that willattempt to determine the most common emotion expressed in a YoutTubevideo’scomments. You will create a number of functions (as specified in the FunctionalSpecification in Section 5) that will perform simple sentiment analysis on the YouTubecomments.To accomplish this, you will need to ...
We used NLTK Vader as our sentiment analyzer. It seemed to have better performance than other libraries such as textblob and fasttext. LD Score: Likes / (Likes + Dislikes) LD Score OHE: Converting decimal LD Score to categorical -1 (negative), 0 (neutral), and 1 (positive). View_Like ...
We used NLTK Vader as our sentiment analyzer. It seemed to have better performance than other libraries such as textblob and fasttext. LD Score: Likes / (Likes + Dislikes) LD Score OHE: Converting decimal LD Score to categorical -1 (negative), 0 (neutral), and 1 (positive). View_Like ...
Additionally, although a negative trend in median text sentiment was observed in 2015 for primates, an otherwise consistent positive median text and emoji sentiment score through time across all IUCN Red List categories was revealed in response to both exotic wild cat and primate videos, further ...
Now moving on to the sentiment analysis, we’ll use lexicon based approach. To do that we will look at the comments that viweres left below these videos. We will calculate sentiment score using lexicon library NRC. To explain briefly what a lexicon is, lexicon libraries are stock of words...
The only validated questionnaire used across multiple studies was the DISCERN score criterion. Conclusions Most information on YouTube about men’s health is unreliable. Videos created by physicians and healthcare organizations are more reliable, and videos that are advertisements are less reliable. ...
A different platform (e.g., YouTube) may have elicited different results as it has been noted that Twitter is an environment that can produce negative sentiment (Dyson and Gorvin, 2017; Thelwall et al., 2011). This negativity was evident in a study by O'Dea et al. (2018) who found...
An information completeness score was assigned, and the video comments were analyzed using sentiment analysis software. Results:The 60 videos included were viewed 34.4 million times by internet users. Braces videos had significantly more likes, comments, and a higher viewer interaction score than the ...
We used NLTK Vader as our sentiment analyzer. It seemed to have better performance than other libraries such as textblob and fasttext. LD Score: Likes / (Likes + Dislikes) LD Score OHE: Converting decimal LD Score to categorical -1 (negative), 0 (neutral), and 1 (positive). View_Like ...
This dataset is retrieved from Kaggle and it contains data from several countries, such as UK, Canada, Mexico, Japan, South Korea, France. etc. People have been using these data in the following ways: Sentiment analysis in a variety of forms Categorising YouTube videos based on their comments...