## 2 Related Works
### Deep Learning-Based Approaches  
Recently, it has been observed that the number of people actively involved in social media is rapidly increasing [58]. People are expressing their feelings in the form of reviews, comments, posts, and statuses on various topics [59]. As a result of this tremendous amount of data generated on the Internet, which can be analysed for further research, traditional methods using predefined rules or ML algorithms are not enough to manage the large volume of data available [60,61]. Hence, the adoption of deep learning models in NLP is essential to uncovering valuable insights from these unorganised data. These models have delivered positive outcomes in analysing emotions, condensing text, and translating languages, becoming essential instruments for comprehending and utilising the large volume of text online. Applications of deep learning models have been successful in identifying patterns and trends in text data, providing valuable information for businesses to make informed decisions. Additionally, these models have the potential to revolutionise customer service by automating responses and improving the overall user experience. Furthermore, these models also help businesses personalise their marketing strategies and target specific  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Features** & **Lexicon** & **Dataset** & **Source** & **Metrics** & **Results** \\ \hline
[44] & Cognitive synonyms & SentiWordNet & SentiWordNet Corpus & SentiWordNet Available online: [[https://github.com/aesuli/SentiWordNet](https://github.com/aesuli/SentiWordNet)]([https://github.com/aesuli/SentiWordNet](https://github.com/aesuli/SentiWordNet)) (accessed on 1 October 2024) & Coverage & 90\% \\ \hline
[10] & Cognitive and emotional words & LIWC & Psycholinguistic Texts & LIWC Available online: [[https://www.liwc.net/](https://www.liwc.net/)]([https://www.liwc.net/](https://www.liwc.net/)) (accessed on 1 October 2024) & \multirow{2}{*}{F1} & \multirow{2}{*}{82\%} \\  & & & & October 2024) & & \\ \hline
[34] & Emotional words & ANEW & Affective Word List & ANEW Available online: [[https://github.com/dwzhou/SentimentAnalysis](https://github.com/dwzhou/SentimentAnalysis)]([https://github.com/dwzhou/SentimentAnalysis](https://github.com/dwzhou/SentimentAnalysis)) (accessed on 1 October 2024) & \multirow{2}{*}{High} \\ \hline
[35] & Emotional words & AFINN-96 & Word List Evaluation & AFINN-96 [[https://www.npmjs.com/package/afinn-96](https://www.npmjs.com/package/afinn-96)]([https://www.npmjs.com/package/afinn-96](https://www.npmjs.com/package/afinn-96)) (accessed on 1 October 2024) & \multirow{2}{*}{Precision} & \multirow{2}{*}{85\%} \\  & & & & & October 2024) & & \\ \hline
[53] & Word, sentence and n-gram & WSD, HMMs & Blogs, Editorials & SemEvalW70 Available online: [[https://web.eves.umich.edu/~mihalcea/affectivetext/&](https://web.eves.umich.edu/~mihalcea/affectivetext/&)]([https://web.eves.umich.edu/~mihalcea/affectivetext/&](https://web.eves.umich.edu/~mihalcea/affectivetext/&)) & \\  & & & & & \textasci  
customer segments more effectively. However, in South Africa, consumer reviews on social media platforms like Heltpeter provide essential insights into the performance and sentiment of financial institutions. Dealing with finance-related documents has presented challenges due to the traditional methods used for sentiment analysis. The recent advancements in LLMs and NLP have led to better ways of conducting sentiment analysis tasks. The development of LLMs and NLP has led to changes in model structures, pre-training techniques, and the integration of RAG technologies. RAG is a process that obtains relevant information from outside data in real time to improve context and relevance generation. The extra information is then fine-tuned into the model itself or downstream tasks [62]. Additionally, RAG technologies enable the models to interact with structured knowledge sources, allowing them to access and answer questions beyond the scope of the provided data.