function to summarize worknotes
def summarize_text(text):
    # Generate a summary if the text is long enough
    if len(text.split()) > 20:  # Arbitrary length cutoff
        summary = summarizer(text, max_length=50, min_length=10, do_sample=False)[0]['summary_text']
    else:
        summary = text  # For very short comments, just return the original
    return summary

# Apply summarization to the cleaned worknotes
grouped_df['Summary_Worknotes'] = grouped_df['Cleaned_Worknotes'].apply(summarize_text)

4. Evaluating and Saving the Results

You now have summarized worknotes for each RTSK Number and assignee. You can save the summarized dataset to a file for further analysis or visualization.

python
# Save the summarized dataset to a new CSV
grouped_df.to_csv('summarized_worknotes.csv', index=False)

5. Further Machine Learning Analysis

After summarizing the comments, you can explore various ML models based on these summaries:

• Classification/Clustering: Categorize worknotes into different ticket resolution categories.
• Sentiment Analysis: Understand the sentiment behind the comments.
• Topic Modeling: Discover common themes across different worknotes using methods like LDA (Latent Dirichlet Allocation).

Example: Sentiment Analysis

python
from transformers import pipeline

# Load sentiment analysis pipeline
sentiment_analyzer = pipeline('sentiment-analysis')

# Apply sentiment analysis to the summarized worknotes
grouped_df['Sentiment'] = grouped_df['Summary_Worknotes'].apply(lambda x: sentiment_analyzer(x)[0]['label'])

# Check the resulting DataFrame
print(grouped_df[['RTSK Assigned to', 'RTSK Number', 'Summary_Worknotes', 'Sentiment']])

Considerations:

• Summarization Models: BART and T5 are state-of-the-art models for summarization. You could also explore GPT-based models for better summaries.
• Text Preprocessing: Ensure the text is cleaned thoroughly, as raw text may introduce noise into the summarization process.
• Scaling: Depending on the dataset size, summarization could be computationally intensive. You may need to parallelize the summarization process for large datasets.

This process will give you a summarized understanding of worknotes, which can then be visualized or further analyzed for insights into how different assignees are managing tickets, their comments, and productivity.

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Preprocess the Text You've already written the preprocessing function, but there are a few corrections needed:

python
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Ensure necessary resources are downloaded (if not already done)
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

# Preprocess text function
def preprocess_text(text):
    if isinstance(text, str):
        tokens = word_tokenize(text.lower())  # Tokenize text
        tokens = [t for t in tokens if t.isalpha()]  # Remove non-alphabetic tokens
        tokens = [t for t in tokens if t not in stopwords.words('english')]  # Remove stopwords
        lemmatizer = WordNetLemmatizer()
        tokens = [lemmatizer.lemmatize(t) for t in tokens]  # Lemmatize tokens
        return " ".join(tokens)  # Return the preprocessed tokens as a string
    else:
        return ""

# Apply the preprocessing to the RTSK Worknote column
df['RTSK Worknote'] = df['RTSK Worknote'].apply(preprocess_text)

Summarizing the Comments Once the comments are preprocessed, you can choose between two common summarization techniques:

• Extractive Summarization: Summarizes by selecting key sentences from the original text.
• Abstractive Summarization: Summarizes by generating new sentences based on the text's meaning (more advanced).

You can use Python libraries like Gensim for extractive summarization. Here’s how to summarize each RTSK Worknote using Gensim:

Install Gensim if you haven't already:

bash
pip install gensim

Then, you can apply extractive summarization:

python
from gensim.summarization import summarize

# Function to generate summary
def summarize_text(text):
    try:
        summary = summarize(text, word_count=50)  # Limit the summary to 50 words (you can adjust this)
        return summary
    except ValueError:  # In case the text is too short to summarize
        return text

# Apply summarization
df['RTSK Worknote Summary'] = df['RTSK Worknote'].apply(summarize_text)

This will create a new column RTSK Worknote Summary with summarized comments.

Grouping by RTSK Number If you want to group comments by RTSK Number and then summarize all worknotes associated with each RTSK number, you can follow this step:

python
# Group by RTSK Number and concatenate the worknotes into one large text block per RTSK
grouped_worknotes = df.groupby('RTSK Number')['RTSK Worknote'].apply(lambda x: ' '.join(x))

# Apply summarization to the grouped worknotes
grouped_worknotes_summary = grouped_worknotes.apply(summarize_text)

# If you want to add this back to the dataframe
summary_df = pd.DataFrame(grouped_worknotes_summary).reset_index()
df = pd.merge(df, summary_df, on='RTSK Number', how='left')