PLStream: A Framework for Fast Polarity Labelling of Massive Data Streams

Motivation

• When dataset freshness is critical, the annotating of high speed unlabelled data streams becomes critical but remains an open problem.
• We propose PLStream, a novel Apache Flink-based framework for fast polarity labelling of massive data streams, like Twitter tweets or online product reviews.

Environment Requirements

relative python packages are summerized in requirements.txt

1. Python 3.7
2. Java 8
3. Redis server v7.0.0

DataSource

• Dataset quick access in https://course.fast.ai/datasets#nlp

Tweets

• 1.6 million labeled Tweets:
• Source:Sentiment140

Yelp Reviews

• 280,000 training and 19,000 test samples in each polarity
• Source:Yelp Review Polarity

Amazon Reviews

• 1,800,000 training and 200,000 testing samples in each polarity
• Source:Amazon product review polarity

Quick Start

quick try PLStream on yelp review dataset

Data Prepare

cd PLStream
wget https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polarity_csv.tgz
tar zxvf yelp_review_polarity_csv.tgz
mv yelp_review_polarity_csv/train.csv train.csv

1. Install required environment of PLStream

pip install -r requirements.txt

2. Start Redis-Server in a terminal

redis-server

3. Run PLStream

python PLStream.py

• The outputs' form is "original text" + "label" + "@@@@":
• With help of a split("@@@@") function we can further reorganize the labelled dataset.

Optional

to see the labelling accuracy, simply run: python PLStream_acc.py