English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study aimed to investigate the influence of the presence of informallanguage, such as emoticons and slang, on the performance of sentiment analysismodels applied to social media text. A convolutional neural network (CNN) modelwas developed and trained on three datasets: a sarcasm dataset, a sentimentdataset, and an emoticon dataset. The model architecture was held constant forall 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 thesarcasm dataset, with the lowest accuracy for class 1. On the sentimentdataset, the model achieved an accuracy of 95.28%. The amalgamation of sarcasmand sentiment datasets improved the accuracy of the model to 95.1%, and theaddition of emoticon dataset has a slight positive impact on the accuracy ofthe model to 95.37%. The study suggests that the presence of informal languagehas a restricted impact on the performance of sentiment analysis models appliedto social media text. However, the inclusion of emoticon data to the model canenhance the accuracy slightly.

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