Presence of informal language, such as emoticons, hashtags, and slang, impact the performance of sentiment analysis models on social media text?
This addresses sentiment analysis accuracy for social media users, but it is incremental as it shows only minor gains from including informal language data.
This study investigated how informal language like emoticons and slang affects sentiment analysis models on social media text, finding that it has a restricted impact with slight accuracy improvements (e.g., from 95.1% to 95.37%) when emoticon data is added.
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.