Converting Text to Machine Learning Data

Requirements Clarification & Assessment

1. Understanding the Goal:

   • The primary objective is to convert text documents into a numerical format that can be processed by machine learning models.
   • Determine the nature of the text data (e.g., emails, social media posts, research papers) and the specific machine learning task (e.g., classification, sentiment analysis, topic modeling).

2. Data Preprocessing Needs:

   • Assess the need for preprocessing steps such as removing punctuation, converting text to lowercase, removing stopwords, and stemming or lemmatization.
   • Determine whether the order of words is important for the task at hand, which influences the choice of representation methods.

3. Select Appropriate Representation Techniques:

   • Identify whether simple methods like Bag of Words (BoW) or TF-IDF suffice, or if more advanced techniques like word embeddings (e.g., Word2Vec, GloVe) or pre-trained models (e.g., BERT, GPT) are necessary.

4. Feature Selection and Dimensionality Reduction:

   • Consider the need for feature selection to reduce the dimensionality of the data, especially if the dataset has a large vocabulary.

5. Evaluation and Validation:

   • Define metrics for evaluating the effectiveness of the text-to-numerical conversion in the context of the machine learning task.