TLA: Twitter Linguistic Analysis

TL;DR

TLA is a framework for structured linguistic analysis of Twitter data across multiple languages, enabling sentiment analysis and understanding of social and psychological behaviors.

Contribution

This paper introduces TLA, a comprehensive framework for collecting, labeling, and analyzing multilingual Twitter data for linguistic and sentiment insights.

Findings

Labeled datasets for multiple languages are provided.

Models trained on these datasets can analyze sentiments across linguistic communities.

The framework facilitates understanding social and psychological behaviors through Twitter analysis.

Abstract

Linguistics has been instrumental in developing a deeper understanding of human nature. Words are indispensable to bequeath the thoughts, emotions, and purpose of any human interaction, and critically analyzing these words can elucidate the social and psychological behavior and characteristics of these social animals. Social media has become a platform for human interaction on a large scale and thus gives us scope for collecting and using that data for our study. However, this entire process of collecting, labeling, and analyzing this data iteratively makes the entire procedure cumbersome. To make this entire process easier and structured, we would like to introduce TLA(Twitter Linguistic Analysis). In this paper, we describe TLA and provide a basic understanding of the framework and discuss the process of collecting, labeling, and analyzing data from Twitter for a corpus of languages while providing detailed labeled datasets for all the languages and the models are trained on these datasets. The analysis provided by TLA will also go a long way in understanding the sentiments of different linguistic communities and come up with new and innovative solutions for their problems based on the analysis.