# Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI
## 3 Labeling Methods for Generative AI
Various algorithms can be employed for this purpose, with deep learning being a notable approach that can be considered. Figure 3: The effect of real-time forecasting. Figure 2: The futures corpus. Figure 4 illustrates the scenario without using the futures corpus as a baseline section, allowing it so serve as a control group for comparing state-of-the-art methods. This approach highlights the significance of utilizing the futures corpus in the real-time text mining of financial news. Without the construction of a futures corpus, one is often limited by surface-level keywords like \"bull\" or \"bear\", as meaningful information may be concealed behind these keywords. The futures corpus generated through the iterative labeling process using machine learning serves as a crucial variable, significantly enhancing the contributions of this article. In comparison to the current research, the proposed method encompassing the futures corpus should improve the accuracy of bull or bear predictions. Before proposing the experimental process, a research model is constructed, as shown in Figure 5. The left side represents the preprocessing step of text mining. Various calculations, including PoS tagging and TF-IDF, are performed to generate multiple empty words. These empty words serve as the training set, with some extracted from the corpus to save collection time. After feedback, the valid data is saved back to the corpus for subsequent extractions. To evaluate the effectiveness of the training models, the latest real-time articles from actual financial websites are obtained for prediction and judgment. These real-time data will be labeled to increase the effectiveness of the training set. The effectiveness of the model requires assessment through training and testing using various algorithms. To cover a wide range of possibilities, several popular algorithms, such as support vector machines (SVMs), multi-layer perceptron (MLP), random forest, and deep learning, will be used for efficiency verification. Figure 5 depicts the scenario in which the futures corpus is utilized, serving as a control group for comparison with state-of-the-art methods as the baseline section. This approach effectively highlights the significance of employing the futures corpus in the real-time text mining of financial news. Without the construction of a futures corpus, the analysis is often limited by surface-level keywords such as \"bull\" or \"bear,\" overlooking meaningful insights that lie beneath these keywords. The futures corpus generated through iterative labeling using machine learning plays a pivotal role, significantly enhancing the contribution of this article. In comparison to the current research, the proposed method, which incorporates the futures corpus, is expected to improve the accuracy of bull or bear predictions. The next section will compare and validate this comparative model against the method proposed in this article. Figure 4: The baseline model. In summary, the process of capturing and analyzing real-time financial news to make instant predictions on futures bull-bear trends involves several key steps. Firstly, relevant financial news must be captured from multiple sources using crawlers or similar methods, followed by the performance of a text analysis on the collected news. Secondly, a corpus must be established to store classified futures bull-bear financial news, serving as a training data source for the prediction model. Thirdly, the model must be trained using techniques like deep learning, enabling it to automatically analyze news and predict its impact on actual trends, such as bull and bear futures. Real-time financial news is then input into the trained model to obtain predictions on the actual bull or bear positions and their influence on factors such as futures prices. It is important to acknowledge the challenges and limitations involved in this process. The vast amount of news information requires quick and accurate analysis.