TweetsAnalysis 1.1.3.1

Real Time Tweets Analysis.

Real-Time Tweets Sentiment Analysis Package

Overview

Retrieving real-time tweets using twitter API, Apache Kafka, and Apache Spark Streaming; then, using tensorflow deep learning model to classify the tweets wether they positive, negative, or neutral; all in a pypi package.

TweetsAnalysis

The streamer and model package, available on pypi TweetsAnalysis

Package Requirements

• gensim
• pandas
• pyspark
• kafka-python
• streamlit
• scikit-learn
• seaborn
• tensorflow
• tweepy==3.9.0
• pydantic
• strictyaml
• joblib

Model

The model architecture:

The model results in about 85.5% in the train set and 84.4% accuracy on the test set, which has 160000 tweets; therefore, there is no over-fitting here.

Run

First we need to install the requirements with:

 pip install TweetsAnalysis

To train the model run, but first we need to specifiy the model and data directories in the config file:

python train_model.py

Straming

Start kafka with:

bin/zookeeper-server-start.sh config/zookeeper.properties
bin/kafka-server-start.sh config/server.properties

then create a kafka topic (tweets_stream) with:

bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic tweets_stream