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NStackSenti: Evaluation of a Multi-level Approach for Detecting the Sentiment of Users

  • Md Fahimuzzman SohanEmail author
  • Sheikh Shah Mohammad Motiur Rahman
  • Md Tahsir Ahmed Munna
  • Shaikh Muhammad Allayear
  • Md. Habibur Rahman
  • Md. Mushfiqur Rahman
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 922)

Abstract

Sentiment Detection plays a vital role worldwide to measure the acceptance level of any products, movies or facts in the market. Text vectorization (converting text from human readable to machine readable format) and machine learning algorithms are widely used to detect the sentiment of users. This paper presents and evaluates a multi-level architecture based approach using stacked generalization technique named NStackSenti. The presented approach enables the combination of machine learning algorithms to improve the accuracy of detection. Here, Extremely Randomized Tree (ET), Random Forest (RF), Gradient Boost (GB), ADA Boost (ADA), Decision Tree (DT) are used as base classifiers and XGBoost classifier is used as meta estimator. The NStackSenti is applied on two separate datasets to demonstrate the effectiveness in terms of accuracy. NStackSenti provides better accuracy with trigram than unigram and bigram. It provides 83.7% and 86.24% accuracy on 2000 and 50000 data respectively.

Keywords

Machine learning Sentiment detection N-gram Stacked generalization Ensemble learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Md Fahimuzzman Sohan
    • 1
    Email author
  • Sheikh Shah Mohammad Motiur Rahman
    • 1
  • Md Tahsir Ahmed Munna
    • 2
  • Shaikh Muhammad Allayear
    • 2
  • Md. Habibur Rahman
    • 1
    • 3
  • Md. Mushfiqur Rahman
    • 1
  1. 1.Department of Software EngineeringDaffodil International UniversityDhakaBangladesh
  2. 2.Department of Multimedia and Creative TechnologyDaffodil International UniversityDhakaBangladesh
  3. 3.Department of Information and Communication TechnologyMawlana Bhashani Science and Technology UniversityTangailBangladesh

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