Natural Language Processing (NLP) has significantly advanced the ability to analyse and interpret textual data, playing a crucial role in understanding user sentiments. This study applies cutting-edge NLP techniques to perform sentiment analysis on reviews from Indiaâs top cryptocurrency apps and Twitter data. Given the growing interest in cryptocurrencies, understanding user sentiment is vital for market insights and product improvement. We collected a diverse dataset from the Google Play store and Twitter, encompassing 7,197 reviews and numerous tweets. Utilizing the BERT (Bidirectional Encoder Representations from Transformers) model, known for its deep learning capabilities, we processed and analysed the data. The dataset underwent thorough pre-processing, including tokenization and the removal of irrelevant elements. Our analysis compared the BERT modelâs performance with traditional classifiers such as Naive Bayes and Support Vector Machines (SVM). Findings show that BERT significantly outperformed other models, achieving superior precision, recall, accuracy and F1-scores. These results underscore the effectiveness of advanced NLP models in sentiment analysis, particularly for understanding public sentiment towards cryptocurrency apps in India. Future research will explore sentiment analysis on a broader range of platforms and fine-tuning BERT model parameters to further enhance performance and accuracy.
The price of cryptocurrency can be affected by several factors these years, such as technology, social media, COVID-19, etc. One of the examples of these factors is Elon Maskâs tweets about cryptocurrency, which help to increase cryptocurrency prices. With the spread of the epidemic, people are restricted from meeting in person. Therefore, more and more people are active on online social media sites such as Twitter. This research wants to determine if tweets related to cryptocurrency (Bitcoin, one of the most popular cryptocurrencies nowadays) affect price. By taking 5 machine learning models and the Granger causality test, the correlation and causation relationship between sentiment analysis and bitcoin price can be determined.
Konstantinos I. Roumeliotis, Nikolaos D. Tselikas, Dimitrios Î. Nasiopoulos
Cryptocurrencies are becoming increasingly prominent in financial investments, with more investors diversifying their portfolios and individuals drawn to their ease of use and decentralized financial opportunities. However, this accessibility also brings significant risks and rewards, often influenced by news and the sentiments of crypto investors, known as crypto signals. This paper explores the capabilities of large language models (LLMs) and natural language processing (NLP) models in analyzing sentiment from cryptocurrency-related news articles. We fine-tune state-of-the-art models such as GPT-4, BERT, and FinBERT for this specific task, evaluating their performance and comparing their effectiveness in sentiment classification. By leveraging these advanced techniques, we aim to enhance the understanding of sentiment dynamics in the cryptocurrency market, providing insights that can inform investment decisions and risk management strategies. The outcomes of this comparative study contribute to the broader discourse on applying advanced NLP models to cryptocurrency sentiment analysis, with implications for both academic research and practical applications in financial markets.
Kia Jahanbin, Mohammad Ali Zare Chahooki, Mahdi YazdianâDehkordi, Fatemeh Rahmanian
OBJECTIVES: Due to the limitations of Twitter, the expansion of Telegram channels, and the Telegram API's easy use, Telegram comments have become prevalent. Telegram is one of the most popular social networks, unlike Twitter, which has no restrictions on sending messages, and experts can share their opinions and media. Some of these channels, managed by influencers of large companies, are very influential in the behavior of the market on various stocks, including cryptocurrencies. In this research, the opinion collection of 10 famous Telegram channels regarding the analysis of cryptocurrencies has been extracted. The sentiments of these opinions have been analyzed using the HDRB model. HDRB is a hybrid model of RoBERTa deep neural network, BiGRU, and attention layer used for sentiment analysis (SA). Analyzing the sentiments of these opinions is very important for understanding the future behavior of the market and managing the stock portfolio. The opinions of this dataset, published by experts in the field of cryptocurrencies, are precious, unlike the opinions that are extracted only by using the hashtag of the names of cryptocurrencies. On the other hand, the dataset related to cryptocurrencies, which has the opinions of experts and the polarity of their feelings, is very rare. DATA DESCRIPTION: The dataset of this research is the sentiments of more than ten popular Telegram channels regarding a wide range of cryptocurrencies. These comments were collected through the Telegram API from December 2023 to March 2024. This data set contains an Excel file containing the text of the comments, the date of comment creation, the number of views, the compound score, the sentiment score, and the type of sentiment polarity. These opinions cover influencer analysis on a wide range of cryptocurrencies. Also, two Word files, one containing the description of the dataset columns and the other Python code for extracting comments from Telegram channels, are included in this dataset.
In recent years, new payment methods have emerged, aimed at improving convenience for users. Cryptocurrencies, in principle, are no different. In this study, we seek to analyze the general population's attitudes towards the adoption of cryptocurrencies as a payment method. To achieve this, we have developed a descriptive survey that targets both current cryptocurrency users and non-users, recognizing that differences in perception may exist. Additionally, we have conducted a sentiment analysis of open-ended questions to understand respondents' views on the future of the cryptocurrency market and its potential as a payment tool, utilizing different lexicons in the English language. Our findings indicate that most cryptocurrency users prefer to invest in these digital assets, often choosing coins based on their popularity rather than other intrinsic features. E-commerce payments are the most attractive activity, followed by international transactions when using cryptocurrencies as a payment method. However, high volatility and a lack of ease of use are the most common difficulties reported by users. Our study also highlights the importance of regulation in a time when users are increasingly demanding higher levels of oversight, in contrast to the past. While users are concerned about the instability and volatility of cryptocurrencies, they also value the anonymity these transactions offer. Our analysis showcases an innovative approach to analyzing interviews and qualitative questionnaires that can be applied in other research fields.
Purpose The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis. Design/methodology/approach We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the âbitcoinâ and/or âbtcâ keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management. Findings Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market. Practical implications Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management. Originality/value We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.
In this paper, the focus is on sentiment analysis of web 3.0 enabled twitter dataset. The objective of the project is to explore various methods for performing sentiment analysis on Twitter datasets and implementing these methods on the web3.0 Twitter platform. The project involves collecting Twitter data through blockchain-based applications, preprocessing the data to remove noise, and applying machine learning models for sentiment analysis. Sentiment analysis is simply the extraction of thoughts, ideas, opinions, and emotions from sources such as text, speech, tweets, and databases using natural language processing (NLP) This process involves text segmentation mentally makes it "good," "bad," and "neutral" groups. In addition, it is known by other terms such as objective evaluation, mindfulness mining, and rating extraction. Web 3.0, also known as Web3, represents the third contemplated iteration of the World Wide Web, which aspires to establish a connected, transparent and intelligent online environment Based on the concept of decentralization, blockchain technology and the implementation of token-based economies. The main outcome of the project is to gain insights into the sentiment of users by analyzing WEB3.0 enabled Twitter data. By implementing sentiment analysis techniques on a WEB3.0 enabled Twitter dataset, the project aims to contribute to the field of sentiment analysis and showcase the effectiveness of using WEB3.0 enabled for data collection and analysis. The project aims to provide valuable insights for various methods of sentiment analysis for researchers. Keywords â Sentiment Analysis, Blockchain- Enabled, Twitter data, WEB 3.0, Machine Learning Models, User Sentiment, Research Contribution
This paper comprehensively examines Large Language Models (LLMs) in sentiment analysis, specifically focusing on financial markets and exploring the correlation between news sentiment and Bitcoin prices. We systematically categorize various LLMs used in financial sentiment analysis, highlighting their unique applications and features. We also investigate the methodologies for effective data collection and categorization, underscoring the need for diverse and comprehensive datasets. Our research features a case study investigating the correlation between news sentiment and Bitcoin prices, utilizing advanced sentiment analysis and financial analysis methods to demonstrate the practical application of LLMs. The findings reveal a modest but discernible correlation between news sentiment and Bitcoin price fluctuations, with historical news patterns showing a more substantial impact on Bitcoinâs longer-term price than immediate news events. This highlights LLMsâ potential in market trend prediction and informed investment decision-making.
M. Thamban Nair, Laila A. Abd-Elmegid, Mohamed I. Marie
Abstract Bitcoin (BTC) is one of the most important cryptocurrencies widely used in various financial and commercial transactions due to the fluctuations in the price of this currency. Recent research in large data analytics and natural language processing has resulted in the development of automated techniques for assessing the sentiment in online communities, which has emerged as a crucial platform for users to express their thoughts and comments. Twitter, one of the most well-known social media platforms, provides many tweets about the BTC cryptocurrency. With this knowledge, we can apply deep learning (DL) to use these data to predict BTC price variations. The researchers are interested in studying and analyzing the reasons contributing to the BTC priceâs erratic movement by analyzing Twitter sentiment. The main problem in this article is that no standard model with high accuracy can be relied upon in analyzing textual emotions, as it represents one of the factors affecting the rise and fall in the price of cryptocurrencies. This article aims to classify the sentiments of an expression into positive, negative, or neutral emotions. The methods that have been used are word embedding FastText model in addition to different DL methods that deal with time series, one-dimensional convolutional neural networks (CONV1D), long-short-term memory networks (LSTMs), recurrent neural networks, gated recurrent units, and a Bi-LSTM + CONV1D The main results revealed that the LSTM method, based on the DL technique, achieved the best results. The performance accuracy of the methods was 95.01, 95.95, 80.59, 95.82, and 95.67%, respectively. Thus, we conclude that the LSTM method achieved better results than other methods in analyzing the textual sentiment of BTC.
As cryptocurrencies become widely accepted due to technical improvements, reliable approaches to capture their future price movements of them become critical. This study mainly combines weighted sentiment analysis results from social media-related comments and financial news headlines with a stacked LSTM model to predict second-day Bitcoin price evolution. This study also compared our results and the results produced by MLP, RF, and SVM after feeding the sentiment analysis results.
Alexandru Costin Baroiu, Vlad DÄąaconÄąČa, SimonaâVasilica Oprea
Cryptocurrencies have emerged as a popular investment vehicle, prompting numerous efforts to predict market trends and identify metrics that signal periods of volatility. One promising approach involves leveraging on-chain data, which is unique to cryptocurrencies. On-chain data, extracted directly from the blockchain, provides valuable information, such as the hash rate, total transactions, or the total number of addresses that hold a specified amount of cryptocurrency. Some studies have also explored the relationship between social media sentiment and Bitcoin, using data from platforms such as Twitter and Google Trends. However, the quality of Twitter sentiment analysis has been lackluster due to suboptimal extraction techniques. This research proposes a novel approach that combines a superior sentiment analysis technique with various on-chain metrics to improve predictions using a deep learning architecture based on long-short term memory (LSTM). The proposed model predicts outcomes for multiple time horizons, ranging from one day to 14 days, and outperforms the Martingale (random walk) approach by over 9%, as measured by the mean absolute percentage error metric, as well as recent results reported in literature. To the best of our knowledge, this study may be among the first to employ this combination of techniques to improve cryptocurrency market prediction.
This study explores the unique linguistic characteristics of bitcoin, which has significantly changed the financial world over the past few years. As the concept of bitcoin, cryptocurrency and the digital network behind them are not dealt with in LSP (Language for Specific Purposes) coursebooks yet, this small-scale research is intended to fill this niche. In order to see what terminology has to be acquired to be able to understand the basic issues about bitcoin, online sources dealing with this innovative technology and its regulatory systems have been used. The selected online texts are analysed by TextStat software, which is capable of making word counts and collocation frequency. The results show us the most common collocations with bitcoin, and blockchain processing within context, as well as the most frequently used words (e.g., cryptocurrency or exchange), which definitely need to be learned by students majoring in Business English. My aim with this research is that LSP teachers get a comprehensive picture of what terminology to teach to their student when dealing with the topic of cryptocurrencies. In addition, bitcoin-related vocabulary can be integrated into other subjects, such as economics, finance or technology, allowing students to explore the connections between different fields of knowledge. Keywords: Bitcoin, cryptocurrency, blockchain, collocation, online
Current study is mostly focused on the exploration of the role of blockchain technology in enhancing the value of greetings. To accomplish this goal, renowned blockchain-based greetings NFTs from Opensea and Young Parrot have been taken into account. The welcome non-fungible tokens (NFTs) are built upon the Matic and Core blockchain networks. In order to get insight into the key determinants that significantly impact the demand for blockchain-based NFTs used for greetings, a comprehensive survey was undertaken including all facets of this burgeoning phenomenon. Extensive research has been undertaken to enhance comprehension of the determinants that propel the demand for NFTs based on blockchain technology within the domain of greetings. The factors under consideration include the pricing, overall quantity, use case, and popularity of NFTs. A survey was conducted on Twitter, using a sample size of 525 individuals. Based on the findings of the conducted study, it can be deduced that the primary determinant of the value attributed to greetings is their level of popularity. Furthermore, it has been observed that the Love Emogie have a restricted availability. The limited availability of just 43 Love Emojie has contributed to the heightened demand for NFTs owing to their inherent scarcity. However, it is also noted that pricing and use case have a substantial influence.
Ivan Ric P. Woogue, Sana Izumi, Angie M. Ceniza-Canillo
Cryptocurrencies are a type of digital currency that have been gathering attention as they are becoming more accepted as an investment medium and societies have become more receptive to cashless payments around the world. Previous studies have predominantly concentrated on the mainstream cryptocurrencies like Bitcoin and Ethereum. In contrast, this research focused on the correlation between meme coins, specifically Dogecoin, and tweets related to Dogecoin. The researchers have used a tweet dataset related to Dogecoin from Kaggle. VADER was used to get the sentiment scoring of the tweets. The input features used were tweet sentiments, tweet volume, and lag values at 1,3,8, and 12 hours. A total of 12 LSTM models using different combinations of the input features were developed utilizing the Bayesian Optimization algorithm. Half of the models were optimized for R-squared, and the other half were optimized for mean squared error (MSE). The models were evaluated using root mean squared error, mean directional accuracy, mean absolute error, R-squared, and percentage of duplicate values. The input features of the best-performing models had a combination of tweet sentiments, tweet volume, and all 4 lag values. The one optimized for R-squared is slightly better than the one optimized for MSE. However, the best-performing models still fell short since their R-squared was a negative value at â6.60364 and â6.21998 for those optimized for MSE and R-squared, respectively. This outcome may be attributed to the lack of available data since the tweet dataset used spans only approximately 4 months.
The previous smart contract code comment (SCC) generation approaches can be divided into two categories: fine-tuning paradigm-based approaches and information retrieval-based approaches. However, for the fine-tuning paradigm-based approaches, the performance may be limited by the quality of the gathered dataset for the downstream task and they may have knowledge-forgetting issues. While for the information retrieval-based approaches, it is difficult for them to generate high-quality comments if similar code does not exist in the historical repository. Therefore we want to utilize the domain knowledge related to SCC generation in large language models (LLMs) to alleviate the disadvantages of these two types of approaches. In this study, we propose an approach SCCLLM based on LLMs and in-context learning. Specifically, in the demonstration selection phase, SCCLLM retrieves the top-k code snippets from the historical corpus by considering syntax, semantics, and lexical information. In the in-context learning phase, SCCLLM utilizes the retrieved code snippets as demonstrations, which can help to utilize the related knowledge for this task. We select a large corpus from a smart contract community Etherscan.io as our experimental subject. Extensive experimental results show the effectiveness of SCCLLM when compared with baselines in automatic evaluation and human evaluation.
This paper presents a comprehensive exploration of graph embedding techniques for sentiment analysis. The objective of this study is to enhance the accuracy of sentiment analysis models by leveraging the rich contextual relationships between words in text data. We investigate the application of graph embedding in the context of sentiment analysis, focusing on it is effectiveness in capturing the semantic and syntactic information of text. By representing text as a graph and employing graph embedding techniques, we aim to extract meaningful insights and improve the performance of sentiment analysis models. To achieve our goal, we conduct a thorough comparison of graph embedding with traditional word embedding and simple embedding layers. Our experiments demonstrate that the graph embedding model outperforms these conventional models in terms of accuracy, highlighting it is potential for sentiment analysis tasks. Furthermore, we address two limitations of graph embedding techniques: handling out-of-vocabulary words and incorporating sentiment shift over time. The findings of this study emphasize the significance of graph embedding techniques in sentiment analysis, offering valuable insights into sentiment analysis within various domains. The results suggest that graph embedding can capture intricate relationships between words, enabling a more nuanced understanding of the sentiment expressed in text data.
Background: Due to the increased interest in cryptocurrencies, opinions on cryptocurrency-related topics are shared on news and social media. The enormous amount of sentiment data that is frequently released makes data processing and analytics on such important issues more challenging. In addition, the present sentiment models in the cryptocurrency domain are primarily focused on English with minimal work on Malay language, further complicating problems. Objective: The performance of the sentiment regression model to forecast sentiment scores for Malay news and tweets is examined in this study. Methods: Malay news headlines and tweets on Bitcoin and Ethereum are used as the input. A hybrid Generalized Autoregressive Pretraining for Language Understanding (XLNet) language model in combination with Bidirectional-Gated Recurrent Unit (Bi-GRU) deep learning model is applied in the proposed sentiment regression implementation. The effectiveness of the proposed sentiment regression model is also investigated using the multi-head self-attention mechanism. Then, a comparison analysis using Bidirectional Encoder Representations from Transformers (BERT) is carried out. Results: The experimental results demonstrate that the number of attention heads is vital in improving the XLNet-GRU sentiment model performance. There are slight improvements of 0.03 in the adjusted R2 values with an average MAE of 0.163 (Malay news) and 0.174 (Malay tweets). In addition, an average RMSE of 0.267 and 0.255 were obtained respectively for Malay news and tweets, which show that the proposed XLNet-GRU sentiment model outperforms the BERT sentiment model with lesser prediction errors. Conclusion: The proposed model contributes to predicting sentiment on cryptocurrency. Moreover, this study also introduced two carefully curated Malay corpora, CryptoSentiNews-Malay and CryptoSentiTweets-Malay, which are extracted from news and tweets, respectively. Further works to enhance Malay news and tweets corpora on cryptocurrency-related issues will be expended with implementing the proposed XLNet Bi-GRU deep learning model for greater financial insight. Keywords: Cryptocurrency, Deep learning model, Malay text, Sentiment analysis, Sentiment regression model
Blockchain technology is leading a revolutionary transformation across diverse industries, with effective governance standing as a critical determinant for the success and sustainability of blockchain projects. Community forums, pivotal in engaging decentralized autonomous organizations (DAOs), wield a substantial impact on blockchain governance decisions. Concurrently, Natural Language Processing (NLP), particularly sentiment analysis, provides powerful insights from textual data. While prior research has explored the potential of NLP tools in social media sentiment analysis, a gap persists in understanding the sentiment landscape of blockchain governance communities. The evolving discourse and sentiment dynamics on the forums of top DAOs remain largely unknown. This paper delves deep into the evolving discourse and sentiment dynamics on the public forums of leading DeFi projectsâAave, Uniswap, Curve Dao, Aragon, Yearn.finance, Merit Circle, and Balancerâplacing a primary focus on discussions related to governance issues. Despite differing activity patterns, participants across these decentralized communities consistently express positive sentiments in their Discord discussions, indicating optimism towards governance decisions. Additionally, our research suggests a potential interplay between discussion intensity and sentiment dynamics, indicating that higher discussion volumes may contribute to more stable and positive emotions. The insights gained from this study are valuable for decision-makers in blockchain governance, underscoring the pivotal role of sentiment analysis in interpreting community emotions and its evolving impact on the landscape of blockchain governance. This research significantly contributes to the interdisciplinary exploration of the intersection of blockchain and society, with a specific emphasis on the decentralized blockchain governance ecosystem. We provide our data and code for replicability as open access on GitHub.
Hardik Choudhary, M. J. Shukla, S Raghavendra, Ramyashree Ramyashree
The development of the fintech industry has transformed cryptocurrencies into intangible assets and opened many opportunities in the fields of financial research and quantitative markets. Cryptocurrencies are a type of electronic currency used to conduct transactions in the financial system. In addition to the technical analysis that a trader typically does, it has been established over time that market mood is extremely important in determining market conditions. This document provides a method for estimating cryptocurrency prices based on historical data and user sentiment. To achieve this, a long short-term memory (LSTM) model and sentiment analysis of tweets were used. Furthermore, it was supported by the outcomes, as the LSTM model demonstrated a precision of 69.32%, which is respectable when it comes to the forecasting of financially risky assets like bitcoin. The final accuracy attained was 70%, indicating that the model will accurately recommend buying or selling in about 3 out of every 4 scenarios that it is presented with. Traders can achieve a high alpha with a risk reward ration of 1:2 to benefit from this research finding and can combine the findings with technical indicators to produce better trades. This research has a very large application in the field of quant trading. Findings in this research can be used to build multiple models with multiple attributes which will improve the overall accuracy and precision of trades.
Blockchain data mining has the potential to reveal the operational status and behavioral patterns of anonymous participants in blockchain systems, thus providing valuable insights into system operation and participant behavior. However, traditional blockchain analysis methods suffer from the problems of being unable to handle the data due to its large volume and complex structure. With powerful computing and analysis capabilities, graph learning can solve the current problems through handling each node's features and linkage relationships separately and exploring the implicit properties of data from a graph perspective. This paper systematically reviews the blockchain data mining tasks based on graph learning approaches. First, we investigate the blockchain data acquisition method, integrate the currently available data analysis tools, and divide the sampling method into rule-based and cluster-based techniques. Second, we classify the graph construction into transaction-based blockchain and account-based methods, and comprehensively analyze the existing blockchain feature extraction methods. Third, we compare the existing graph learning algorithms on blockchain and classify them into traditional machine learning-based, graph representation-based, and graph deep learning-based methods. Finally, we propose future research directions and open issues which are promising to address.
Cryptocurrency works similar to standard currency, however, virtual payments are made for goods and services without the intervention of any central authority. Many investors believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of sentiment analysis and emotion for cryptocurrencies. For the study, we targeted (BTC) Bitcoin and collected related data. The data collection, cleaning were essential components of the study. Analysis of the sentiments about cryptocurrency is highly desirable to provide a holistic view of peoples' perceptions. In this regard, this study performs both sentiment analysis and emotion detection using the tweets related to the Bitcoin which are widely used for predicting the market prices of cryptocurrency. For increasing the efficacy of the analysis, a deep learning ensemble model LSTM-GRU is proposed that combines two recurrent neural networks applications. Comparatively, a larger number of people feel happy with the use of cryptocurrency, followed by fear and surprise emotions. The model achieves the highest performance for sentiment analysis with a 0.91 accuracy score and the highest emotion 0.83. Similarly, LSTM-GRU outperforms all other models in terms of correct and wrong predictions for both sentiment analysis 0.99 and emotion detection 0.98. Key Words: bitcoin, sentiment analysis, machine learning, cryptocurrencies, tweets