Sentiment Analysis on Kerala Floods

Dudani, Anmol; Srividya, V.; Sneha, B.; Tripathy, B. K.

doi:10.1007/978-981-15-1286-5_10

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…instant noodle (Fernandes et al , 2019), skincare (Pugsee et al , 2019), movie (Azizan et al , 2019), airline (Dutta Das et al , 2017), exploring the recent issues, e.g. climate change (Dudani et al , 2020; Loureiro and Alló, 2020), politics (Tsaniya et al , 2021), Covid-19 (AlAgha, 2021; Flores-Ruiz et al , 2021; Sharma and Sharma, 2020) and others.…”

Section: Methodsmentioning

confidence: 99%

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Hassan

Hudaefi

Caraka

2021

SEF

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Purpose This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning. Design/methodology/approach An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive. Findings The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon. Research limitations/implications This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency. Practical implications The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies. Social implications Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis. Originality/value This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

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Section: Methodsmentioning

confidence: 99%

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Hassan

Hudaefi

Caraka

2021

SEF

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“…В ряде исследований предлагались различные подходы к вычислению тональности отзывов и рейтингов [14] об отелях. В работе [15] были проанализированы реакция и отношение людей, выраженные в сети интернет во время естественного бедствия (наводнение в Керале) с использованием Наивного Байесовского классификатора. В [16][17] авторы исследовали влияние демонетизации на мировом и индийском финансовом рынках.…”

Section: рис 1 сферы применения анализа тональностиunclassified

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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Sentiment Analysis on Kerala Floods

Cited by 5 publications

References 17 publications

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

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

Sentiment analysis using fuzzy logic: A comprehensive literature review

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