## 2 Literature Review  
### Financial Sentiment Analysis  
Financial sentiment analysis is a distinct area of study in data analytics that specifically deals with the interpretation of emotions, opinions, and attitudes conveyed in financial texts [18]. These encompass many types of information, such as news stories, social media posts, analyst reports, and earnings call transcripts. The main objective is to assess the sentiment toward financial markets, specific companies, or broader economic conditions, as these factors significantly impact investment choices and market dynamics [19].  
The theory that public sentiment affects financial markets was initially introduced by [20], who attributed market behaviors to \"animal spirits\"--alternating waves of optimism and pessimism. This concept has been substantiated by a multitude of studies that have shown substantial correlations between investor sentiment and market fluctuations in asset prices and trading volumes [21; 22].  
[PERSON]. [22] made significant contributions to the field of sentiment analysis for financial texts utilizing lexicon-based techniques, including the Bag of Words (BoW) model, to identify underlying sentiments in financial news. This method entails creating word lists with each word labeled as either having a positive or negative sentiment. [PERSON]. [23] created a financial emotion dictionary specifically designed for evaluating 10-K reports, emphasizing the need to have lexicons that are tailored to specific sectors. Although lexicon-based analysis is widely used, it has been criticized for its simplistic approach and lack of consideration for word context [24; 25; 26; 27].  
In order to overcome these constraints, researchers have transitioned to employing machine learning methodologies. Machine learning frameworks, which are trained on labeled financial texts, offer more accurate sentiment assessments by adapting to word context, unlike static lexicon-based algorithms [28]. Substantial advancements have been achieved in the field, such as the introduction of the FinancialPhraseBank dataset and the utilization of machine learning for sentiment detection [29]. Additionally, [PERSON]. [30] suggested a competitive approach that relies on semantic characteristics.  
The field has undergone further development following the integration of deep learning methods into natural language processing (NLP), resulting in considerable improvements in sentiment analysis capabilities. Transformer-based models such as BERT, presented by [31], have shown outstanding performance in sentiment classification tasks. This is attributed to their capacity to understand context, capture long-term relationships, and decrease data complexity [32]. Furthermore, the implementation of FinBERT, which is a specialized version of BERT [33] for the financial domain, has enhanced the reliability and accuracy of financial sentiment analysis [34; 35].  
The latest advancements in LLMs have significantly improved their effectiveness for NLP. New standards have been established by innovative techniques such as instruction tuning [17] and the retrieval-augmented framework for strengthening sentiment analysis [36]. The models ChatGPT, Gemini, and LLAMAs are presently under evaluation to determine their ability to forecast market trends based on news headlines [37; 38]. This suggests a positive outlook for LLMs in the field of financial analytics.