Find and Validate SaaS Ideas

The big idea: Find consumer pain points from reddit to validate or invalidate your idea.

1. Data Collection with Apify Actor

• Use the Apify Actor harshmaur/reddit-scraper-pro to scrape posts and comments from target subreddits. This actor allows you to specify subreddits, post limits, and comment depth.
• Steps:
• Sign up for an Apify account and get your API token from the Apify Console.
• Configure the actor with parameters like subreddit name, number of posts, and comment scraping options.
• Run the actor via Apify’s API or console and retrieve the output (JSON format).
• Example Python code to run the actor and fetch data:
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import requests
import pandas as pd

# Apify API setup
API_TOKEN = 'YOUR_APIFY_API_TOKEN'
ACTOR_ID = 'harshmaur/reddit-scraper-pro'
url = f'<https://api.apify.com/v2/acts/{ACTOR_ID}/run-sync-get-dataset-items>'

# Actor input configuration
input_config = {
    'subreddits': ['target_subreddit'],  # Replace with your subreddit
    'maxPosts': 1000,  # Adjust as needed
    'maxComments': 100,  # Max comments per post
    'sort': 'top',  # Options: hot, new, top, etc.
    'time': 'all'  # Options: hour, day, week, month, year, all
}

# Run actor and get data
response = requests.post(
    url,
    json=input_config,
    headers={'Authorization': f'Bearer {API_TOKEN}'}
)
data = response.json()

# Convert to DataFrame
posts = []
for item in data:
    posts.append({
        'title': item.get('title', ''),
        'selftext': item.get('selftext', ''),
        'comments': [comment.get('body', '') for comment in item.get('comments', [])]
    })
df = pd.DataFrame(posts)
df.to_csv('reddit_data.csv', index=False)

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# Actor Input
{
    "crawlCommentsPerPost": true,
    "includeNSFW": false,
    "maxCommentsCount": 0,
    "maxCommentsPerPost": 0,
    "maxCommunitiesCount": 0,
    "maxPostsCount": 0,
    "proxy": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ],
        "apifyProxyCountry": "US"
    },
    "searchComments": true,
    "searchCommunities": false,
    "searchPosts": true,
    "searchSort": "new",
    "searchTime": "year",
    "startUrls": [
        {
            "url": "https://www.reddit.com/r/airbnb_hosts/",
            "method": "GET"
        },
        {
            "url": "https://www.reddit.com/r/vrbohosts",
            "method": "GET"
        },
        {
            "url": "https://www.reddit.com/r/AirBnBHosts/",
            "method": "GET"
        },
        {
            "url": "https://www.reddit.com/r/ShortTermRentals/",
            "method": "GET"
        },
        {
            "url": "https://www.reddit.com/r/askhotels/",
            "method": "GET"
        },
        {
            "url": "https://www.reddit.com/r/PropertyManagement/",
            "method": "GET"
        },
        {
            "url": "https://www.reddit.com/r/hostaway/",
            "method": "GET"
        }
    ]
}

• Notes:
• Ensure compliance with Reddit’s terms and Apify’s usage policies.
• The actor’s output includes fields like title, selftext, and comments. Check the actor’s documentation for additional fields (e.g., timestamps, upvotes).
• Adjust maxPosts and maxComments based on your needs and Apify plan limits.

2. Data Preprocessing

• Clean the scraped data as before to prepare it for analysis.
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import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()

def preprocess_text(text):
    tokens = word_tokenize(text.lower())
    tokens = [lemmatizer.lemmatize(token) for token in tokens if token.isalnum() and token not in stop_words]
    return ' '.join(tokens)

df['cleaned_text'] = df['selftext'].apply(preprocess_text) + ' ' + df['comments'].apply(lambda x: ' '.join([preprocess_text(c) for c in x]))

3. Extract Consumer Pain Points

• Sentiment Analysis: Identify negative sentiment to pinpoint pain points.
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
df['sentiment'] = df['cleaned_text'].apply(lambda x: analyzer.polarity_scores(x)['compound'])
negative_texts = df[df['sentiment'] < -0.2]['cleaned_text']  # Negative sentiment threshold

• Topic Modeling: Use LDA to cluster negative texts into pain point topics.
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from gensim import corpora
from gensim.models import LdaModel
texts = [text.split() for text in negative_texts]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary, passes=15)
topics = lda_model.print_topics()
for topic in topics:
    print(topic)

• Keyword Extraction: Extract key phrases with KeyBERT.
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from keybert import KeyBERT
kw_model = KeyBERT()
keywords = negative_texts.apply(lambda x: kw_model.extract_keywords(x, keyphrase_ngram_range=(1, 3), stop_words='english'))
print(keywords)

4. Identify and Analyze "How" Questions

• Detect "How" Questions:
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import re

def is_how_question(text):
    pattern = r'^(?i)\\s*how\\s+(do|can|to|does|did|are|is|was|were|should|would|could)\\b.*\\?$'
    return bool(re.match(pattern, text.strip()))

df['is_how_question'] = df['selftext'].apply(is_how_question) | df['comments'].apply(lambda x: any(is_how_question(c) for c in x))
how_questions = df[df['is_how_question']][['title', 'selftext', 'comments']]

how_comments = []
for index, row in how_questions.iterrows():
    for comment in row['comments']:
        if is_how_question(comment):
            how_comments.append({'post_title': row['title'], 'question': comment})
how_questions_df = pd.DataFrame(how_comments)
how_questions_df['cleaned_question'] = how_questions_df['question'].apply(preprocess_text)

• Extract Meaning/Topic:
• Keyword Extraction:
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how_questions_df['keywords'] = how_questions_df['cleaned_question'].apply(
    lambda x: kw_model.extract_keywords(x, keyphrase_ngram_range=(1, 3), stop_words='english')
)

• Topic Modeling:
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texts = [text.split() for text in how_questions_df['cleaned_question']]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
lda_model = LdaModel(corpus, num_topics=5, id2word=dictionary, passes=15)
topics = lda_model.print_topics()
for topic in topics:
    print(topic)

• Intent Classification (Optional):
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from transformers import pipeline
classifier = pipeline('zero-shot-classification', model='facebook/bart-large-mnli')
candidate_labels = ['troubleshooting', 'seeking advice', 'product inquiry', 'process inquiry']
how_questions_df['intent'] = how_questions_df['question'].apply(
    lambda x: classifier(x, candidate_labels)['labels'][0]
)

5. Summarize and Visualize

• Pain Points Summary:
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pain_points = pd.DataFrame({
    'Pain Point': ['Issue 1', 'Issue 2'],
    'Description': ['From topic modeling', 'From keywords'],
    'Frequency': [100, 50]
})
pain_points.to_csv('pain_points_summary.csv', index=False)

• How Questions Summary:
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how_questions_df[['post_title', 'question', 'keywords', 'intent']].to_csv('how_questions_summary.csv', index=False)

• Visualization (Pain Points):
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{
  "type": "bar",
  "data": {
    "labels": ["Product Issues", "Customer Service", "Pricing", "Delivery", "Other"],
    "datasets": [{
      "label": "Frequency of Pain Points",
      "data": [150, 100, 80, 60, 30],
      "backgroundColor": ["#FF6384", "#36A2EB", "#FFCE56", "#4BC0C0", "#9966FF"],
      "borderColor": ["#FF6384", "#36A2EB", "#FFCE56", "#4BC0C0", "#9966FF"],
      "borderWidth": 1
    }]
  },
  "options": {
    "scales": {
      "y": {
        "beginAtZero": true,
        "title": {
          "display": true,
          "text": "Number of Mentions"
        }
      },
      "x": {
        "title": {
          "display": true,
          "text": "Pain Point Categories"
        }
      }
    },
    "plugins": {
      "legend": {
        "display": false
      },
      "title": {
        "display": true,
        "text": "Consumer Pain Points from Reddit Data"
      }
    }
  }
}

• Visualization (How Questions):
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{
  "type": "pie",
  "data": {
    "labels": ["Troubleshooting", "Seeking Advice", "Product Inquiry", "Process Inquiry"],
    "datasets": [{
      "data": [40, 30, 20, 10],
      "backgroundColor": ["#FF6384", "#36A2EB", "#FFCE56", "#4BC0C0"],
      "borderColor": ["#FF6384", "#36A2EB", "#FFCE56", "#4BC0C0"],
      "borderWidth": 1
    }]
  },
  "options": {
    "plugins": {
      "legend": {
        "position": "right"
      },
      "title": {
        "display": true,
        "text": "Distribution of 'How' Question Intents"
      }
    }
  }
}

6. Cross-Reference Pain Points and How Questions

• Link "how" questions to pain points based on keywords.
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how_questions_df['related_pain_point'] = how_questions_df['keywords'].apply(
    lambda x: next((pp for pp in pain_points['Pain Point'] if any(kw[0] in pp.lower() for kw in x)), 'None')
)

7. Ethical Considerations

• Respect Reddit’s terms and Apify’s usage limits.
• Anonymize user data and avoid storing personally identifiable information.
• Ensure compliance with data privacy regulations.

Tools Needed

• Apify: Account and API token for harshmaur/reddit-scraper-pro.
• Python Libraries: requests, pandas, nltk, gensim, keybert, vaderSentiment, transformers, re.
• Setup: Install libraries via pip install requests pandas nltk gensim keybert vaderSentiment transformers.

Notes

• The Apify Actor simplifies scraping compared to PRAW by handling rate limits and providing a structured JSON output. However, it requires an Apify subscription for large-scale scraping.
• Check the actor’s documentation for additional parameters (e.g., filtering by date or flair).
• Adjust LDA topic counts and regex patterns based on your subreddit’s content.
• If you have a specific subreddit or dataset, share details for a tailored approach.
• I can search X for real-time validation of pain points or "how" questions if you provide a topic.