Youtube Comment Sentimental Analysis
Aiswarya A S1, Haritha Rajeev2
1Aiswarya A S, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
2Haritha Rajeev, Department of Computer Science, St. Albert’s College, Kochi (Kerala), India.
Manuscript received on 15 April 2024 | Revised Manuscript received on 02 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 5-8 | Volume-4 Issue-1 May 2024 | Retrieval Number: 100.1/ijdm.A163304010524 | DOI: 10.54105/ijdm.A1633.04010524
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The amount of textual data has grown dramatically over time, opening up new avenues for machine learning (ML) and natural language processing (NLP) study. These days, sentiment analysis of comments on YouTube is a really fascinating subject. Although there are a lot of user reviews and comments on many of these films, the low consistency and quality of the material in these comments has prevented much work from being done in terms of identifying trends from them thus far. In this research, we use machine learning techniques and algorithms to perform sentiment analysis on YouTube comments pertaining to popular themes. We show that a clear picture of how real-world events affect public sentiment can be obtained by analyzing the attitudes to identify trends, seasonality, and projections. The findings indicate a strong correlation between the sentiment trends of users and the actual occurrences linked to the corresponding keywords. This study uses a YouTube extractor to perform sentiment analysis on comments on YouTube using citation sentences.To remove the noise from the corpus of comments, various data normalization algorithms were applied to the data. We created a system using six distinct machine learning techniques, including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), to perform classifying on this data set.
Keywords: Youtube Comments, NLP, Youtube Extractor, Machine Learning Algorithms.
Article of the Scope: Data Science