--Cryptocurrencies fluctuate in markets with high price volatility, which becomes a great challenge for investors. T o aid investors in making informed decisions, systems predicting cryptocurrency market movements have been developed, commonly framed as feature-driven regression problems that focus solely on historical patterns favored by domain experts. However, these methods overlook three critical factors that significantly influence the cryptocurrency market dynamics: 1) the macro investing environment, reflected in major cryp-tocurrency fluctuations, which can affect investors collaborative behaviors, 2) overall market sentiment, heavily influenced by news, which impacts investors strategies, and 3) technical indicators, which offer insights into overbought or oversold conditions, momentum, and market trends are often ignored despite their relevance in shaping short-term price movements. In this paper, we propose a dual prediction mechanism that enables the model to forecast the next day's closing price by incorporating macroeconomic fluctuations, technical indicators, and individual cryptocurrency price changes. Furthermore, we introduce a novel refinement mechanism that enhances the prediction through market sentiment-based rescaling and fusion. In experiments, the proposed model achieves state-of-the-art performance (SOT A), consistently outperforming ten comparison methods in most cases. Cryptocurrencies have recently become a topic of conversation due to their great impact on the financial world. This heightened attention is fueled by several factors including the sudden drops and shocks in cryptocurrency markets [1], which offer opportunities for substantial returns, and the innovative technologies underpinning these assets, such as Blockchain [2], [3]. Unlike traditional financial markets such as bonds and stocks, the cryptocurrency market is characterized by a comparatively smaller market capitalization and pronounced volatility in short-term fluctuations [4], creating a unique and challenging investment landscape. This volatility stems from a complex interplay of factors that perpetuate a self-fulfilling cycle.

Analyzing stocks and making higher accurate predictions on where the price is heading continues to become more and more challenging therefore, we designed a new financial algorithm that leverages social media sentiment analysis to enhance the prediction of key stock earnings and associated volatility. Our model integrates sentiment analysis and data retrieval techniques to extract critical information from social media, analyze company financials, and compare sentiments between Wall Street and the general public. This approach aims to provide investors with timely data to execute trades based on key events, rather than relying on long-term stock holding strategies. The stock market is characterized by rapid data flow and fluctuating community sentiments, which can significantly impact trading outcomes. Stock forecasting is complex given its stochastic dynamic. Standard traditional prediction methods often overlook key events and media engagement, focusing its practice into long-term investment options. Our research seeks to change the stochastic dynamic to a more predictable environment by examining the impact of media on stock volatility, understanding and identifying sentiment differences between Wall Street and retail investors, and evaluating the impact of various media networks in predicting earning reports.

Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets.

In the distributed systems landscape, Blockchain has catalyzed the rise of cryptocurrencies, merging enhanced security and decentralization with significant investment opportunities. Despite their potential, current research on cryptocurrency trend forecasting often falls short by simplistically merging sentiment data without fully considering the nuanced interplay between financial market dynamics and external sentiment influences. This paper presents a novel Dual Attention Mechanism (DAM) for forecasting cryptocurrency trends using multimodal time-series data. Our approach, which integrates critical cryptocurrency metrics with sentiment data from news and social media analyzed through CryptoBERT, addresses the inherent volatility and prediction challenges in cryptocurrency markets. By combining elements of distributed systems, natural language processing, and financial forecasting, our method outperforms conventional models like LSTM and Transformer by up to 20\% in prediction accuracy. This advancement deepens the understanding of distributed systems and has practical implications in financial markets, benefiting stakeholders in cryptocurrency and blockchain technologies. Moreover, our enhanced forecasting approach can significantly support decentralized science (DeSci) by facilitating strategic planning and the efficient adoption of blockchain technologies, improving operational efficiency and financial risk management in the rapidly evolving digital asset domain, thus ensuring optimal resource allocation.

Abstract: In the process of numerically modeling natural languages, developing language embeddings is a vital step. However, it is challenging to develop functional embeddings for resource-poor languages such as Sinhala, for which sufficiently large corpora, effective language parsers, and any other required resources are difficult to find. In such conditions, the exploitation of existing models to come up with an efficacious embedding methodology to numerically represent text could be quite fruitful. This paper explores the effectivity of several one-tiered and two-tiered embedding architectures in representing Sinhala text in the sentiment analysis domain. With our findings, the two-tiered embedding architecture where the lower-tier consists of a word embedding and the upper-tier consists of a sentence embedding has been proven to perform better than one-tier word embeddings, by achieving a maximum F1 score of 88.04% in contrast to the 83.76% achieved by word embedding models.

Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these user generated sentiment data can span so many different domains that it is difficult to manually label training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classification where a systemtrained using labeled reviews from a source domain is deployed to classify sentimentsof reviews in a different target domain. In this paper, we propose to link heterogeneous input features with pivots via joint non-negative matrix factorization. This is achieved by learning the domain-specific information from different domains into unified topics, with the help of pivots across all domains. We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products. Experimental results show that our proposed approach significantly outperforms the baseline method, and achieves an accuracy which is competitive with the state-of-the-art methods for sentiment classification adaptation.

Shaip is a leader and innovator in the structured AI Data solutions category. Artificial intelligence is getting smarter by the day. Today, powerful machine learning algorithms are within reach of normal businesses, and algorithms requiring processing power that would once have been reserved for massive mainframes can now be deployed on affordable cloud servers. Natural language processing of the kind seen in popular chatbots may appear mundane, but it wasn't all that long ago it was the stuff of science fiction. Gartner ranks augmented data management, NLP and conversation AI as some of the key coming trends for data and analytics.

Despite only being early in the year, significant events have already taken place in 2021. Mass vaccinations for Covid-19 have begun around the world, and new strains of the disease have surfaced in the United Kingdom, South Africa and Brazil. For companies, this news has had a direct impact on their ability to conduct business while further placing their pandemic response under the public microscope. How companies are being talked and written about is changing as the pandemic unfolds, and these nuances could reveal more than simply how effective an organization's marketing department is. What if shifts in sentiment could help traders make more informed financial decisions?

In the Finance Industry, Alternative Data is used to give investors an information advantage. Quantitative Hedge Funds have used trading models based on Alternative Data for many years. The most common Alternative Data signal used in quantitative trading and quantitative investing is based on text data from the Internet, and the trading models can broadly be defined as algorithmic trading models and as statistical arbitrage models. It has been suggested that text analysis is the key to success for the most successful money manager of all times. The trading model can use text data and sentiment data as the only, or as one of several, inputs, and it can be the main strategy, or one of several strategies, in a hedge fund. Some traditional funds use text-based signals to build the models they use as an overlay to other strategies and as a risk indicator for tactical asset allocation.

Algorithmic trading, due to its inherent nature, is a difficult problem to tackle; there are too many variables involved in the real world which make it almost impossible to have reliable algorithms for automated stock trading. The lack of reliable labelled data that considers physical and physiological factors that dictate the ups and downs of the market, has hindered the supervised learning attempts for dependable predictions. To learn a good policy for trading, we formulate an approach using reinforcement learning which uses traditional time series stock price data and combines it with news headline sentiments, while leveraging knowledge graphs for exploiting news about implicit relationships. Keywords: Reinforcement Learning · Trading · Stock Price Prediction · Sentiment Analysis · Knowledge Graph. 1 Introduction Machine learning is mainly about building predictive models from data. When the data are time series, models can also forecast sequences or outcomes.