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A Global Optimization Approach to Multi-Polarity Sentiment Analysis

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  • Xinmiao Li
  • Jing Li
  • Yukeng Wu

Abstract

Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From the results of this comparison, we found that PSOGO-Senti is more suitable for improving a difficult multi-polarity sentiment analysis problem.

Suggested Citation

  • Xinmiao Li & Jing Li & Yukeng Wu, 2015. "A Global Optimization Approach to Multi-Polarity Sentiment Analysis," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-18, April.
  • Handle: RePEc:plo:pone00:0124672
    DOI: 10.1371/journal.pone.0124672
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    References listed on IDEAS

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    1. Abdennour Boulesnane & Souham Meshoul & Khaoula Aouissi, 2022. "Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network," Mathematics, MDPI, vol. 10(21), pages 1-22, November.
    2. Ahmed Al-Saffar & Suryanti Awang & Hai Tao & Nazlia Omar & Wafaa Al-Saiagh & Mohammed Al-bared, 2018. "Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-18, April.

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