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Ensemble Language Models for Multilingual Sentiment Analysis

Md Arid Hasan

TL;DR

This study explores sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset and investigates four pretrained language models and proposed two ensemble language models.

Abstract

The rapid advancement of social media enables us to analyze user opinions. In recent times, sentiment analysis has shown a prominent research gap in understanding human sentiment based on the content shared on social media. Although sentiment analysis for commonly spoken languages has advanced significantly, low-resource languages like Arabic continue to get little research due to resource limitations. In this study, we explore sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset. Moreover, We investigated four pretrained language models and proposed two ensemble language models. Our findings include monolingual models exhibiting superior performance and ensemble models outperforming the baseline while the majority voting ensemble outperforms the English language.

Ensemble Language Models for Multilingual Sentiment Analysis

TL;DR

This study explores sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset and investigates four pretrained language models and proposed two ensemble language models.

Abstract

The rapid advancement of social media enables us to analyze user opinions. In recent times, sentiment analysis has shown a prominent research gap in understanding human sentiment based on the content shared on social media. Although sentiment analysis for commonly spoken languages has advanced significantly, low-resource languages like Arabic continue to get little research due to resource limitations. In this study, we explore sentiment analysis on tweet texts from SemEval-17 and the Arabic Sentiment Tweet dataset. Moreover, We investigated four pretrained language models and proposed two ensemble language models. Our findings include monolingual models exhibiting superior performance and ensemble models outperforming the baseline while the majority voting ensemble outperforms the English language.
Paper Structure (21 sections, 2 figures, 1 table)

This paper contains 21 sections, 2 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. BACKGROUND
  3. METHODOLOGY
  4. Data Collection and Preprocessing
  5. Model Selection and Baseline
  6. IMPLEMENTATION DETAILS
  7. EVALUATION
  8. Experimental Setup
  9. First Experiment Setup
  10. Second Experiment Setup
  11. Third Experiment Setup
  12. Evaluation Metrics
  13. Experimental Results
  14. CHALLENGES
  15. INDIVIDUAL CONTRIBUTION
  16. ...and 6 more sections

Figures (2)

  • Figure 1: Proposed ensemble models
  • Figure 2: Detailed implementation workflow diagram of our study.