Emotion Detection from Text

Abstract

Understanding human emotions expressed through written language has become increasingly important in the fields of natural language processing and text analytics. With the expansion of social media platforms and online communication channels, large volumes of user-generated text provide valuable insights into people's emotional states. Automatically identifying emotions from textual data can support applications such as sentiment monitoring, mental health analysis, customer feedback evaluation, and social media analytics.This study investigates the application of machine learning methods for detecting emotions in textual data. The research utilizes the SemEval-2018 Affect in Tweets dataset, which contains tweets annotated with multiple emotional categories including anger, fear, joy, love, sadness, and surprise. Prior to model training, the dataset undergoes several preprocessing steps such as punctuation removal, tokenization, elimination of stop words, and lemmatization to enhance textual consistency and reduce noise.