  This study aimed to investigate the influence of the presence of informal
language, such as emoticons and slang, on the performance of sentiment analysis
models applied to social media text. A convolutional neural network (CNN) model
was developed and trained on three datasets: a sarcasm dataset, a sentiment
dataset, and an emoticon dataset. The model architecture was held constant for
all experiments and the model was trained on 80% of the data and tested on 20%.
The results revealed that the model achieved an accuracy of 96.47% on the
sarcasm dataset, with the lowest accuracy for class 1. On the sentiment
dataset, the model achieved an accuracy of 95.28%. The amalgamation of sarcasm
and sentiment datasets improved the accuracy of the model to 95.1%, and the
addition of emoticon dataset has a slight positive impact on the accuracy of
the model to 95.37%. The study suggests that the presence of informal language
has a restricted impact on the performance of sentiment analysis models applied
to social media text. However, the inclusion of emoticon data to the model can
enhance the accuracy slightly.
