3 research outputs found
Nostalgic Sentiment Analysis of YouTube Comments for Chart Hits of the 20th Century
Examining the comments associated with YouTube postings of songs
from the later decades of the 20th century can be fascinating. Many older people express how nostalgic the music might make them feel for that time in their
lives, and how it evokes a desire to be young again. It is interesting to understand whether they reflect a social phenomenon only possible through modern
technologies. The aim of this paper is to make an initial investigation. YouTube
videos for Number 1 songs from the British charts since the 1960’s were identified. Their comments were extracted and labelled as being nostalgic or not.
Two Machine learning techniques from the GATE tool were applied to the data
for different feature sets to find which technique performed best at classifying
nostalgia. The results show that, with cross-validation, the Decision Tree Classifier outperformed the Naïve Bayes. Additionally, it is shown that the feature
set has an influence on the accuracy
Nostalgic Sentiment Analysis of YouTube Comments for Chart Hits of the 20th Century
Examining the comments associated with YouTube postings of songs
from the later decades of the 20th century can be fascinating. Many older people express how nostalgic the music might make them feel for that time in their
lives, and how it evokes a desire to be young again. It is interesting to understand whether they reflect a social phenomenon only possible through modern
technologies. The aim of this paper is to make an initial investigation. YouTube
videos for Number 1 songs from the British charts since the 1960’s were identified. Their comments were extracted and labelled as being nostalgic or not.
Two Machine learning techniques from the GATE tool were applied to the data
for different feature sets to find which technique performed best at classifying
nostalgia. The results show that, with cross-validation, the Decision Tree Classifier outperformed the Naïve Bayes. Additionally, it is shown that the feature
set has an influence on the accuracy