An Analysis of World Leader’s Speeches

Language of Leaders

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The spoken word can ignite change and influence millions, which is why our latest study takes a peek into the world of political communication. We’ve compiled and analyzed speeches from renowned global leaders, from politicians to authors, activists, and religious figures, applying detailed text analysis to uncover patterns in vocabulary and lexical diversity (how many different words they use). This article offers insights into how world figures use language to persuade and impact society. Join us as we explore the power of words in politics.

Key Takeaways

  • Of all the speeches we analyzed, Ales Bialiatski had the highest lexical diversity: 56.2% of the words in his speeches were unique. 

  • Donald Trump’s speeches had 26.8% lexical diversity, while Joe Biden’s had 25.8%.

  • The most frequently used word in U.S. leader’s speeches was “know.”

  • “People” was the most common word in world leader’s speeches.

  • The speeches we analyzed had an average of 33.6% unique words overall.

Decoding the Language of U.S. Leaders

Our study uncovers the top words used by U.S. leaders and how varied their vocabulary is. See which words are the hallmarks of influential voices below.

Top Words Among US LeadersTop Words Among US Leaders

Overall, the most frequently used word in speeches by U.S. leaders was “know,” which suggests they often desire to sound confident and knowledgeable.

James Hansen stood out from the crowd with “climate” as his most-used word, reflecting a focused commitment to environmental issues. His lexical diversity score was also the highest among all U.S. leaders in our study, with just over 43% of the words in his speeches that we analyzed being unique. Similarly, Al Gore’s emphasis on “fossil” and Tim Cook’s on “people” showcased their respective areas of focus, be it the former’s environmental activism or the latter’s people-centric approach to technology.

Compared to these leaders, former President Donald Trump’s speeches exhibited about 27% lexical diversity, while President Joe Biden’s stood at around 26% — both lower than most others in the study. Similarly, Elon Musk, known for his transformative impact on technology and space exploration, displayed a lexical diversity of about 22%. This relatively low word variance could suggest a focused and consistent use of language in their communications.

Lexical Exploration of International Leaders

According to the next part of our study, the linguistic diversity among global figures paints a striking portrait of their priorities and rhetoric styles.

Top Words Among International LeadersTop Words Among International Leaders

Topping our list was Ales Bialiatski, a Belarusian human rights activist whose lexical diversity reached an impressive 56.2%. This means that more than half of the words in his speeches were unique, indicating a richly varied use of language in advocating for human rights. Here’s another view of global leaders’ vocabulary uniqueness — Bialiatski is clearly ahead of the pack.

Lexical Diversity Among World LeadersLexical Diversity Among World Leaders

We found Tibet’s spiritual leader, The Dalai Lama, had the second-highest diversity score (46.4%) among the international leaders studied. He also used “think” in his speeches more than any other word. Joko Widodo of Indonesia focused most on “Indonesia” and ranked third with a diversity score of 44.5%. Both of these figures demonstrated a strong, varied use of language that reflects their respective spiritual and national concerns.

Similarly, Greta Thunberg’s 41% lexical diversity and top word, “climate,” echoed her passionate and varied advocacy for environmental issues.

Linguistic Patterns in Leadership Speeches

Next, we were curious to explore how leaders’ word choices and overall sentiment varied when they spoke. We broke down the most frequent and unique words in their speeches alongside their lexical diversity and sentiment measures for a detailed look at their linguistic patterns.

Top Words Among World LeadersTop Words Among World Leaders

The top 10 most frequent words, led by “people,” “know,” and “one,” reflected a relatable and cognitively engaging narrative woven throughout the speeches we analyzed. These ubiquitous words speak to universal themes and shared human experiences. On the flip side, the overall lexical diversity of 33.6% indicates a thoughtful balance between everyday language and more distinctive choices, like “apprehensive” and “diocese” — words that add a unique flavor to the discourse.

Furthermore, the average sentiment score of all the speeches we analyzed was 0.1443, measured on a scale from -1 (most negative) to 1 (most positive). This indicates a mostly neutral but slightly positive sentiment overall among leaders’ speeches, a rhetorical balancing act that maintains engagement without veering into extremes.

Reflecting on The Power of Words

Honing in on the speech details of global and U.S. leaders uncovers various and enlightening patterns. From James Hansen’s emphatic focus on climate to the stark contrast in lexical diversity between figures like Ales Bialiatski and Elon Musk, our findings provide deep insights into the inner workings of political and public discourse. These insights underscore the profound impact of language in shaping public perception and policy.

Do these differences in lexical diversity indicate that Bialiatski has a wider, more complex vocabulary than other leaders? Or, might Musk’s lower score simply reflect a more straightforward, conversational style — an approach that prioritizes clarity and accessibility for a broader audience?

Ultimately, these different communication styles show just how varied and intricate public discourse can be. They remind us that what makes language powerful isn’t just the words we choose, but also how these words connect with and interest the people listening.


We compiled 861 speeches from the past 20 years from 80 world leaders. We gathered YouTube URLs of each speech and transcribed them to text using BeeCut and YTScribe. We translated all non-English speeches into English before analyzing them. We analyzed a minimum of five speeches per leader. We determined the lexical diversity based on the percentage of unique words in each speech.

Our sentiment analysis relied on the TextBlob library’s API for common natural language processing (NLP) tasks such as identifying word types and word groups, and understanding positive or negative sentiments. We also used the NLTK library for tokenizing text (splitting into words), removing stop words (common words that are usually ignored), and part-of-speech tagging (identifying whether a word is a noun, verb, adjective, etc.).

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