A Clustering Analysis of Tweet Length and its Relation to Sentiment

TL;DR

This paper investigates how tweet length correlates with sentiment scores by clustering tweets and introduces a new method for deriving sentiment dictionaries from seed words, enhancing sentiment analysis techniques.

Contribution

It presents a novel approach for deriving sentiment score dictionaries from seed words and analyzes the relationship between tweet length and sentiment through clustering.

Findings

Longer tweets tend to have more extreme sentiment scores

Clustered sentiment scores vary significantly with tweet length

New method improves sentiment dictionary derivation

Abstract

Sentiment analysis of Twitter data is performed. The researcher has made the following contributions via this paper: (1) an innovative method for deriving sentiment score dictionaries using an existing sentiment dictionary as seed words is explored, and (2) an analysis of clustered tweet sentiment scores based on tweet length is performed.