Analysis of Filipino Mood Swings within a Day using Tweets

Rodalyn Balajadia, Vincent Maglambayan, Maria Pulido

2019

Abstract

We infer moods and how they vary with time using a dataset from the social media application Twitter. We used Python text mining techniques to gather all tweets originating from the Philippines within a span of 24 hours. From the dataset of around 130,000 tweets, we gathered the highest-frequency words and filtered out neutral words to come up with words that imply mood levels. We then plotted the density of keyword usage with respect to time, distinguishing between positive and negative moods. Our initial results of positive mood and negative mood trends are consistent with published studies regarding microblogging mood scales. The emergence of Big Data and the Internet of Things has greatly amplifed our ability not only to express ourselves but to understand each other.

Paper Citation

in Harvard Style

Balajadia R., Maglambayan V. and Pulido M. (2019). Analysis of Filipino Mood Swings within a Day using Tweets.In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-369-8, pages 364-369. DOI: 10.5220/0007749803640369

in Bibtex Style

@conference{iotbds19,
author={Rodalyn Balajadia and Vincent Maglambayan and Maria Pulido},
title={Analysis of Filipino Mood Swings within a Day using Tweets},
booktitle={Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2019},
pages={364-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007749803640369},
isbn={978-989-758-369-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Analysis of Filipino Mood Swings within a Day using Tweets
SN - 978-989-758-369-8
AU - Balajadia R.
AU - Maglambayan V.
AU - Pulido M.
PY - 2019
SP - 364
EP - 369
DO - 10.5220/0007749803640369