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Semantic relational machine learning model for sentiment analysis using cascade feature selection and heterogeneous

Anuradha Yenkikar1, C Narendra Babu1, D Jude Hemanth2

  • 1Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India.

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|October 20, 2022
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Summary
This summary is machine-generated.

This study introduces a Semantic Relational Machine Learning (SRML) model for accurate tweet sentiment analysis using classifier ensembles and optimal features. The proposed Cascaded Feature Selection (CFS) strategy enhances classification accuracy while reducing feature count.

Keywords:
Deep learningEnsemble modelNatural language processingSentiment analysis

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Computational Linguistics

Background:

  • Social media sentiment analysis is crucial for business intelligence but faces challenges like data scarcity and low accuracy.
  • Existing approaches for sentiment classification using classifier ensembles are underexplored in scientific literature.
  • The need for robust and accurate sentiment analysis models is growing with the rise of user-generated content.

Purpose of the Study:

  • To propose a novel Semantic Relational Machine Learning (SRML) model for automated tweet sentiment classification.
  • To leverage classifier ensembles and optimal feature selection for improved sentiment analysis accuracy.
  • To address limitations of existing sentiment analysis methods, including lack of dictionaries and unannotated data.

Main Methods:

  • Developed a Semantic Relational Machine Learning (SRML) model incorporating classifier ensemble and optimal features.
  • Employed a novel Cascaded Feature Selection (CFS) strategy using statistical tests (Wilcoxon rank sum, logistic regression, cross-correlation).
  • Utilized word2vec (continuous bag-of-words, n-grams) and SentiWordNet for feature extraction and selection.

Main Results:

  • The CFS strategy achieved higher classification accuracy with up to 50% fewer features compared to count vectorizers.
  • The Best Trained Ensemble (BTE) strategy outperformed individual classifiers and existing state-of-the-art models.
  • The SRML model demonstrated superior performance against transformer-based methods (BERT, BERTweet, RoBERTa) and other ensemble techniques.

Conclusions:

  • The proposed SRML model with CFS and ensemble methods offers a robust and accurate solution for tweet sentiment analysis.
  • The research provides insights for developing generic and adaptable expert systems for sentiment analysis across industries.
  • The study highlights the effectiveness of combining advanced feature selection with heterogeneous classifier ensembles for superior performance.