The Best Disney Movies to Learn English According to Data Science

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The first time I used Disney Plus, I used to be extremely joyful — and it wasn’t as a result of the Pixar and Marvel motion pictures to be had within the catalog, however as a result of their content material to be had in a couple of languages.

English, Spanish, Portuguese, Italian, you title it! At that second, I assumed, “this can be a excellent alternative for finding out international languages.” On best of that, we’re already aware of some Disney motion pictures, which is a bonus as it will increase our possibilities of figuring out the discussion in any language.

But Disney+ has around 662 motion pictures in its catalog. This is an excessive amount of content material to make a choice from, so I made a knowledge research to to find the most productive Disney motion pictures that can assist us be informed a international language simply as I up to now did for Netflix shows and 3000 top-rated movies.

The motion pictures analyzed are from the Disney+catalog, so content material from Pixar, Marvel, Star Wars and National Geographic have been additionally integrated.

Table of Contents
1. How Am I Choosing The Best Movies?
2. The Best Disney Movies to Learn a Foreign Language
- Ranking of The 300 Disney Movies
- The Best Movies for Beginner, Intermediate and Advanced Level
- The Movie Genres With Simple and Hard Vocabulary
3. Methodology
- Data Collection
- Tokenization
- Lemmatization
- Data Cleaning
4. Final Note

To make a choice the most productive Disney motion pictures to be informed a international language, I used the transcripts that comprise the discussion in each and every film.

Thanks to a wordlist created for corpus research within the linguistic box, I may just to find the trouble of the vocabulary utilized in each and every film. Then with some Python code, I created a score of the most productive motion pictures this is in response to the histogram under.

Don’t let the image scare you! The essential factor to know is that the easier the film’s vocabulary, the simpler to realize it. For instance, the highest 20 motion pictures are the most productive since you handiest want to know the highest 1000 maximum commonplace phrases in a language to acknowledge a minimum of 93% of the phrases.

Image by means of Author

The vocabulary in a language follows the Pareto rule. The top 1,000 most frequent words in a language make up over 80% of on a regular basis dialog. Learning commonplace phrases is a great alternative for finding out a international language!

Now that my definition of “best possible motion pictures,” let’s to find out which can be the most productive Disney motion pictures to be informed a international language.

More information about the research are within the following sections.

The following are the 10 best possible Disney motion pictures, wherein you handiest want 1000 phrases to acknowledge a minimum of 93% of the dialogues. There are 19 motion pictures within the first bar up to now proven, however I’m record the 10 most well liked to discover a trade-off between motion pictures with simple vocabulary and recognition:

Photo by means of inspiredbythemuse on Pixabay — Frozen 2 is within the best 10, whilst Frozen is within the best 30.
  1. The Last Song (2010)
  2. Pete’s Dragon (2016)
  3. The Parent Trap (1961 and 1998)
  4. Camp Rock 2: The Final Jam (2010)
  5. High School Musical 3 (2008)
  6. Confessions of a Teenage Drama Queen (2004)
  7. Brother Bear (2003)
  8. A Wrinkle In Time (2018)
  9. The Straight Story (1999)
  10. Frozen 2 (2019)

But that’s no longer all! The research is going past the highest 10 motion pictures. You don’t develop into fluent in a international language by means of handiest looking at 10 motion pictures, proper?

Ranking of The 300 Disney Movies

If you wish to have to to find the highest 20, 50, 100 or problem your self and watch the film that ranks #300 (the only with essentially the most tough vocabulary), you simply want to seek them within the desk under.

For instance, I loved looking at the film Finding Nemo when I used to be a child, so to know whether or not that’s a sensible choice to watch in Spanish or Portuguese, I simply kind Finding Nemo within the field and to find its score.

So, I discovered that Finding Nemo is in #123 within the score. Not dangerous; on the other hand, Finding Dory ranks #70, so I believe I’ll watch that one first to strengthen my language talents.

The Best Movies for Beginner, Intermediate and Advanced Level

If you wish to have extra customization, then you’ll to find the very best film to your language degree within the plot under. Unlike the former research, the place we handiest center of attention on the commonest 1000 phrases (newbie degree), on this case, we additionally analyze the share of dialogues that the commonest 2000 and 3000 phrases duvet in each and every film.

If 300 motion pictures aren’t sufficient for you, test my different articles to to find the most productive Netflix movies and shows and the most productive 3000 most popular movies for finding out a international language.

In case you’d like to know extra about how to be informed languages by means of looking at TV displays and films, I wrote a whole information explaining how I realized Three languages up to now by means of looking at TV.

The Movie Genres With Simple and Hard Vocabulary

After acquiring the effects for this research, I couldn’t assist noticing that many documentaries rank within the ultimate 10, so I spent a few of my time looking at a few of them to download a greater figuring out.

After looking at The African Lion, Dolfin Reef and The Living Desert on Disney Plus, I noticed that the vocabulary could be difficult for many language rookies. Unless you’re into wildlife, I wouldn’t suggest looking at that more or less content material in case your purpose is to develop into fluent in a international language.

On the opposite hand, the highest 10 comprise motion pictures from other genres, so I will’t get a hold of a conclusion about the most productive film style to be informed a international language. I’ll take a look at to make a unique research on this regard.

I did all this research in Python. The main points are on my Github. These have been the stairs I adopted:

Data Collection

For this research, I used 2 datasets — the Disney Plus catalog and picture transcripts. I googled film transcripts to to find as many Disney film transcripts as I may just get. In the top, I handiest discovered round 400, however after the cleansing procedure, handiest 300 transcripts remained for the research.

Then I downloaded the Disney Plus catalog dataset to be had on Kaggle. It is composed of titles to be had on Disney Plus as of 2020. I used the catalog to fit the transcripts with the titles to be had on Disney Plus.

Tokenization

To analyze the vocabulary within the transcripts, I tokenized all phrases spoken by means of characters. There are many gear for tokenization in Python, however I used CountVectorizer as it converts the transcripts accumulated to a dataframe of token counts, which simplifies the research. I defined a little extra about how the CountVectorizer works within the article the place I analyzed 3000 movies.

from sklearn.feature_extraction.textual content import CountVectorizer
from sklearn.feature_extraction import textual content
import scipy.sparse
cv = CountVectorizer()
cv_matrix = cv.fit_transform(df_analysis['transcripts'])
df_dtm = pd.DataBody.sparse.from_spmatrix(cv_matrix, index=df_analysis.index,columns=cv.get_feature_names())
df_dtm = df_dtm.T

Lemmatization

After tokenizing, I had to to find the bottom type of each and every token. You can do that by means of the usage of lemmatization ways, which you’ll to find within the NLTK library. However, I used word-family lists that do a equivalent task and in addition provide the degree of issue of each and every be aware in response to its frequency. As of 2020, there are 29-word-family lists and you’ll to find a few of them here. These lists have been evaluated on analysis papers in linguistics and English finding out as a 2d language.

Data Cleaning

I got rid of phrases that couldn’t be heard within the motion pictures, akin to scenes’ descriptions and audio system’ names. The cleansing approach I used is some distance from highest, nevertheless it is helping me standardized the dialogues inside transcripts. The clean_transcripts report I used is to be had on my Github.

I additionally excluded transcripts whose discussion had greater than 4.5% of phrases that didn’t fit the word-family lists (they may well be outliers or corrupted knowledge). I normally used 3.5%, however on this case, I watched one of the motion pictures that had between 3.5% and four.5% and I didn’t to find the rest bizarre within the transcripts.

from cleansing import clean_transcripts
round1 = lambda x: clean_transcripts(x)
df_analysis['transcripts'] = df_analysis['transcripts'].follow(round1)
...
df_statistics = df_statistics[df_statistics[100]<4.5]

In this research, we discovered the most productive Disney motion pictures to be informed a language. It’s a really perfect thought to get started with simple motion pictures, so we’ve got much less bother figuring out dialogues and extra a laugh looking at the scenes. However, additionally imagine looking at motion pictures you prefer, so select motion pictures you prefer that rank prime.

The transcripts used for the research are in English. I examined the effects for some languages like Spanish and Portuguese they usually paintings wonderful. I might say that this works smartly for love languages, however I will’t ensure the similar effects for different languages.

Please let me know if the films nonetheless have a very simple vocabulary by means of looking at them within the language you’re finding out.

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