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An ensemble deep learning classifier for sentiment analysis on code-mix Hindi-English data.

Rahul Pradhan1, Dilip Kumar Sharma1

  • 1GLA University, Mathura, India.

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Summary
This summary is machine-generated.

This study introduces an effective sentiment analysis method for code-mixed social media text. The proposed classifier combines RoBERTa and Universal Sentence Encoder for improved accuracy in understanding multilingual user opinions.

Keywords:
Classifier optimisationCode-mixingData analyticsSASentiment analysisSentiment analysis on indian languagesTransfer learningUniversal sentence encoderWord embeddingXLM-R

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Social Media Analysis

Background:

  • Code-mixing, the use of multiple languages in communication, is prevalent on social media platforms, particularly in multilingual societies like India.
  • Sentiment analysis is crucial for gauging public opinion on various issues, but analyzing code-mixed text presents unique challenges due to linguistic diversity and out-of-vocabulary terms.
  • Existing sentiment analysis models often struggle with the complexities of code-mixed data, necessitating novel approaches.

Purpose of the Study:

  • To develop and evaluate a robust sentiment analysis classifier for code-mixed social media data.
  • To address the challenges posed by code-mixing in natural language processing tasks.
  • To improve the accuracy and efficiency of sentiment detection in multilingual online communication.

Main Methods:

  • Ensembling a multilingual variant of RoBERTa (a transformer-based model) with sentence-level embeddings from Universal Sentence Encoder.
  • Utilizing transfer learning by leveraging the strengths of both models to optimize performance.
  • Conducting experiments on real-world benchmark datasets to assess the classifier's effectiveness.

Main Results:

  • The proposed ensemble classifier achieved higher accuracy, precision, and recall compared to baseline and other deep learning models.
  • Specific accuracies include 66% on Joshi et al. 2016, 60% on SAIL 2017, and 67% on SemEval 2020 Task-9.
  • The ensemble approach demonstrated an average performance improvement of approximately 3% over contemporary baselines on code-mixed datasets.

Conclusions:

  • The developed ensemble classifier effectively handles the complexities of code-mixed text for sentiment analysis.
  • This approach offers a significant improvement in sentiment detection accuracy for multilingual social media data.
  • The findings contribute to advancing the field of natural language processing for under-resourced and complex linguistic scenarios.