Conventional and structure based sentiment analysis: a survey

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Abstract

Sentiment Analysis is a strand of Natural Language Processing that deals with the emotional polarity a given piece of text has. To gain this understanding from just a string of words, we must first consider a suitable way to break down the text to further classify what each part means. This can be done in a plethora of ways, which mostly stem from the understanding of a classifier. We believe that there is a large amount of information stored in structure-based features within the text, for instance, where the writer may place negation-terms not, neither and how this affects the overall polarity. Similarly, how words in sentences or sections, remote to the current-analysed section, may affect the polarity of said section. A combination of features from both a conventional and a structure-based understanding may also provide us with a larger accuracy in polarity. Therefore, this paper aims to explain both conventional sentiment analysis methods with structure-based methods as well as their practices, advantages and disadvantages concluding with how sentiment analysis can move forward with the appropriation of hybrid methods (methods involving motifs, practices and understandings) from conventional and structure-based methods, for classification.
Original languageEnglish
Title of host publicationProceedings of the 2020 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)978-1-7281-6926-2
ISBN (Print)978-1-7281-6927-9
DOIs
Publication statusPublished - 28 Sep 2020
EventIEEE World Congress on Computational Intelligence (WCCI) 2020 - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameIEEE IJCNN Proceedings Series
PublisherIEEE
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

ConferenceIEEE World Congress on Computational Intelligence (WCCI) 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20

Keywords

  • machine learning
  • sentiment analysis
  • naive bays
  • support vector machines
  • maimum entropy
  • rhetorical structure theory
  • discourse analysis

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