Analysis of User Sentiment Toward Mountain Climbing Content Based on YouTube Comments Using the K-Nearest Neighbors Algorithm
DOI:
https://doi.org/10.30871/jaic.v10i4.13518Keywords:
Sentiment Analysis, YouTube, Mountain Climbing, K-Nearest Neighbor (KNN), TF-IDF, Natural Language Processing (NLP)Abstract
The growth of social media has made YouTube one of the primary sources of information regarding mountain climbing activities. YouTube’s comment feature contains a variety of user opinions that can be used to gauge public perception of climbing content. However, the sheer volume of comments makes manual analysis less effective. This study aims to analyze user sentiment toward mountain climbing content based on YouTube comments using the K-Nearest Neighbor (KNN) algorithm. The research method used is a quantitative approach involving data collection through YouTube comment crawling, manual sentiment abelling, and data preprocessing which includes cleaning, case folding, normalization, tokenization, stopword removal, and stemming followed by feature weighting using Term Frequency–Inverse Document Frequency (TF-IDF) and classification using the K -Nearest Neighbor (KNN) algorithm. The model was evaluated using a confusion matrix with the metrics of accuracy, precision, recall, and F1-score. The results of the study show that the K-Nearest Neighbor (KNN) algorithm is capable of classifying the sentiment of YouTube comments with a maximum accuracy of 79.35% at K = 5. These results indicate that the K-Nearest Neighbor (KNN) algorithm is quite effective in analyzing the sentiment of comments related to mountain climbing content, although it still has limitations in understanding semantic context, such as the use of informal language, irony, and sarcasm. Therefore, future research is recommended to use more complex feature representation methods, such as Word2Vec, FastText, or BERT-based models, to improve classification capabilities and sentiment analysis accuracy.
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