https://github.com/mubaris/urban-robot

Reddit bot :computer: which replies to sarcastic comments :trollface: :trollface:
https://github.com/mubaris/urban-robot

bot praw python reddit-bot sarcasm sentiment-analysis svm

Last synced: 4 days ago
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Reddit bot :computer: which replies to sarcastic comments :trollface: :trollface:

• Host: GitHub
• URL: https://github.com/mubaris/urban-robot
• Owner: mubaris
• Created: 2017-09-15T10:52:57.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-12-22T11:23:16.000Z (almost 7 years ago)
• Last Synced: 2024-08-03T01:22:07.211Z (3 months ago)
• Topics: bot, praw, python, reddit-bot, sarcasm, sentiment-analysis, svm
• Language: Jupyter Notebook
• Homepage:
• Size: 21.5 MB
• Stars: 33
• Watchers: 2
• Forks: 15
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-rainmana - mubaris/urban-robot - Reddit bot :computer: which replies to sarcastic comments :trollface: :trollface: (Jupyter Notebook)

README

        
# Urban Robot

![Urban Robot](ub.png)

Reddit bot which replies to sarcastic comments

## Libraries

* numpy, scipy - For Mathematical and Scientific processes

* nltk - NLP Application

* scikit - Model Training and Feature Extraction

* textblob - Sentiment Analysis

* pickle - Pickling Models and Vectorizers

* langdetect - Language Detection of comments

* praw - Reddit Bot

## Features Used

* Sentiment Analysis of full text, equal 2 and 3 parts of text

* n-grams - 1 to 5

* Term Frequency–Inverse Document Frequency(TF-IFD) after stemming, tokenizing and using n-grams of 1 to 5

* Part of Speech Dictionary Vector

* Topic Modeling

## Data Preprocessing

* Removed URLs

* Removed Stopwords

* Removed words with less than 4 tokens

## Model Training and Classification

Using above Features and Preprocessing 4 models are trained,

* Logistic Regression

* Linear SVM

* SVM with Gaussian Kernel

* Random Forest

If a comment is predicted as 'sarcastic' by 3 out 4 models, it is treated as sarcastic.

## Files

* `classifier.py` - Training and Testing Models

* `bot.py` - Reddit Bot

* `cli_bot.py` - A Command Line Interactive Interface for the Reddit Bot

* `main.ipynb` - iPython Notebook led to the final model hypothesis

## Running

1. Register for new Reddit App [here](https://www.reddit.com/prefs/apps/) and fill details (username, password, client id, client secret) under name 'bot1' in `praw.ini`

2. Run `classifier.py` with Python 3(Optional) or use pretrained models

3. Run `bot.py` with Python 3 for the automated Reddit Bot

4. Run `cli_bot.py` with Python 3 for an interactive version of the Reddit Bot.

That's it.

Logs can accessed at `comment.log`

How to fill [praw.ini](https://praw.readthedocs.io/en/v4.0.0/getting_started/configuration/prawini.html)

Final accuracy of models are in `final_accuracy.txt`

## Dataset

Dataset is available in `container`

Downloaded from [here](https://nlds.soe.ucsc.edu/sarcasm1)