Social Sentiment Analysis for Prediction of Cryptocurrency Prices Using Neuro-Fuzzy Techniques

Birim, Şule; Sönmez, Filiz

doi:10.1007/978-3-031-09176-6_68

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…ANFIS integrates the neural network and fuzzy logic methodologies and utilizes the advantages of both methodologies. ANFIS models for SA have been built for different topics: Movies (Kulkarni et al, 2013), Products (Cosma & Acampora, 2016a), Politics (Mazumder et al, 2018), Food (Sasikala & Sheela, 2020), COVID-19 Tweets (S. S. Mohammed et al, 2022), Cricket (Anbazhagu & Anandan, 2021), Cryptocurrency prices (Birim & Sönmez, 2022), and Twitter data (Erkartal & Yılmaz, 2022) A total of 16 articles were categorized into ANFIS category from our corpus of fuzzy logic-based SA articles. While some of these researches focused on the classification of sentiment, the majority of them have laid down models for both the classification and prediction of the sentiments.…”

Section: Adaptive Neuro-fuzzy Network-based Sentiment Analysismentioning

confidence: 99%

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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Section: Adaptive Neuro-fuzzy Network-based Sentiment Analysismentioning

confidence: 99%

Sentiment analysis using fuzzy logic: A comprehensive literature review

Vashishtha

Gupta

Mittal

2023

WIREs Data Min & Knowl

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“…Another aspect of this research is related to predicting the trend of cryptocurrencies with sentiment analysis. Several articles [17]- [19] have investigated predicting the trend of cryptocurrencies in the short term (a few hours to a day) and in a long time (more than a day). Formally speaking, the problem of forecasting cryptocurrencies or any other stock with sentiment analysis is formulated as follows.…”

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confidence: 99%

Aspect-Based Sentiment Analysis of Twitter Influencers to Predict the Trend of Cryptocurrencies Based on Hybrid Deep Transfer Learning Models

Jahanbin,

Chahooki

2023

IEEE Access

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With the expansion of social networks, sentiment analysis has become one of the hot topics in machine learning. However, in traditional sentiment analysis, the text is considered of a general nature and ignores the different aspects that may exist in the text. This paper presents a hybrid model of transfer deep learning methods for the aspect-oriented sentiment analysis of influencers' tweets to predict the trend of cryptocurrencies. In the first model, different aspects of tweets are extracted using the Concept Latent Dirichlet Allocation (Concept-LDA). Then, by using the pre-trained RoBERTa network and combining it with the Bidirectional Gated Recurrent Unit (BiGRU) deep learning network and attention layer, sentiments of different aspects of tweets are determined. In the following, the price trend of seven cryptocurrencies, Bitcoin, Ethereum, Binance, Ripple, Dogecoin, Cardano, and Solana, is determined using the historical price and the polarity of tweets with BiGRU combined deep neural network and the attention layer. Also, we used the gridsearch method to select dropout hyper-parameters, learning rate, and the number of GRU units, and the Akaike Information Criterion (AIC) criterion confirmed the results of this proposed combination. The results show that the proposed model in the aspect-based sentiment analysis section has been able to achieve 5.94% accuracy and 9.9% improvement in the f1-score on the SemEval 2015 dataset and 2.61% improvement on the SemEval 2016 dataset in f1-score compared to the state-of-arts. Also, the results of predicting the price trend of cryptocurrencies show that the proposed model has correctly recognized the price trend in the next five days in 77% of cases according to the ROC-AUC criterion.INDEX TERMS Aspect Based sentiment analysis, Prediction trend price, Cryptocurrencies, Pre-trained networks, Hybrid deep learning models.

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How are texts analyzed in blockchain research? A systematic literature review

Zhuo,

Irresberger,

Bostandzic

2024

Financ Innov

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This paper provides a systematic literature review of text analysis methodologies used in blockchain-related research to comprehend and synthesize existing studies across disciplines and define future research directions. We summarize the research scope, text data, and methodologies of 124 papers and identify the two most common combinations of these dimensions: (1) papers that focus on specific cryptocurrencies tend to apply sentiment analysis to instant user-generated content or news articles to discover the correlations between public opinion and market behavior, and (2) studies that examine the broad concept of blockchain with text data from documents published by companies tend to apply topic modeling techniques to explore classifications and trends in blockchain development. We discover five major research topics in the academic literature: relationship discovery, cryptocurrency performance prediction, classification and trend, crime and regulation, and perception of blockchain. Based on these findings, we highlight three potential research directions for researchers to select topics and implement suitable methodologies for text analysis.

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Social Sentiment Analysis for Prediction of Cryptocurrency Prices Using Neuro-Fuzzy Techniques

Cited by 3 publications

References 27 publications

Sentiment analysis using fuzzy logic: A comprehensive literature review

Sentiment analysis using fuzzy logic: A comprehensive literature review

Aspect-Based Sentiment Analysis of Twitter Influencers to Predict the Trend of Cryptocurrencies Based on Hybrid Deep Transfer Learning Models

How are texts analyzed in blockchain research? A systematic literature review

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