WASSUP? LOL : Characterizing Out-of-Vocabulary Words in Twitter

TL;DR

This paper investigates the sociolinguistic properties of out-of-vocabulary words in Twitter, proposing a classification model that categorizes these words with over 81% accuracy, highlighting the importance of content features.

Contribution

It introduces a novel classification approach for OOV words in social media, achieving high accuracy and revealing key discriminative features.

Findings

Content features are most discriminative for classification.

Achieved 81.26% accuracy in categorizing OOV words.

OOV words exhibit distinct sociolinguistic properties.

Abstract

Language in social media is mostly driven by new words and spellings that are constantly entering the lexicon thereby polluting it and resulting in high deviation from the formal written version. The primary entities of such language are the out-of-vocabulary (OOV) words. In this paper, we study various sociolinguistic properties of the OOV words and propose a classification model to categorize them into at least six categories. We achieve 81.26% accuracy with high precision and recall. We observe that the content features are the most discriminative ones followed by lexical and context features.