Note: This repo (capstone-35) is used primarily to harvest GULF Arabic from twitter and to conduct basic EDA on the data. For the relevant EG Arabic twitter streams and users, please refer to the capstone-34 repo. Additionally, to conduct topic modeling and classification on a corpus containing both dialects, please refer to capstone-52.

Twitter Dialect Datasets and Classifiers (Arabic)

A project to harvest corpora for Egyptian Arabic and Gulf Arabic from Twitter, conduct descriptive analyses of the resulting corpora, and show that a simple classifier can predict dialect quite effectively.

Getting Started

Clone this repository to your local harddrive: git clone https://github.com/telsahy/capstone-35.git

Prerequisites

Install dependencies from the included requirements.txt file by running either of the following commands:

• !pip install -r requirements.txt
• $ pip install -r requirements.txt

Harvesting Twitter Data and Required Infrastructure

Streaming:

• Create list of dialect specific keyword search terms to use for twitter streamers.
• Create Docker file containing tweepy authentication tokens + other modules added to the jupyter scipy docker image to make the code generalized enough to work with different instances.
• Stream prefiltered keywords list for each class (EG, and GULF). Requires a crone job in order to:
  • Collect 1-username, 2-tweet, 3-location.
  • Decode Arabic Unicode characters.
  • Store data as jsonl or json on AWS instance.
  • Automatically restart tweet streams in case of common errors.

Storing:

• Store raw data into Mongo collection (e.g: raw_gulf, with documents being raw_stream and raw_timelines).
• Raw data remains stored on AWS instance.

Infrastructure:

• Two t2.micros with unique oauth to stream two dialects to decrease chances of dialects mixing.
• One t2.large for modeling and more computationally expensive tasks.

Munging/Cleaning/Storing the Data

Instructions on working with resulting datasets using pandas DataFrames are provided within the related Jupyter Notebooks.

Cleaning data:

• Using regex to filter out emojis, links, http, excluded Arabic unicode in many cases. An easier way to clean the data is to import tweet-preprocessor, the twitter preprocessing package provided in the requirements.txt file.
• Check for duplicates before converting document formats.
• Pickle cleaned data into a seperate folder (e.g: gulf_twitter_pickled).

Storing data in MongoDB:

• Storing should be taking place at each stage of the process.
• Build up corpus, store in Mongo collection as two documents for each class, EG and Gulf.
• Store combined documents under a new collection on Mongo.
• Store cleaned data into Mongo collection (e.g: cleaned_gulf, with documents being cleaned_stream and cleaned_timelines).

Basic EDA/Visualization

• Inspect keyword documents for excessive advertisement and remove duplicates.
• Inspect geographic origins of keyword documents to determine the document's utility to the overall collection.
• Identify users who contribute most to the keyword stream and add them to the timelines stage

EDA, Tokenization, and SVD

• Perform EDA, tokenization and SVD on collected data:
  • Check for term co-ocurrences in EG and Gulf documents and add to stopwords list.
  • Subtract co-occurances of terms between dialects from the data before tokenization?
  • Identify dialectically different keywords and include in the twitter streaming pipeline.
  • Identify users with the richest dialectal tweets and add them to timeline streams.
  • Confirm geographic origin of tweets and make term substitutions in stop word list as needed.
  • Continue rinsing and repeating until terms appear mostly in either one or the other documents.
• Repeat same process for user timelines using Twitter's REST API
• Optional: Stanford Arabic Parser (with built-in ATB) to lametize and seg the data. Use Stanford Arabic Word Segmenter concurrently with Parser, before or after?
• Use the three techniques below and explore best results:
  • Tfidf, SVD, latent semantic analysis
  • Okapi best match, SVD, latent semantic analysis
  • Kullback-Leibler Divergence Model, SVD.

Train/Test Estimators on Collected Data (Classes: EG & GULF)

Classifiers:

• Naive Bayes
• Multinomial LR classifiers
• Logistic Regression

Results:

• Perform plotting, confusion matrix, classification report, roc curve, etc.
• Optional: Clustering estimators, DBSCAN, KMeans, Spectral Clustering

Deep Learning - Text Classification with RNN

• Word2Vec
• Word embeddings using Keras or Gensim

Authors

• Tamir ElSahy

Acknowledgments

• Full acknowledgments available in the file titled Building Datasets for Dialect Classifiers using Twitter.pdf contained within this repo.