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AI Model for Sentiment Analysis System Using Python
Subject area: Science,Engineering and Technology · Area of research: Medical Health
Abstract
Sentiment analysis is an important task in natural language processing that aims to understand and classify opinions and emotions represented in text data. This research article introduces a complete way to develop an AI model for sentiment analysis using the Python programming language. The study makes use of a labeled Twitter dataset, which includes messages with positive, negative, and neutral attitudes. On this dataset, machine learning model is trained and assessed, with a focus on preprocessing stages such as text cleaning, tokenization, and feature extraction. The suggested system makes use of cutting-edge machine learning and deep learning algorithms, as well as Natural Language Processing (NLP) libraries. Data pre-processing, feature extraction, and the use of various text representation approaches, such as word embeddings, are all part of the model construction process.
Keywords
Random Forest, Health, Text, Machine Learning, Sentiment, Voice, Twitter
How to cite this paper
@article{1705456,
author = {Kiran Pal, Dr. Santosh Singh, Rimsy Dua, Lisa Rodrigues},
title = {AI Model for Sentiment Analysis System Using Python},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {8},
pages = {41-45},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705456.pdf},
abstract = {Sentiment analysis is an important task in natural language processing that aims to understand and classify opinions and emotions represented in text data. This research article introduces a complete way to develop an AI model for sentiment analysis using the Python programming language. The study makes use of a labeled Twitter dataset, which includes messages with positive, negative, and neutral attitudes. On this dataset, machine learning model is trained and assessed, with a focus on preprocessing stages such as text cleaning, tokenization, and feature extraction. The suggested system makes use of cutting-edge machine learning and deep learning algorithms, as well as Natural Language Processing (NLP) libraries. Data pre-processing, feature extraction, and the use of various text representation approaches, such as word embeddings, are all part of the model construction process.},
keywords = {Random Forest, Health, Text, Machine Learning, Sentiment, Voice, Twitter},
month = {February},
}