Cinephiles and lovers of glam rejoice this Sunday – it’s Oscars Award Ceremony time! Due to Corona, this year’s 93rd edition was postponed from February to April 25th – the latest in the history of the award. I was personally happy about the delay, because it gave me more time to analyse some data and come up with this blog post.

On this ocassion, I was curious to do some text mining on the acceptance speeches. Specifically, I analysed the speeches of the Best Directors between 1941 and 2019. I used a dataset from Kaggle and added missing data for 2017, 2018, and 2019 directly from the Academy Awards Acceptance Speech database.

In total, 74 Best Directors have been awarded and almost all of them gave acceptance speeches, which I analysed with Python and the NLTK library. You can find the Jupyter notebook here. Let’s see what the words reveal about the Best Directors and the Oscars!

### 1. Average speech length

The speech of a Best Director has 104 words on average, but speeches range widely from 8 to 267 words.

Here’s how to calculate the number of words in a text:

directing["words"] = directing['Speech_clean'].str.split().str.len()


### 2. Longest & shortest speeches

The longest speech runs at 267 words and was given by Mel Gibson at the 68th Academy Awards in 1995 for his film Braveheart. This guy had a looot of people to thank to and seems to have used up all his words for saying pretty much nothing:

Oh, I don’t write speeches but I would like to thank a few people. And I have it on a list, not a long list. Not too long anyway. I mean, I’d like to thank the Academy first of all, and Alan Ladd, Jr. and Randall Wallace who first came to me with the script. Randy wrote it, Laddy was attached as the producer. And they had no problem with giving the reins to a fiscal imbecile. You know, this takes real courage. My production company at Icon – Bruce Davey, Steve McEveety, Dean Lopata, my right hand man. And everyone at Icon. Paramount and Fox, who both went in, they double dipped, Sherry Lansing, Jon Dolgen, Barry London, Peter Chernin, Bill Mechanic, Jim Gianopulos. It’s one of the best cast and crews I’ve ever worked with. John Toll, who’s already been honored here tonight. Tom Sanders, Charles Knode, Steve Rosenblum, David Tomblin – thanks for dragging me through the mud sometimes. James Horner, Mic Rodgers, Simon Crane, and the clan Wallace. We mustn’t forget the clan Wallace. Nigel Sinclair, Ed Limato, Jeff Berg, Alan Nierob, Paul Bloch, Rod Lurie, my wife and family for indulging me. And God, for indulging me in this tiny moment. And every director I’ve ever worked with, they were my film school. And now that I’m a bona fide director with a golden boy, I uh, well, like most directors I suppose what I really want to do is act. Is that right? Thank you from the bottom of my heart. This is a truly wonderful evening for me. Thank you.

The shortest speech was summed up in 8 words by Delbert Mann at the 28th Academy Awards in 1955 for his film Marty. I really like his efficient “I came. I won. I thanked.” structured speech:

Thank you. Thank you very much. Appreciate it.

Here’s how to find the longest and shortest text in a dataframe with pandas:

directing.sort_values(by="words")


### 3. Lexical richness

Lexical richness is a measure of how many unique words are used in the text. Lexical richness is calculated as the total number of unique words divided by the total number of words. The higher the score, the richer the vocabulary–and viceversa. Here’s to calculate lexical richness for each speech in the dataframe with Python:

def lexical_richness(text):
return round(len(set(str(text))) / len(str(text)), 3)
directing["lex_rich"] = [lexical_richness(directing["Speech_clean"][i]) for i in range(len(directing))]


The speech with the highest lexical richness (0.408) is Delbert Mann’s, the director of Marty, awarded in 1955. This means that 40.8% of the words he used are distinct.

At the other end, the speech with the lowest lexical richness (0.034) is Mel Gibson’s, the director of Braveheart, awarded in 1995. This means that 3.4% of the words he used are distinct.

### 4. Longest words

The longest words used in directors’ speeches have 15 words: administrations, cinematographer, and czechoslovakian.

Here’s how to select the longest words in a text:

long_words = [w for w in all_speeches_tokenized if len(w) > 14]
sorted(long_words)


### 5. Most common words

The top 10 most common words in all acceptance speeches are: thank (201 occurences), much (56), like (50), people (48), want (42), would (30), movie (26), film (26), say (24), and many (22).

Interestingly, out of these 10 words, 3 are nouns (referring to people and film/movie), 2 express large quantities (much and many), and 5 are verbs that express personal feelings (want, like) or actions (say, thank). It’s also worth noting that the word thank has a significantly higher frequency than the following common words, which is however understandable.

Here’s how to find the frequency distribution of words in a text with NLTK:

FreqDist(all_speeches_tokenized).most_common(10)


### 6. “Thank” to…

Ok, winners thank a lot, but who do they thank to? It turns out… to you, but also to the Pacific Command of the United States, Mr. harry Cohn, Marlon, the producers, and each one of them.

Here’s see the location of a word in context with NLTK:

Text(word_tokenize(all_speeches)).concordance('thank')


That’s all, folks! Could/should I have analysed anything else? Let me know what you think in the comments below ⬇️