sophannaek / covid19_tweet_sentiment_analysis

Sentiment Analysis of Long-term of Social Data during the COVID-19 Pandemic

Sentiment Analysis of Long-term of Social Data during the COVID-19 Pandemic

Social Network Sentimental Analysis is a directed research project which focus on the sentiment of long-term social media content during the covid-19 pandemic. The project was directed under Prof. Hailu Xu at CSU, Long Beach.

Achievement

This research paper is accepted for publication in the Springer Nature - Research Book Series: Transactions on Computational Science & Computational Intelligence. It is scheduled to be published soon after the 22nd International Conference on Internet Computing & IoT (ICOMP'21), which will take place in July 26-29, 2021, USA.

PAPER ID #: ICM4216

TITLE OF PAPER/ARTICLE: Sentiment Analysis of Long-term Social Data during the COVID-19 Pandemic

CONTRIBUTORS: Sophanna Ek, Marco Curci, Xiaokun Yang, Beiyu Lin, Hailu Xu

Overview

Millions of COVID-19 related tweets have been collected, processed, analyzed, and gender classified from Februrary 2020 to Februrary 2021.

Research Features

• Data collection and processing
• Analysis methods
• Sentiment analysis
• Twitter Gender classification based on tweet content

Current Work -- Streaming Tweet Sentiment Analysis and Spam Detection

The research focused on Tweet batch processing sentimental analysis and spam dectection on covid-19 related tweets in three categories: economy, politics and vaccine related. I've always had a hugh interest on Big Data and streaming processing, but I couldn't be able to work on the streaming processing due to the limited time and resources during the semester. Therefore, I've decided to continue this research on my own.

Tech Stack

Python3, PySpark, Pandas, Numpy, Scikit-learn, Tweepy API, Socket