Reducing the deterioration of sentiment analysis results due to the time impact

Yuliya Rubtsova

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The research identifies and substantiates the problem of quality deterioration in the sentiment classification of text collections identical in composition and characteristics, but staggered over time. It is shown that the quality of sentiment classification can drop up to 15% in terms of the F-measure over a year and a half. This paper presents three different approaches to improving text classification by sentiment in continuously-updated text collections in Russian: using a weighing scheme with linear computational complexity, adding lexicons of emotional vocabulary to the feature space and distributed word representation. All methods are compared, and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described. It is shown that suggested approaches could reduce the deterioration of sentiment classification results for collections staggered over time.

Original languageEnglish
Article number184
Number of pages12
JournalInformation (Switzerland)
Volume9
Issue number8
DOIs
Publication statusPublished - 25 Jul 2018

Keywords

  • Machine learning
  • Sentiment analysis
  • Sentiment classification
  • Social network analysis
  • Text classification
  • sentiment classification
  • sentiment analysis
  • social network analysis
  • text classification
  • machine learning

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