What Reddit Can Teach Us About Women’s Watch Preferences (Python + NLP Project)

Most “what watch should I buy?” discussions online skew heavily male. A friend wanted to launch a women’s watch, so I helped with a small data analysis.

In this post I’ll walk through a small but complete Python pipeline I built:

• Scrape relevant posts and comments from Reddit with no API keys
• Filter out irrelevant posts (e.g. men asking for themselves)
• Run NLP analysis: sentiment, brands, features, prices, keywords, clustering, topic modeling
• Generate visualizations and CSVs you can explore further

Everything here is powered by standard Python libraries: requests, pandas, nltk, scikit‑learn, and wordcloud.

1. Collecting Reddit data without API keys

We didn’t use the official Reddit API; instead we hit the public JSON endpoints directly using requests.

At the top of reddit_json_scraper.py we define search URLs across multiple subreddits:

Each URL returns a JSON blob; we wrap that in a helper:

Reddit’s listing JSON has a fairly nested structure, so we created extract_post_data to normalize it into a flat dictionary with the fields we actually care about (ID, subreddit, title, body, score, comment count, timestamps, etc.):

def extract_post_data(post_json):
    """
    Extract relevant information from a Reddit post JSON
    """
    try:
        data = post_json['data']

        return {
            'post_id': data.get('id', ''),
            'subreddit': data.get('subreddit', ''),
            'title': data.get('title', ''),
            'text': data.get('selftext', ''),
            'author': data.get('author', ''),
            'score': data.get('score', 0),
            'upvote_ratio': data.get('upvote_ratio', 0),
            'num_comments': data.get('num_comments', 0),
            'created_utc': data.get('created_utc', 0),
            'created_date': datetime.fromtimestamp(data.get('created_utc', 0)).strftime('%Y-%m-%d %H:%M:%S'),
            'url': f"https://reddit.com{data.get('permalink', '')}",
            'post_url': data.get('url', ''),
            'is_video': data.get('is_video', False),
            'over_18': data.get('over_18', False)
        }

The main collection loop simply iterates through all search URLs, fetches JSON, and appends normalized posts into a list:

We also fetch comments for the most “interesting” posts, sorted by engagement (score + num_comments), by hitting each post’s .json endpoint and walking the comment tree.

At the end of main() we save everything to CSV and run a quick text summary (brand and keyword counts, simple price stats).

2. Filtering: keeping posts that are really about women’s watches

Search results are noisy. Some posts mention “women” but are actually men asking for themselves.
filter_posts.py applies a simple but effective regex filter.We flag posts that contain phrases like “as a man” or “for men”:

…but we keep posts that clearly talk about buying for a woman, e.g. “gift for my wife”:

NON_FILTER_PATTERNS=r"(for|gift|buying|getting|choosing|help).*(mum|mom|mother|wife|girlfriend|partner|daughter|sister|woman|female|her|she)"

filter_check combines title and text, applies these patterns, and filtered_posts_csv writes a cleaned filtered_posts.csv. This becomes the starting point for our analysis.

3. Analyzing the conversations with WatchDataAnalyzer

The main analysis lives in watch_analyzer.py as a single class:

• Load the filtered posts and comments
• Combine titles, bodies, and comment text into all_text
• Set up NLTK and VADER sentiment

3.1. Light text cleaning

We remove URLs and normalize whitespace, then build a combined_text column per post:

3.2. Sentiment on posts and comments

Using VADER, we compute a compound score and label each post/comment as positive, neutral, or negative:

self.posts_df['sentiment_scores']=self.posts_df['combined_text'].apply(lambda x: self.sia.polarity_scores(x))
self.posts_df['sentiment_compound']=self.posts_df['sentiment_scores'].apply(lambda x: x['compound'])
self.posts_df['sentiment_label']=self.posts_df['sentiment_compound'].apply(
    lambda x: 'positive' if x>0.05 else ('negative' if x<-0.05 else 'neutral')
)

We do the same for comments and then plot the distribution, saving sentiment_dist.png.

3.3. Brands, price ranges, and features

We look at three practical angles:

• Brand mentions — a curated list from Titan and Seiko to Rolex and Omega, counted across all text.

def extract_brands(self):
    # Common watch brands
    brands = [
        'casio', 'seiko', 'citizen', 'timex', 'fossil', 'orient', 'tissot',
        'michael kors', 'daniel wellington', 'mvmt', 'skagen', 'swatch',
        'rolex', 'omega', 'cartier', 'tag heuer', 'breitling', 'patek philippe',
        'audemars piguet', 'vacheron constantin', 'baume mercier', 'longines',
        'hamilton', 'bulova', 'invicta', 'bering', 'titan', 'fastrack',
        'sonata', 'maxima', 'hmt', 'raymond weil', 'zenith', 'iwc'
    ]
    brand_mentions={}
    # ...

• Price — regexes to capture Indian price patterns with ₹/rs/inr or “rupees”, then bucketed into budget/mid‑range/premium/luxury ranges.

def extract_prices(self):
    # Patterns for price extraction
    patterns = [
        r'(?:₹|rs\.?|inr)\s*(\d+(?:,\d{3})*(?:\.\d+)?)',
        r'(\d+(?:,\d{3})*(?:\.\d+)?)\s*(?:₹|rs\.?|inr)',
        r'(\d+(?:,\d{3})*(?:\.\d+)?)\s*(?:rupees|rupee)',
    ]

    all_prices=[]

    for text in self.all_text:
        for pattern in patterns:
            matches=re.findall(pattern,str(text),re.IGNORECASE)
            for match in matches:
                price_str=match.replace(',','').replace('.','')
                all_prices.append(int(price_str))

    ranges = {
            'Budget (<₹5,000)': sum(1 for p in all_prices if p < 5000),
            'Mid-range (₹5,000-₹20,000)': sum(1 for p in all_prices if 5000 <= p < 20000),
            'Premium (₹20,000-₹1,00,000)': sum(1 for p in all_prices if 20000 <= p < 100000),
            'Luxury (>₹1,00,000)': sum(1 for p in all_prices if p >= 100000)
        }

• Features — categories like size, material, movement, style, strap, and “features” (water resistance, sapphire, chronograph, etc.), each with their own keyword list.

This gives a quick picture of which brands dominate, what price bands people discuss, and which attributes come up most.

3.4. Keywords, clusters, and topics

Using scikit‑learn:

TF‑IDF keywords — we build a TfidfVectorizer over combined_text
and save the top terms to keywords_tfidf.csv.

def extract_keywords(self):
    self.preprocess_all_text()

    vectorizer=TfidfVectorizer(
        max_features=80,
        stop_words='english',
        min_df=2
    )

    texts=self.posts_df['combined_text'].fillna('').tolist()
    X=vectorizer.fit_transform(texts)

    feature_names=vectorizer.get_feature_names_out()

    scores = X.mean(axis=0).A1

    # Create keyword dataframe
    keywords_df = pd.DataFrame({
        'keyword': feature_names,
        'tfidf_score': scores
    }).sort_values('tfidf_score', ascending=False)

• Clustering — we cluster posts into 5 groups using K‑Means over TF‑IDF vectors, then inspect top words per cluster.

def cluster_posts(self, n_clusters=5):
    """
    Cluster posts based on text similarity
    """
    # ...
    vectorizer = TfidfVectorizer(
        max_features=50,
        stop_words='english',
        min_df=2
    )

    texts = self.posts_df['combined_text'].fillna('').tolist()
    X = vectorizer.fit_transform(texts)

    # K-means clustering
    kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
    clusters = kmeans.fit_predict(X)

    self.posts_df['cluster'] = clusters

• Topic modeling — we run LDA/NMF over the same vectors to discover high‑level themes (“budget gifts”, “small wrists and office wear”, “sporty/outdoor”, etc.).

def topic_modeling(self, n_topics=5, method='lda'):
    """
    Perform topic modeling using LDA or NMF
    """
    # ...
    vectorizer = TfidfVectorizer(
        max_features=100,
        stop_words='english',
        ngram_range=(1, 2),
        min_df=2,
        max_df=0.95
    )
    # ...
    if method.lower() == 'lda':
        model = LatentDirichletAllocation(n_components=n_topics, random_state=42)
    else:  # NMF
        model = NMF(n_components=n_topics, random_state=42)
    # ...
    for idx, topic in enumerate(model.components_):
        top_words = [feature_names[i] for i in topic.argsort()[-10:][::-1]]
        print(f"\n   Topic {idx + 1}: {', '.join(top_words)}")

4. Putting it all together

The generate_report() method runs the full pipeline:

• Preprocess text
• Run sentiment, brand/feature/price extraction
• Compute keywords, clusters, and topics
• Generate a word cloud and sentiment plot
• Save everything to CSVs you can open in Excel or a notebook

def generate_report(self):

    # 1. Pre- processing the text 
    self.preprocess_all_text()

    # 2. Analyze sentiment
    sentiment_df=self.analyze_sentiment()

    # 3. Brands mentions
    brands=self.extract_brands()

    # 4. Features
    features = self.extract_features()

    # 5. Prices
    prices = self.extract_prices()

    # 6. Keywords
    keywords_df = self.extract_keywords()

    # 7. Clustering
    clusters = self.cluster_posts(n_clusters=5)

    # 8. Topic Modeling
    print("\n🔍 Running topic modeling (this may take a moment)...")
    topic_model, vectorizer = self.topic_modeling(n_topics=5, method='lda')

    # 9. Visualizations
    print("\n🎨 Creating visualizations...")
    self.create_wordcloud('wordcloud.png')
    self.plot_sentiment_distribution('sentiment_dist.png')

    # Save results ...

It’s a compact example of how to go from raw Reddit JSON to structured insights about a very specific question: what are people really saying when they talk about women’s watches ?