Abstract— Emotions are a person's response to an event.
Emotions can be expressed verbally or nonverbally. Over time
people can express their emotions through social media.
Considering that emotion is a reflection of society's response, it is
important to classify emotions in society to find out the
community's response as information for consideration in
decision-making. This study is aimed to identify and analyze the
datasets, methods, and evaluation metrics that are being used in
the classification of emotional texts in textual data from research
data from 2013 to 2022. Based on the inclusion and exclusion
design in selecting literature, a total of 50 kinds of literature were
used in extracting and synthesizing data. Analysis of the data
shows that out of 50 pieces of literature, there are 36 works of
literature that use public datasets while 14 kinds of literature use
private datasets. In the method of developing models for
classifying, the SVM and Naive Bayes models are the most popular
among the other models. In evaluating the model, the F-measure
or F1-score metric is the most widely used metric compared to
other metrics. There are three main contributions identified in this
study, namely methods, models, and evaluation