Preprocessing for Text Data and NLP

PRDTM2-785 AI Trading C2 L3 Vid6 Preprocessing For Text Data And NLP

Using Text Data in Trading Models

Generative AI, with large language models (LLMs) for text generation, is pivotal, even if not the immediate focus. Pre-processing text data is crucial for effective trading models. It includes:

Text Data Sources: Consider integrating news articles, social media posts, regulatory filings, and rating reports to gain insights, particularly about investor sentiment.
Word Embedding: Text data must be converted into numerical vectors, known as embeddings, to be compatible with machine learning models.
Text Cleaning: Ensure consistency in text by converting to lowercase and removing unnecessary punctuation and special characters. Use regular expressions (RegeX) to efficiently clean data.
Tokenization: Transform words into tokens, the basic currency of natural language processing (NLP), using Python libraries.
Stopwords Removal: Eliminate common words like 'and' and 'the' to enhance the model's focus on meaningful words.
Stemming and Lemmatization: Simplifies words to their root forms, improving clarity and efficiency in text analysis. Use this step to further refine your data inputs for optimal model performance.

SOLUTION:

Text preprocessing converts text into numerical formats that can be fed into machine learning models since models can't be built on words.