Natural Language Processing and Sentiment Analysis

EasyChair Preprint 12569

Abstract

Natural Language Processing (NLP) and Sentiment Analysis have garnered significant attention in recent years due to their potential to extract valuable insights from vast amounts of textual data. NLP refers to the field of artificial intelligence concerned with the interaction between computers and human language, enabling machines to understand, interpret, and generate human language. Sentiment Analysis, a subfield of NLP, focuses on extracting subjective information and sentiment from textual data, aiming to determine the emotional tone or polarity associated with a given text.

This abstract provides an overview of the concepts, methodologies, and applications of NLP and Sentiment Analysis. It highlights the growing importance of these fields in various domains, including social media analysis, customer feedback analysis, market research, and opinion mining.

The abstract begins by introducing the fundamental concepts and techniques utilized in NLP, such as tokenization, part-of-speech tagging, syntactic parsing, and named entity recognition. It also explores the challenges associated with processing unstructured and noisy textual data, including ambiguity, sarcasm, and colloquial language.

The abstract then delves into Sentiment Analysis, elucidating its primary objective of automatically categorizing text into positive, negative, or neutral sentiments. It discusses the different approaches employed in sentiment classification, ranging from lexicon-based methods to machine learning algorithms, deep learning models, and hybrid approaches.

Keyphrases: Technology, science, vision

@booklet{EasyChair:12569,
  author    = {Favour Olaoye and Kaledio Potter},
  title     = {Natural Language Processing and Sentiment Analysis},
  howpublished = {EasyChair Preprint 12569},
  year      = {EasyChair, 2024}}