Presence of informal language, such as emoticons, hashtags, and slang, impact the performance of sentiment analysis models on social media text?
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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 The results revealed that the model achieved an accuracy of 96.47 sarcasm dataset, with the lowest accuracy for class 1. On the sentiment dataset, the model achieved an accuracy of 95.28 and sentiment datasets improved the accuracy of the model to 95.1 addition of emoticon dataset has a slight positive impact on the accuracy of the model to 95.37 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.
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