Social Media, Topic Modeling and Sentiment Analysis in Municipal Decision Support

TL;DR

This paper introduces a framework that analyzes social media posts to gauge citizen opinions on municipal issues by combining sentiment analysis, topic modeling, and fuzzy logic, demonstrated on tweets from Ostrava.

Contribution

The paper presents a novel framework integrating sentiment polarity, topic identification, and fuzzy number aggregation for social media analysis in municipal decision support.

Findings

Fuzzy numbers effectively represent diverse sentiments.

The framework captures nuanced opinions on municipal topics.

Application on Ostrava tweets demonstrates practical utility.

Abstract

Many cities around the world are aspiring to become. However, smart initiatives often give little weight to the opinions of average citizens. Social media are one of the most important sources of citizen opinions. This paper presents a prototype of a framework for processing social media posts with municipal decision-making in mind. The framework consists of a sequence of three steps: (1) determining the sentiment polarity of each social media post (2) identifying prevalent topics and mapping these topics to individual posts, and (3) aggregating these two pieces of information into a fuzzy number representing the overall sentiment expressed towards each topic. Optionally, the fuzzy number can be reduced into a tuple of two real numbers indicating the "amount" of positive and negative opinion expressed towards each topic. The framework is demonstrated on tweets published from Ostrava, Czechia over a period of about two months. This application illustrates how fuzzy numbers represent sentiment in a richer way and capture the diversity of opinions expressed on social media.