/Reddit-Web-API-NLP

- Problem Statement

Primary LanguageJupyter Notebook

Project 1: Reddit: Web API & NLP

Table of Contents

Overview

"ADme" is conducting research on how to target subreddit on famous reddit social media with 50 million users efficiently/effectively. They have 3 stage iteration of research which includes building model, based on analysis build survey and last stage create targeted advertisement in subreddit.

Data Analysis has been conducted using following libraries:

  • pandas
  • numpy
  • seaborn
  • matplotlib
  • skylearn
    • BaggingClassifier
    • RandomForestClassifier
    • ExtraTreesClassifier
    • GradientBoostingClassifier
    • AdaBoostClassifier
    • VotingClassifier
    • StackingClassifier
    • confusion_matrix
    • ConfusionMatrixDisplay
    • classification_report
    • RocCurveDisplay
    • accuracy_score
    • Pipelin
    • train_test_split
    • GridSearchCV
    • cross_val_score
    • cross_val_predict
    • make_column_transformer
    • StandardScaler
    • MultinomialNB
    • LogisticRegression
    • KNeighborsClassifier
    • CountVectorizer
    • TfidfVectorizer
  • nltk
    • SentimentIntensityAnalyzer
    • PorterStemmer

Problem-Statement

"ADme" acquired our team to help them with stage 1 process to give recommendations on how to build surveys based on analysis. In order to make any recommendation, we need to correctly classify posts by their subreddit. Create Natural Language Processing to create Classifier models. Create analysis and give suggestions on what type of words need to be used to make surveys that will catch engagement by subreddit users which will lead to gain behavioral data for stage 3.

Datasets

Features Data Types Description
selftext object Post body text
title object Post title
score int64 User upvote to downvote ratio
domain object Subreddit domain
id object User unique identification
author object Username
num_comments int64 Number of comments
url object Url used on a post
created_utc int64 Post posted timestamp
subreddit int64 Subreddit name

Analysis Summary

Overall Data Analysis

Price Distribution Model performance comparison to determine which model should be picked for modeling

Price Distribution Chart shows board games subreddit uses more words compare to wall street bets

Price Distribution Top 15 words used by boardgames subreddit

Price Distribution Top 15 words used by wall street bets subreddit

Price Distribution Combination of top 15 words from both subreddits combined to illustrate the words they share

Price Distribution Emoji use in a post distribution

Price Distribution Sentiment analysis per subreddit

Price Distribution Number of posts distribution by weekday

Highest Engagement User's Analysis

Price Distribution Top 15 words used by boardgames subreddit

Price Distribution Top 15 words used by wall street bets subreddit

Price Distribution Combination of top 15 words from both subreddits combined to illustrate the words they share

Price Distribution Number of posts distribution by weekday

Conclusion & Recommendation

ADme tasked to create models to classify subreddit post based on the subreddit it belongs. The main goal is to make targeted ads to posts that has highest engagement. Using best practices and state of the art NLP we have reached one of the best models we have seen so far.

  • Stage 1:
    • Cleanup & EDA:
      • We have identified from the scrapped dataset during cleanup, many of the posts were missing body text. To tackle this issue, we have combined body and title text to avoid null values and called new entry as subreddit_text.
      • Stripped and cleaned all non alphanumeric values
      • Calculated length and word-count for subreddit_text
      • Counted number of emojis used in a text
    • Preprocessing & Modeling:
      • Based on all the modeling that has been conducted VotingClassifier performed best by injecting best estimators from individual models:
      • MultinomialNB
        • [94/93]
        • [Confidence Interval: 93.32 ± 0.02]
      • LogisticRegression
        • [97/94]
        • [Confidence Interval: 93.94 ± 0.01]
      • RandomForest
      • [83/83]
      • [Confidence Interval: 82.93 ± 0.02]

As the initial stage of configuring model, we have decided to create classification model to correctly identify which post belongs to subreddit in simulated unseen data. Model's accuracy 95.2%, Specificity 92% and Sensitivity 97%. Since models performing well, we are ready to move on to Stage 2 of the process. Our recommendation includes:

  • Use following words to create interactive survey to collect behavioral scoring from both subreddit:
    • ['market', 'reddit', 'year', 'stock', 'twitter', 'loss', 'price', 'get', 'help', 'day', 'youtube', 'discord', 'would', 'wiki', 'go', 'play']
  • Post it on Wednesday or Friday to get possible highest engagement
  • Survey topic ['trade']