Analysing Market Sentiments: Utilising Deep Learning to Exploit Relationships within the Economy
In today{'}s world, globalisation is not only affecting inter-culturalism but also linking markets across the globe. Given that all markets are affecting each other and are not only driven by fundamental data but also by sentiments, sentiment analysis regarding the markets becomes a tool to predict, anticipate, and milden future economic crises such as the one we faced in 2008. In this paper, an approach to improve sentiment analysis by exploiting relationships among different kinds of sentiment, together with supplementary information, from and across various data sources is proposed.
Code (0)
등록된 구현이 없습니다.
Tasks
Opinion MiningSentiment AnalysisSimilar Papers 제목 키워드 기반
Leveraging News Sentiment to Improve Microblog Sentiment Classification in the Financial Domain
With the rising popularity of social media in the society and in research, analysing texts short in length, such as microblogs, becomes an increasingly important task. As a medium of communication, microblogs carry peopl…
General ClassificationSentiment AnalysisSentiment ClassificationA longitudinal sentiment analysis of Sinophobia during COVID-19 using large language models
The COVID-19 pandemic has exacerbated xenophobia, particularly Sinophobia, leading to widespread discrimination against individuals of Chinese descent. Large language models (LLMs) are pre-trained deep learning models us…
MisinformationSentiment AnalysisAnalysing Public Transport User Sentiment on Low Resource Multilingual Data
Public transport systems in many Sub-Saharan countries often receive less attention compared to other sectors, underscoring the need for innovative solutions to improve the Quality of Service (QoS) and overall user exper…
Opinion MiningSentiment AnalysisFinancial News Analytics Using Fine-Tuned Llama 2 GPT Model
The paper considers the possibility to fine-tune Llama 2 GPT large language model (LLM) for the multitask analysis of financial news. For fine-tuning, the PEFT/LoRA based approach was used. In the study, the model was fi…
Language ModelingLanguage ModellingLarge Language ModelSentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning
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 t…
Algorithmic TradingDeep Reinforcement LearningKnowledge Graphsreinforcement-learning+4