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Analyzing Reddit Trends: A Python Tool for Discovering Popular Words

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Hamody
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3 min read

Introduction

As an engineering student fascinated by the potential of data science to uncover patterns and insights, I developed a Python-based tool that analyzes Reddit data to discover trending topics/words across various subreddits. This tool uses several libraries and frameworks to fetch, process, and visualize data, making it a practical and easy to use tool.

Technical walkthrough:

  1. Data Collection with PRAW:

    • PRAW (Python Reddit API Wrapper) is used to automate the interaction with Reddit's API. It fetches posts from user-specified subreddits, allowing us to analyze up-to-date content without manual intervention.

    • Example usage:

    •     reddit = praw.Reddit(client_id='your_id', client_secret='your_secret', user_agent='app_name')
    subreddit = reddit.subreddit('python')
    posts = [post.title for post in subreddit.hot(limit=100)]

  • Text Preprocessing with NLTK:

    • NLTK (Natural Language Toolkit) helps in preprocessing the text data. This involves removing stopwords—words which are frequent but carry little semantic weight, and could skew the analysis.

    • Lemmatization: Ensures that different forms of the same word are analyzed as a single item, improving the accuracy of the trend detection.

    • Implementation detail:

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

    stop_words = set(stopwords.words('english'))
    tokenizer = word_tokenize
    lemmatizer = WordNetLemmatizer()

    def preprocess_text(text):
        words = tokenizer(text.lower())
        return [lemmatizer.lemmatize(word) for word in words if word.isalpha() and word not in stop_words]

  • Identifying Keywords with TF-IDF:

    • TF-IDF (Term Frequency-Inverse Document Frequency) is a technique from Scikit-learn that measures a word's relevance to a document against its frequency in all documents. This statistical measure helps us focus on words that are important in a specific context rather than generally across all texts.

    • TF-IDF serves two main purposes:

      • Term Frequency: This measures how frequently a term appears in a document. Higher frequency can indicate greater importance.

      • Inverse Document Frequency: This reduces the weight of terms that appear more frequently across multiple documents, helping to diminish the importance of common words.

Using TF-IDF allows us to pinpoint words that are not just common, but uniquely significant to the subreddit's recent posts, providing a clearer picture of what topics are truly trending.

  • How it's applied:
    •    from sklearn.feature_extraction.text import TfidfVectorizer

   def extract_keywords(data):
       vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)
       X = vectorizer.fit_transform(data)
       importance = sorted(zip(vectorizer.get_feature_names_out(), X.toarray().sum(axis=0)), key=lambda x: x[1], reverse=True)
       return [word for word, score in importance[:10]]

Visualization and Interactivity with Matplotlib and Streamlit

  • Matplotlib is used to create bar charts that visualize the keywords and their significance, making the trends easy to understand at a glance.

  • Streamlit transforms the Python script into an interactive web application, where users can select subreddits, define the scope of the posts, and get real-time analysis results. This interaction makes the tool more flexible and user-friendly.

Challenges and Solutions

A significant challenge was managing common but uninformative words like "says" and "now," which could potentially mislead the analysis. The TF-IDF model addresses this by prioritizing words based on their distinctiveness in the context of the subreddit being analyzed, rather than their sheer frequency.

Conclusion

This tool demonstrates the practical application of Python libraries to analyze and visualize data from a major social media platform. It offers insights into trends, making it a handy tool for anyone interested in digital culture analysis.

You can access the tool repo on github:
https://github.com/thisishamody/redditrends