Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{167645,
        author = {Devaram Sharathchandra},
        title = {A SURVEY ON USAGE OF VECTORIZERS FOR TEXTUAL DATA IN EXPLORATORY DATA ANALYSIS (EDA)-GENERATIVE AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {4},
        pages = {9-14},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167645},
        abstract = {In the early age of Artificial Intelligence (AI) and Machine Learning (ML) domain, mostly training of ML models are depended on numerical data to classify, predict or generate. In today’s world we achieved the state “Machine models” can interact with human in a pure form of humanized text. Natural Language Processing (NLP) is the growing domain where it interacts with human in a way of speech recognition, text classification and text generation. The present era is experiencing prompt-based AI, where we can generate new images with a simple text prompt input or can generate a professional video or chat bot types models for virtual assistance. Simultaneously we are interacting with speech with a machine. The core technology behind this textual input is vectorizing the text data. When we interact with ML model with a speech input, in the background-the speech is converted into a textual format and then vectorized for prediction or generation to produce output. Based on the produced output the output layer can interact with human according to the choice provided by the end user weather it is belonging to NLP or Text generation transformer type model. The best example for humanized text generation model we are experiencing in today’s technology era are Google’s Gemini and Open AI’s Generative Pre-Trained Transformer (GPT) model. Vectorizers are the main technology behind these text transformation and analyzation models. The main amin these vectorizers re to improve machine learning model accuracy and reducing computational complexity of a ML model. NLP use multilayered neural networks for a Deep Learning (DL) model. Before feeding the first input layer with this textual data, we are using this vectorizers concept while training the deep learning model. Vectorization concept is involved in feature extraction and these will include different type of vectorizers. In this survey paper we discussed most of the vectorizers in section wise. In the I. Introduction section, I am going to introduce the concepts of vectors and what are different types of vectors available to use for machine learning model. From the section II. Core Technology, I’ll explain how we use vectorizers for a Machine Learning, Deep Learning and Transformer models to train. From the final section III. Results, difference between all type of vectorizers are concluded.},
        keywords = {Vectorizers, Machine Learning (ML), Deep Learning, NLP, Transformers, Artificial Intelligence (AI).},
        month = {September},
        }