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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Classification of movie reviews using term frequency-inverse document frequency and optimized machine learning

Muhammad Zaid Naeem1, Furqan Rustam1, Arif Mehmood2

  • 1Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary

This study implemented machine learning models to analyze sentiment in IMDb reviews. Support Vector Machines with TF-IDF features achieved 89.55% accuracy, improved to 92% using TextBlob for sentiment assignment.

Keywords:
Bag of wordsMovies reviewsSentiment classificationSupervised machine learningText analysis

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Science

Background:

  • The Internet Movie Database (IMDb) hosts millions of user reviews, offering rich data for sentiment analysis.
  • Manual analysis of vast review datasets is impractical, necessitating automated sentiment analysis tools.

Purpose of the Study:

  • To implement and evaluate machine learning models for sentiment polarity detection in IMDb user reviews.
  • To identify the optimal combination of feature engineering techniques and classification models for accurate sentiment analysis.

Main Methods:

  • Preprocessing of IMDb reviews to remove noise and redundant information.
  • Application of feature engineering techniques including Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words, Global Vectors for Word Representations (GloVe), and Word2Vec.
  • Implementation and hyperparameter tuning of classification models such as Support Vector Machines (SVM), Naïve Bayes, Random Forest, and Gradient Boosting.
  • Utilizing TextBlob for sentiment assignment to address contradictions in user-assigned labels.

Main Results:

  • Support Vector Machines (SVM) combined with TF-IDF features achieved an accuracy of 89.55%.
  • The use of TextBlob for sentiment assignment improved the overall sentiment classification accuracy to 92%.

Conclusions:

  • Machine learning models, particularly SVM with TF-IDF, are effective for sentiment analysis of online movie reviews.
  • TextBlob's sentiment assignment pre-processing step can significantly enhance the accuracy of sentiment classification models by mitigating label inconsistencies.