Opinion mining and fuzzy quantification in hotel reviews

Dundar, Betul; Özdemir, Suat; Akay, Diyar

doi:10.1109/isncc.2016.7746066

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Это способ анализа письменной или устной речи, чтобы определить, является ли сказанное благоприятным, неблагоприятным или нейтральным, и в какой степени. В настоящее время применение анализа тональности востребовано в таких областях как: маркетинг, электронная коммерция, фильмы [1], новости [2], политика [3][4][5], отели [6][7][8], рестораны, социальные сети [9] и т.д. Интернет стал неотъемлемой частью современной жизни.…”

Section: Sentiment Analysis Of Text Reviews Using Tone Dictionaries A...unclassified

Sentiment Analysis of Text Reviews Using Tone Dictionaries And Fuzzy Set Cardinality

Gerasimenko¹,

Stetsenko²

2022

IZVESTIYA SFedU. ENGINEERING SCIENCES

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Section: Sentiment Analysis Of Text Reviews Using Tone Dictionaries A...unclassified

Sentiment Analysis of Text Reviews Using Tone Dictionaries And Fuzzy Set Cardinality

Gerasimenko¹,

Stetsenko²

2022

IZVESTIYA SFedU. ENGINEERING SCIENCES

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Sentiment analysis using fuzzy logic: A comprehensive literature review

Vashishtha

Gupta

Mittal

2023

WIREs Data Min & Knowl

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Understanding and comprehending humans' views, beliefs, attitudes, or opinions toward a particular entity is sentiment analysis (SA). Advancements in e‐commerce platforms has led to an abundance of the real‐time and free forms of opinions floating on social media platforms. This real‐world data are imprecise and vague hence fuzzy logic is required to deal with such subjective data. Since opinions can be fuzzy in nature and definitions of opinion words can be elucidated differently; fuzzy logic has witnessed itself as an effective method to capture the expression of opinions. The study presents an elaborate review of the around 170 published research works for SA using fuzzy logic. The primary emphasis is focused on text‐based SA, audio‐based SA, and fusion of text‐audio features‐based SA. This article discusses the various novel ways of classifying fuzzy logic‐based SA research articles, which have not been accomplished by any other review article till date. The article puts forward the importance of SA tasks and identifies how fuzzy logic adds to this importance. Finally, the article outlines a taxonomy for sentiment classification based on the technique‐supervised and unsupervised in the SA models and comprehensively reviews the SA approaches specific to their task. Prominently, this study highlights the suitability of fuzzy‐based SA approaches into five different classes vis‐a‐vis (a) Sentiment Cognition from Words using fuzzy logic, (b) Sentiment Cognition from Phrases using fuzzy logic, (c) Fuzzy‐rule based SA, (d) Neuro‐fuzzy network‐based SA, and (e) Fuzzy Emotion Recognition.This article is categorized under: Algorithmic Development > Text Mining Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining

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Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis

Vashishtha

Susan

2020

2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

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Opinion mining and fuzzy quantification in hotel reviews

Cited by 3 publications

References 30 publications

Sentiment Analysis of Text Reviews Using Tone Dictionaries And Fuzzy Set Cardinality

Sentiment Analysis of Text Reviews Using Tone Dictionaries And Fuzzy Set Cardinality

Sentiment analysis using fuzzy logic: A comprehensive literature review

Fuzzy Interpretation of Word Polarity Scores for Unsupervised Sentiment Analysis

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