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.
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 projects—Aave, Uniswap, Curve Dao, Aragon, Yearn.finance, Merit Circle, and Balancer—placing 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.
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
Saachin Bhatt, Mustansar Ali Ghazanfar, Mohammad Hossein Amirhosseini
This research explores the impact of social media sentiments on predicting Bitcoin prices using machine learning models, integrating on-chain data, and applying a Multi Modal Fusion Model. Historical crypto market, on-chain, and Twitter data from 2014 to 2022 were used to train models including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting, and Multi Modal Fusion. Performance was compared with and without Twitter sentiment data which was analysed using the Twitter-roBERTa and VADAR models. Inclusion of sentiment data enhanced model performance, with Twitter-roBERTa-based models achieving an average accuracy score of 0.81. The best performing model was an optimised Multi Modal Fusion model using Twitter-roBERTa, with an accuracy score of 0.90. This research underscores the value of integrating social media sentiment analysis and onchain data in financial forecasting, providing a robust tool for informed decision-making in cryptocurrency trading.
Relation triple extraction (RTE) is an essential task in information extraction and knowledge graph construction. Despite recent advancements, existing methods still exhibit certain limitations. They just employ generalized pre-trained models and do not consider the specificity of RTE tasks. Moreover, existing tagging-based approaches typically decompose the RTE task into two subtasks, initially identifying subjects and subsequently identifying objects and relations. They solely focus on extracting relational triples from subject to object, neglecting that once the extraction of a subject fails, it fails in extracting all triples associated with that subject. To address these issues, we propose BitCoin, an innovative Bidirectional tagging and supervised Contrastive learning based joint relational triple extraction framework. Specifically, we design a supervised contrastive learning method that considers multiple positives per anchor rather than restricting it to just one positive. Furthermore, a penalty term is introduced to prevent excessive similarity between the subject and object. Our framework implements taggers in two directions, enabling triples extraction from subject to object and object to subject. Experimental results show that BitCoin achieves state-of-the-art results on the benchmark datasets and significantly improves the F1 score on Normal, SEO, EPO, and multiple relation extraction tasks.
Many researchers agree that sentiment analysis can improve the performance of quantitative trading models. We develop two off-the-shelf solutions for analyzing the sentiments of cryptocurrency-related social media posts. First, we posttrain and fine-tune a Twitter-oriented model based on the bidirectional encoder representations from transformers (BERT) architecture, BERTweet, on the cryptocurrency domain, resulting in CryptoBERT. Second, we generate the language-universal cryptocurrency emoji (LUKE) sentiment lexicon and prediction pipeline, utilizing the sentiment of emojis prevalent in social media. CryptoBERT is highly accurate, while LUKE is suitable for non-English posts, thus allowing for direct classification and noisy label generation in less popular languages. Our research can help cryptocurrency investors develop trading software supported by sentiments mined from social media.
Achraf Boumhidi, Abdessamad Benlahbib, El Habib Nfaoui
Reputation generation systems are decision-making tools used in different domains including e-commerce, tourism, social media events, etc. Such systems generate a numerical reputation score by analyzing and mining massive amounts of various types of user data, including textual opinions, social interactions, shared images, etc. Over the past few years, users have been sharing millions of tweets related to cryptocurrencies. Yet, no system in the literature was designed to handle the unique features of this domain with the goal of automatically generating reputation and supporting investors’ and users’ decision-making. Therefore, we propose the first financially oriented reputation system that generates a single numerical value from user-generated content on Twitter toward cryptocurrencies. The system processes the textual opinions by applying a sentiment polarity extractor based on the fine-tuned auto-regressive language model named XLNet. Also, the system proposes a technique to enhance sentiment identification by detecting sarcastic opinions through examining the contrast of sentiment between the textual content, images, and emojis. Furthermore, other features are considered, such as the popularity of the opinions based on the social network interactions (likes and shares), the intensity of the entity’s demand within the opinions, and news influence on the entity. A survey experiment has been conducted by gathering numerical scores from 827 Twitter users interested in cryptocurrencies. Each selected user assigns 3 numerical assessment scores toward three cryptocurrencies. The average of those scores is considered ground truth. The experiment results show the efficacy of our model in generating a reliable numerical reputation value compared with the ground truth, which proves that the proposed system may be applied in practice as a trusted decision-making tool.
Cryptocurrencies are digital currencies that operate on the blockchain, which is the technology that offers security and decentralization. The principal characteristic of cryptocurrencies is that they are not generally issued by a central authority. Many factors can influence the volatility of prices. This paper enables to drive insights into the behavior of markets through the application of sentiment analysis of Tweets, Google news and machine learning techniques for the challenging task of cryptocurrency price prediction. Most of the studies have focused exclusively on the sentiment analysis of tweets. In this work, we propose the use of common machine learning tools and available Google News data for predicting the price of crypto. We present the results of the Long Short-Term Memory (LSTM) model using Tweets and Google News data.
The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user's following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%.
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.
The volume of information on the internet is currently rising dramatically. Social media platforms/e-commerce market place is producing a lot of data, including reviews, comments, and opinions, every day. As there are a number of fake reviews should incorporate Spam detection to produce a genuine opinion. Fake reviews are growing problem in online shopping, and they have a significant impact on consumer’s decision-making. Many people today base their decisions when choosing a product or service on social media opinions. Because so many false or phoney evaluations have been written by businesses or individuals for a variety of reasons, detecting opinion spam is a difficult and time-consuming task. They produce fictitious reviews to deceive users or automated detection systems by elevating or degrading the reputations of their target products in order to elevate or lower them. In this article, we’ll regulate it by leveraging blockchain technology to make the review system more authentic by allowing only legitimate product purchasers to submit evaluations using their account credentials. We use the Ethereum blockchain to authenticate user credentials, and we only permit verified users to purchase things. Also, we only permit customers to leave reviews or comments on products, ensuring that the reviews are accurate.
Cryptocurrencies have emerged in recent years and have continued to grow until they have become very popular, widespread, and surrounded by various pros and cons of their innovative developments. This study uses sentiment analysis on Twitter towards cryptocurrencies to create positive and negative trends based on comments by classifying data on Twitter. The dataset used is a Tweet related to cryptocurrency in June 2022. To get positive or negative sentiment of this research using SVM classification method with the following stages. The dataset goes through the stages of preprocessing, labeling, imbalance handling, train test split, TF-IDF weighting to transform the data from text to numeric which are then used for sentiment analysis using SVM. This research resulted in sentiment analysis based on the TextBlob library to determine positive sentiment and negative sentiment. The accuracy of the application of SVM in this study is known through the use of k-fold cross validation and three methods, namely undersampling, without imbalance handling and oversampling and three comparisons of train test splits, namely 90:10, 80:20, and 70:30. In k-fold cross validation, an accuracy of 93. 19% was obtained. While the other three methods, obtained three highest accuracy where the accuracy is in a ratio of 80:20, namely 94.64% for undersampling, 93.42% for data without imbalance handling, and 93.40% for oversampling. The best accuracy is in the data that goes through the undersampling process with a ratio of 80:20 which is of 94.64% and the sentiment analysis result is a positive sentiment.
Abstract Cryptocurrency investment especially Bitcoin, has become favourable over recent years due to promising returns in the future. However, the movement of price is mainly speculation-based as these currencies are still new in the market. The COVID-19 outbreak boosted research interest in predicting the price fluctuation of Bitcoin since cryptocurrency trading produced many millionaires. Five lexicon-based Twitter sentiment analysis approaches are examined to see the effect of Tweets on the price of Bitcoin during the pre- and post- COVID-19 period. Results show that negative Twitter sentiments affected the price of Bitcoin pre- COVID-19 and the second year of post- COVID-19 when Elon Musk actively criticised Bitcoin on Twitter.
Emotions form an essential and fundamental aspect of our lives. What we do and say reflects some of our feelings in some way, though not directly. We must examine these feelings using emotional data, also known as affect data, to comprehend a person's basic behavior. Text, voice, facial expressions, and other data types can be included. Since social networking websites have become so popular, many individuals have started reading the material on these numerous sites.Twitter is one of these social networking sites. People's feelings and thoughts about a subject reveal positive, negative, and neutral emotional values. Doing sentiment analysis on Twitter is a very important and challenging task. In this study, we aim to investigate the sentiments of Bitcoin and provide an overview of its effect on the value of Bitcoin by utilizing the power of deep learning architectures and machine learning methods. The study collected tweets in English shared on Twitter between December 12, 2021, and March 13, 2022. First, people's feelings about Bitcoin were assessed using TextBlob, a natural language processing (NLP) tool. Then, it was done using basic machine learning algorithms for sentiment classification and CNN, LSTM, and BiLSTM deep learning architectures that we modeled. However, deep learning models were tested separately with the TF-IDF and Glove word embedding approaches. Experimental results prove the success of deep learning architectures using the Glove word embedding approach.
Gül Cihan Habek, Mansur Alp Toçoğlu, Aytuğ Onan
As the cryptocurrency trading market has grown significantly in recent years, the number of comments related to cryptocurrency has increased tremendously in social media platforms. Due to this, sentiment analysis of the cryptocurrency-related comments has become highly desirable to give a comprehensive picture of peoples’ opinions about the trend of the market. In this regard, we perform cryptocurrency-related text sentiment classification using tweets based on positive and negative sentiments. For increasing the efficacy of the sentiment analysis, we introduce a novel deep neural network hybrid architecture which is composed of an embedding layer, a convolution layer, a group-wise enhancement mechanism, a bidirectional layer, an attention mechanism, and a fully connected layer. Local features are derived using a convolution layer, and weight values associated with intuitive features are developed using the group-wise enhancement mechanism. After feeding the improved context vector to the bidirectional layer to grab global features, the attention mechanism and the fully connected layer have been employed. The experimental findings indicate that the proposed architecture outperforms the state-of-the-art architectures with an accuracy value of 93.77%.
Individual mental feelings and reactions are getting more significant as they help researchers, domain experts, businesses, companies, and other individuals understand the overall response of every individual in specific situations or circumstances. Every pure and compound sentiment can be classified using a dataset, which can be in the form of Twitter text by various Twitter users. Twitter is one of the vital platforms for individuals to participate and share their ideas about different topics; it is also considered to be one of the most famous and the biggest website for micro-blogging on the Internet. One of the key purposes of this study is to classify pure and compound sentiments based on text related to cryptocurrencies, an innovative way of trading and flourishing daily. The cryptocurrency market incurs many fluctuations in the coins’ value. A small positive or negative piece of news can sensate the whole scenario about the specific cryptocurrencies. In this paper, individuals’ pure and compound sentiments based on cryptocurrency-related Twitter text are classified. The dataset is collected through the Twitter API. In WEKA, the two deployment schemes are compared; firstly, straight with single feature selection technique (Tweet to lexicon feature vector), and secondly, a tetrad of feature selection techniques (Tweet to lexicon feature vector, Tweet to input lexicon feature vector, Tweet to SentiStrength feature vector, and Tweet to embedding feature vector) are used to purify the data LibLINEAR (LL) classifier, which contains fast algorithms for linear classification using L2-regularization L2-loss support vector machines (Dual SVM). The LL classifier differs in that it can potentially alleviate the sum of the absolute values of errors rather than the sum of the squared errors and is typically much speedier. Based on the overall performance parameters, the deployment scheme containing the tetrad of feature selection techniques with the LL classifier is considered the best choice for the purpose of classification. Among machine learning techniques, LL produces effective results and gives an efficient performance compared to other prevailing techniques. The findings of this research would be beneficial for Twitter users as well as cryptocurrency traders.
Traditional sentiment analysis methods are based on text-, visual- or audio-processing using different machine learning and/or deep learning architecture, depending on the data type. This situation comes with technical processing diversity and cultural temperament effect on analysis of the results, which means the results can change according to the cultural diversities. This study integrates a blockchain layer with an LSTM architecture. This approach can be regarded as a machine learning application that enables the transfer of the metadata of the ledger to the learning database by establishing a cryptographic connection, which is created by adding the next sentiment with the same value to the ledger as a smart contract. Thus, a "Proof of Learning" consensus blockchain layer integrity framework, which constitutes the confirmation mechanism of the machine learning process and handles data management, is provided. The proposed method is applied to a Twitter dataset with the emotions of negative, neutral and positive. Previous sentiment analysis methods on the same data achieved accuracy rates of 14% in a specific culture and 63% in a the culture that has appealed to a wider audience in the past. This study puts forth a very promising improvement by increasing the accuracy to 92.85%.
Virtual currencies or cryptocurrencies are based on Blockchain technology, also known as distributed ledger technology. As of March 2022, there are already over 10k virtual coins, their number being continuously growing since 2013. This paper aims to extract the public sentiment expressed towards the cryptocurrency market and Blockchain technology, two topics widely debated in the last decade. Our research was based on the use of Twitter data, collected with the help of an API in the RStudio environment.
Misinformation constitutes one of the main challenges to counter the infodemic: misleading news, even if not blatantly false, can cause harm especially in crisis scenarios such as the pandemic. Due to the fast proliferation of information across digital media, human fact-checkers struggle to keep up with fake news, while automatic fact-checkers are not able to identify the grey area of misinformation. We, thus, propose to reverse engineer the manipulation of information offering citizens the means to become their own fact-checkers through digital literacy and critical thinking. Through a corpus analysis of fact-checked news about COVID-19, we identify 10 fallacies–arguments which seem valid but are not–that systematically trigger misinformation and offer a systematic procedure to identify them. Next to fallacies, we examine the types of sources associated to (mis-/dis-)information in our dataset as well as the type of claims making up the headlines. The statistical patterns surfaced from these three levels of analysis reveal a misinformation ecosystem where no source type is exempt from flawed arguments with frequent evading the burden of proof and cherry picking behaviors, even when descriptive claims are at stake. In such a scenario, exercising the audience’s critical skills through fallacy and semantic analysis is necessary to guarantee fake news immunity.
Abstract Twitter sentiment has been shown to be useful in predicting whether Bitcoin’s price will increase or decrease. Yet the state-of-the-art is limited to predicting the price direction and not the magnitude of increase/decrease. In this paper, we seek to build on the state-of-the-art to not only predict the direction yet to also predict the magnitude of increase/decrease. We utilise not only sentiment extracted from tweets, but also the volume of tweets. We present results from experiments exploring the relation between sentiment and future price at different temporal granularities, with the goal of discovering the optimal time interval at which the sentiment expressed becomes a reliable indicator of price change. Two different neural network models are explored and evaluated, one based on recurrent nets and one based on convolutional networks. An additional model is presented to predict the magnitude of change, which is framed as a multi-class classification problem. It is shown that this model yields more reliable predictions when used alongside a price trend prediction model. The main research contribution from this paper is that we demonstrate that not only can price direction prediction be made but the magnitude in price change can be predicted with relative accuracy ( 63%).