Analysis of Public Sentiment Towards Celebrity Endorsment On Social Media Using Support Vector Machine - Syahputra, M Oriza; Bustami, Bustami; Rosnita, Lidya (Malikussaleh University, Aceh, Indonesia, 2024)
Analysis of public sentiment towards celebrity endorsements on social media is very important to understand the public's response to promotional campaigns involving celebrities. In this study, we combine the VADER labeling method with the Support Vector Machine (SVM) method to analyze public sentiment toward celebrity endorsements on social media. Data is taken from various social media sources such as Twitter, Instagram, and Facebook. The data is pre-processed to ensure data accuracy and relevance and then labeled with the VADER method to determine the positive, negative, or neutral sentiment of the text. The labeled data is then extracted for features and used to train the SVM model. The trained SVM model is then validated using test data to measure its accuracy and performance. The results of the analysis provide useful insight into public sentiment towards celebrity endorsements on social media and can provide recommendations for stakeholders regarding this matter. Overall, combining the VADER labeling method with SVM in analyzing public sentiment towards celebrity endorsements on social media shows more accurate results and can provide practical benefits in marketing and promotional strategies. The results shown using the Support Vector Machine method with a ratio of 80:20 can provide average precision results of 77%, recall of 100%, f1-score of 87%, and accuracy of 76.92%. Twitter application user sentiment shows that 77% (338 data) of Twitter user reviews provide positive sentiment and 23% (119 data) provide negative sentiment reviews from a total of 517 data. Suggestions from researchers are that in future research they can add more data to make modeling easier to provide higher accuracy values. Using other classification and performance evaluation methods, such as Naive Bayes, Decision Tree, Fuzzy, or Deep Learning. Use other data processing tools, such as RapidMiner, Jupyter Notebook, RStudio, or others.