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AuthorLigthart, Alexander
AuthorCatal, Cagatay
AuthorTekinerdogan, Bedir
Available date2022-11-30T11:23:20Z
Publication Date2021
Publication NameArtificial Intelligence Review
ResourceScopus
Resource2-s2.0-85114194059
URIhttp://dx.doi.org/10.1007/s10462-021-09973-3
URIhttp://hdl.handle.net/10576/36797
AbstractWith advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis. 2021, The Author(s).
Languageen
PublisherSpringer Science and Business Media B.V.
SubjectSentiment analysis; Sentiment classification; Systematic literature review; Tertiary study
TitleSystematic reviews in sentiment analysis: a tertiary study
TypeArticle
Pagination4997-5053
Issue Number7
Volume Number54
dc.accessType Open Access


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