# Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI
## 3 Labeling Methods for Generative AI  
Based on the research methods and the issues discussed in recent literature, this study employs text processing techniques aligned with generative AI to collect text data and perform various preprocessing steps for the domain of extremely short-term trading in index futures. The methodology involves creating and labeling a bull-bear training set, as depicted in Figure 1. Initially, a widely recognized and extensive financial website is utilized as a web crawler to gather relevant articles. When employing deep learning for natural language processing, the articles are transformed, converting each word into a vector representation through techniques such as word embedding. In Figure 1, pertinent word vectors can be employed to convert words into vectors. TF-IDF is commonly used in text retrieval and information retrieval tasks to measure the similarity between documents by calculating the weights of words. BERT, on the other hand, is widely applied in various natural language processing tasks, including text classification, named entity recognition, and question answering. Its ability to understand contextual information enables it to excel in handling complex semantic tasks. However, in this article, the focus is on labeling the text representing the bull and bear stock futures market, based on its binary nature and the characteristics of short texts. The objective is to quickly annotate the data to respond to very short-term trading. Therefore, BERT may not be the most suitable choice for this specific research area [25]. These innovative models utilize TF-IDF and Word2 Vec techniques to enhance their effectiveness in analyzing and processing the data in this field. One widely used approach is Word2 Vec, which is a neural network-based language model that represents words as high-dimensional vectors. In the context of Chinese financial news, Word2 Vec can be utilized to train word vectors by considering multiple words or even word sequences as a single unit. This approach, known as a word vector model using multiple words or word sequences as a unit, enhances the representation power of the word vectors and captures more contextual information. Chinese words are often structurally complex, comprising multiple characters or morphemes. Consequently, Chinese text processing commonly involves word segmentation to divide the text into individual words or phrases. In the Word2 Vec model, the segmented words serve as training units to derive vector representations for each word. These word vectors effectively capture semantic information and express contextual relationships between Chinese words. Figure 1: Bull and Bear Words Training Set. Subsequently, the vector representations of all words are concatenated to create a matrix of dimensions (N, d), where N represents the number of words in the text, and d represents the dimension of the word vector. This matrix can be perceived as a sequence of length N. Moving forward the sequence is subjected to a 1D convolutional layer. The convolution operation is applied to the sequence, allowing flexibility in defining the size of each convolution kernel. The convolution operation treats neighboring words as local text segments, enabling the extraction of features from these segments. It can be visualized as a sliding window moving across the sequence, extracting features within the window at each step. Subsequently, a pooling layer is applied to reduce the extracted features from each convolution kernel to a single numerical value. Common pooling techniques, such as max pooling or average pooling, can be utilized for this purpose. This step aids in compressing the features extracted by each convolution kernel, resulting in a reduction in the number of model parameters. Finally, all the compressed features are concatenated and passed through a fully connected layer for classification. In this specific case, a three-layer fully connected network can be employed for classification.