Classification of sentiment reviews using n-gram machine learning approach

Abinash Tripathy*, Ankit Agrawal, Santanu Kumar Rath

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

254 Scopus citations


With the ever increasing social networking and online marketing sites, the reviews and blogs obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like classification or clustering to provide a meaningful information for future uses. These reviews and blogs may be classified into different polarity groups such as positive, negative, and neutral in order to extract information from the input dataset. Supervised machine learning methods help to classify these reviews. In this paper, four different machine learning algorithms such as Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM) have been considered for classification of human sentiments. The accuracy of different methods are critically examined in order to access their performance on the basis of parameters such as precision, recall, f-measure, and accuracy.

Original languageEnglish (US)
Pages (from-to)117-126
Number of pages10
JournalExpert Systems with Applications
StatePublished - Sep 15 2016


  • IMDb dataset
  • Maximum Entropy (ME)
  • N-gram
  • Naive Bayes (NB)
  • Sentiment analysis
  • Stochastic Gradient Descent (SGD)
  • Support Vector Machine (SVM)

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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