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Visualizing the YouTube Comments on Oscar-Nominated Movie Trailers

March 23, 2018Category : Blog
Visualizing the YouTube Comments on Oscar-Nominated Movie Trailers

Last Updated on by Preetish

It’s been only a few weeks since the 90th academy awards ceremony and the heated discussions about movies, actors and other film artists haven’t settled down yet. The 2018 Oscars brought recognition to many much deserving artists and their works. While the conversations on whether or not a particular movie deserved nomination seems to have no end, we were more interested in shining some light into the matter using data.

As a data solutions company focused on large-scale web data extraction, we decided visualize the user-generated content to understand viewers’ intent, sentiment and behavior. While it’s too late to make any predictions, we couldn’t stop ourselves from pulling the comment data from the YouTube trailers of the Oscar nominated movies to find out some of the interesting insights from the comments. 

Our goal here is to bring out any patterns, conflicts or insights even if they are seemingly subjective and draw connections to the success of these movies.

The data

For this study, we extracted the comments on the Oscar nominated movies’ trailers with the following data fields:

  •         User alias
  •         Timestamp
  •         Comment text
  •         Likes
  •         Has replies (True/False)
  •         Number of replies

Data extraction was completed on 6th March, 2018.

Popularity isn’t directly tied to winning or nomination

The number of comments on the Oscar-nominated movie trailers is relatively on the lower side if we compare it with the high-grossing popular superhero movies. Dunkirk is the only one with more than 15,000 comments but that’s a reasonable exception considering it’s a Christopher Nolan movie. However, if we look at the big picture, movies that got nominated for Oscar don’t have a high comment count as one would expect.

The Shape of Water, which won the 2018 academy awards has over 5000 comments (released in December, 2017). From this, It’s safe to say that it’s pretty difficult to predict a movie’s chances of winning the Oscar by taking its popularity alone into account.

Let’s now do an exploratory analysis on the comments to  figure out whether the movie enthusiasts write longer or shorter comments. Given below is the histogram of the length of the reviews based on character count (excluding white space).

We can see that the majority of the comments are not more than 60 characters in terms of length. Note that we have not considered the comments with more than 400 characters because of low frequency. If we consider all the comments, then the average length would come to 81 characters.

Top terms used in the reviews

It’d be interesting to visualize how the viewers perceive different movies and the unique element that just pops out for different movies. Here is a chart that shows the most frequently used words in the reviews posted for each of the Oscar-nominated movies.

It is evident that in case of ‘Dunkirk’ the dominating elements are Harry from One Direction and Christopher Nolan. For ‘Get Out’ the racial connotation is highly visible.

We’ll discuss about ‘The Shape of Water’ in greater details, since it won the best picture award.

The Shape of Water got comments with mixed sentiments

Although positive sentiment dominates in the comments on The Shape of Water trailer, the number of comments that expressed sentiments like anger, disgust and negativity were also close in numbers. This means, not everyone was impressed by the trailer and many even disliked it for some reason. However, none of this seemingly had an effect on the success of this movie.

Oscar nomination can change audience’s perception of a movie

The Shape of Water dominated with 13 nominations on 23rd January 2018, when the 90th Academy Awards nominations were announced. There has been a sharp rise in the positive sentiment and the gap between positive and negative sentiment widened which suggests that the perception of a movie among the public can be impacted by Oscar nomination and win.

Words that surround ‘The Shape of Water’

We created a word cloud to find the most commonly occurring words in the comments for “The Shape of Water” trailer.

The most common words are Abe, Hellboy, Bioshock, looks, like and love. This clearly means the commentators were comparing the creature or plotline of the movie with Hellboy movie (‘Abe’ character) and Bioshock game. We can also see that the audience were quite curious about the love and romance element in the movie despite the couple belonging to two entirely different forms of life.

Relationship between words – network graph of bigrams

We found out the most frequently used words in the reviews; however, what about the underlying intent and association between words? Let’s try to uncover how certain words are connected by plotting a network graph of bi-grams.

We can clearly see that the story has a strong association as a prequel of the movie ‘Hellboy’. There is some reference of ‘Pacific Rim’ as well — mostly because the director (Guillermo del Toro) of this movie had directed first part of ‘Pacific Rim’. We can also see that the viewers have mentioned the original inspiration of the movie, i.e., ‘The Creature from the Black Lagoon’. Apart from that the egg eating part of the story has also emerged.

Conclusion

Winning the Oscar is a big deal for those perfecting their craft of making great movies which are setting the standards higher by each passing year. While we couldn’t spot any correlation between the responses received by the trailers of these Oscar nominated movies, we could clearly see a rise in the positive sentiment towards movies that have been nominated among the audience and critics. However, we hope this might put some heated discussions on whether a certain movie deserved nomination to an end or at least shed some light into the intricacies of the matter.  


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3 thoughts on “Visualizing the YouTube Comments on Oscar-Nominated Movie Trailers
  • Giridhar G

    Need information about the language used for this analysis….And the code used for sentimental analysis on this data sets

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