Abstract:
The focus of this thesis is the categorization of seven emotional states: anger, happiness,
surprise, fear, sadness, confusion, and disgust. Additionally, a emotion detection study was
carried out in Bangla text. This capability of automatically detecting emotions in text has
grown in value with the expansion of digital communication. It can be used in social media
analysis, consumer feedback, and mental health assistance. However, the complexity in
Bangla morphologically and syntactically rich language are often missed by the existing
emotion recognition methods that are mostly built over high resource languages like
English. The aim of the study is to develop a classification model that will identify emotion
from a single Bangla text sample with high accuracy in overcoming these limitations. A
dataset was designed and annotated for the study, and then pre-processing techniques like
tokenization, normalization, stemming applied specifically for Bangla. Efficiency in
handling classification was tested by several machine learning and deep learning models.
Model performance for each category of emotion has been presented with the help of key
evaluation measures: precision, recall, F1-score. Confusion matrix is also shown in this
paper.
