The finance sector is one of those that have had a dramatic change after the integration of technology and more so Artificial Intelligence, Big Data, Text Mining and Cloud computing which has also given way to the development of e-finance where electronic channels together with digital technologies are used to deliver financial services. E-finance takes advantage of these new technologies to provide easy, efficient, and innovative financial services to people. This makes it possible for DFI to reduce expenses, make data-informed decisions and improve their experience for clients but also opens access to financial services for people in emerging markets. Advances need to ensure data protection and secrecy for the well and confidence in financial industry as well: research will scrutinize effects due technological progress on AI plus Big Data, Text Mining or Cloud Computing within Finance. This paper attempts a comprehensive review of literature through scholarly articles and papers published over the past thirteen years (2013–2025) available through Google Scholar and SCOPUS. The research methodology proceeded by choosing and crossing keywords related to technology and fintech, based on a bibliometric analysis of the past and current literature of related fields, the frequency of citation, and usage in emerging AI, Big Data, Text Mining, and Cloud computing trends in finance. Wide coverage was pursued by considering expert-reviewed high-ranking keywords drawn from the core areas of the field. The results identify the top publishers and journals holding significant influence in the field of E-finance. Moreover, this paper will also shed light on the possible benefits of text mining in finance which include improving customer satisfaction as well as detecting and preventing fraudulent transactions and risks mitigation. Results underscore the co-dependence between Finance, Technology, AI, Big Data, Text Mining, Cloud. And how they fit with each other; it further proposed a new methodology that detailed using multi-source datasets. However, these findings form valuable insights into the growing importance of emerging technologies upon the finance industry and current key trends in this area.

Financial time series forecasting is traditionally based on historical price patterns, often overlooking exogenous and behavioral variables that can significantly affect market movements. In this study, we enhance the Prophet forecasting framework by integrating sentiment signals derived from financial news headlines and social media platforms such as Twitter. These sentiment indicators, processed using both lexicon-based (TextBlob) and transformer-based (BERT) models, are aligned temporally with stock indices data (notably the Dow Jones Industrial Average) to create a multimodal dataset. The sentiment scores are incorporated into Prophet as external regressors, allowing the model to learn associations between market mood and price trends. This hybrid approach captures both technical and psychological dimensions of financial behavior. Extensive experiments on the ^DJI index demonstrate that the sentiment-augmented model consistently outperforms the baseline Prophet implementation across all evaluation metrics. For example, the mean absolute percentage error (MAPE) is reduced from 7.63 to 5.13% in the test dataset and from 4.25 to 3.12% in the validation dataset. These performance gains are particularly evident during periods of high volatility, such as macroeconomic announcements and geopolitical developments, where traditional models tend to reach their limits. By combining quantitative signals with qualitative sentiment, our approach provides a more adaptive and accurate forecasting system. This approach is very promising for traders, portfolio managers, and political analysts seeking to predict market behavior in an increasingly sentiment-driven environment.