Presence of informal language, such as emoticons, hashtags, and slang, impact the performance of sentiment analysis models on social media text?

• URL: http://arxiv.org/abs/2301.12303v1
• Date: Sat, 28 Jan 2023 22:21:51 GMT
• Title: Presence of informal language, such as emoticons, hashtags, and slang, impact the performance of sentiment analysis models on social media text?
• Authors: Aadil Gani Ganie
• Abstract summary: This study investigated the influence of informal language, such as emoticons and slang, on the performance of sentiment analysis models applied to social media text. A CNN model was developed and trained on three datasets: a sarcasm dataset, a sentiment dataset, and an emoticon dataset. The results revealed that the model achieved an accuracy of 96.47% on the sarcasm dataset, with the lowest accuracy for class 1. 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%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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