Scrape blog comments into feedback themes

To scrape blog comments and organize them into feedback themes, you can follow a structured process involving scraping, cleaning, and natural language processing (NLP) to categorize the comments into meaningful themes. Here’s a high-level workflow and sample code using Python:

Step 1: Scrape Blog Comments

Use tools like BeautifulSoup or Scrapy to extract comments from blog pages.

python
CopyEdit
import requests
from bs4 import BeautifulSoup

def scrape_comments(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    
    # Modify selectors as per your blog's structure
    comments = soup.find_all('div', class_='comment-content')
    return [comment.get_text(strip=True) for comment in comments]

Step 2: Clean and Preprocess Comments

Use nltk or spaCy to tokenize and clean the text.

python
CopyEdit
import re
import nltk
from nltk.corpus import stopwords

nltk.download('stopwords')
stop_words = set(stopwords.words('english'))

def clean_comment(comment):
    comment = re.sub(r'[^a-zA-Zs]', '', comment)
    words = comment.lower().split()
    return ' '.join([word for word in words if word not in stop_words])

Step 3: Cluster Comments Into Themes

You can use TF-IDF + KMeans or BERTopic (for better semantic clustering):

Option A: Using TF-IDF + KMeans

python
CopyEdit
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

def cluster_comments(comments, num_clusters=5):
    vectorizer = TfidfVectorizer()
    X = vectorizer.fit_transform(comments)
    model = KMeans(n_clusters=num_clusters, random_state=42)
    model.fit(X)
    clusters = model.predict(X)
    return list(zip(comments, clusters))

Option B: Using BERTopic (Semantic Clustering)

python
CopyEdit
from bertopic import BERTopic

def cluster_with_bertopic(comments):
    topic_model = BERTopic()
    topics, probs = topic_model.fit_transform(comments)
    return topic_model.get_topic_info()

Step 4: Summarize Themes

To generate themes with summaries:

python
CopyEdit
from collections import defaultdict

def summarize_clusters(comment_clusters):
    themes = defaultdict(list)
    for comment, cluster_id in comment_clusters:
        themes[cluster_id].append(comment)
    
    summaries = {}
    for cluster_id, comments in themes.items():
        combined = " ".join(comments)
        summaries[cluster_id] = combined[:300] + '...'  # Simple summarization
    return summaries

Step 5: Output Example

Example structure of results:

json
CopyEdit
{
  "Theme 0": [
    "People love the blog layout and design.",
    "Navigation is intuitive."
  ],
  "Theme 1": [
    "Suggestions for more tutorials.",
    "Request for video guides."
  ],
  ...
}

Notes:

• Use Selenium if the blog loads comments dynamically via JavaScript.

• You can enhance theme labeling using keyword extraction (RAKE, YAKE, or KeyBERT).

• For production use, wrap the entire process in a pipeline or API.

Let me know if you want a working script tailored to a specific blog URL or platform (e.g., WordPress, Blogger).