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The Goldfinch

  by Donna Tartt


(about 1,177 pages)
294,284
total words
of all the books in our library
55.50%
vividness
of all the books in our library
7.47%
passive voice
of all the books in our library
3.57%
all adverbs
of all the books in our library
1.31%
ly-adverbs
of all the books in our library
2.26%
non-ly-adverbs
of all the books in our library

clippings from this book

We’ve analyzed hundreds of millions of words, from thousands of different authors, training our linguistic models to recognize the most vivid words in the English language… the words that create the most intense sensory experiences: colors, textures, sounds, flavors, and aromas.

Based on our analysis, we’ve scanned through the pages of this book to find the two pages at the extremes, both the most-passive and the most-vivid pages, so that you can compare them side-by-side and see the difference:

MOST PASSIVE PAGE
MOST VIVID PAGE
All these tiny signs that I ignored, or didn’t fully recognize—everything could have been different if only I’d been paying attention—like, Welty was trying so hard to get me to go earlier, he must have asked a dozen times, it was like he had a sense of it himself, something bad going to happen, it was my fault we were even there that day—” “At least you hadn’t been expelled from school.” “Were you expelled?” “Suspended. Bad enough.” “It’s weird to think—if it had never happened. If we hadn’t both been there that day. We might not know each other. What do you think you would be doing now?” “I don’t know,” I said, a bit startled. “I can’t even imagine.” “Yeah, but you must have an idea.” “I wasn’t like you. I didn’t have a talent.” “What’d you do for fun?” “Nothing that interesting. The usual. Computer games, sci-fi stuff. When people asked me what I wanted to be, I’d usually be a smart ass and say I wanted to be a blade runner or something like that.” “God, I’m so haunted by that movie. I think about Tyrell’s niece a lot.” “What do you mean?” “That scene where’s she’s looking at the pictures on the piano. When she’s trying to figure out whether her memories belong to her or Tyrell’s niece. I go back through the past too, only looking for signs, you know? Things I should have picked up on, but missed?” “Listen, you’re waterfalls of neon, electricity blazing and pulsing and cascading down in bubbles all around us, Boris’s upturned face glowing red and then gold in the crazy drench of lights. Inside the Venetian, gondoliers propelled themselves down a real canal, with real, chemical-smelling water, as costumed opera singers sang Stille Nacht and Ave Maria under artificial skies. Boris and I trailed along uneasily, feeling shabby, scuffing our shoes, too stunned to take it all in. My dad had made reservations for us at a fancy, oak-panelled Italian restaurant—the outpost of its more famous sister restaurant in New York. “Order what you like, everyone,” he said, pulling out Xandra’s chair for her. “My treat. Go wild.” We took him at his word. We ate asparagus flan with shallot vinaigrette; smoked salmon; smoked sable carpaccio; perciatelli with cardoons and black truffles; crispy black bass with saffron and fava beans; barbecued skirt steak; braised short ribs; and panna cotta, pumpkin cake, and fig ice cream for dessert. It was by leaps and fathoms the best meal I’d eaten in months, or maybe ever; and Boris—who’d eaten two orders of the sable all by himself—was ecstatic. “Ah, marvelous,” he said, for the fifteenth time, practically purring, as the pretty young waitress brought out an extra plate of candies and biscotti with the coffee. “Thank you! Thank you Mr. Potter, Xandra,” he said again. “Is delicious.” My dad—who hadn’t eaten all that much compared to us (Xandra hadn’t either)—pushed his plate

emotional story arc

Click anywhere on the chart to see the most significant emotional words — both positive & negative — from the corresponding section of the text…
This chart visualizes the the shifting emotional balance for the arc of this story, based on the emotional strength of the words in the prose, using techniques pioneered by the UVM Computational Story Lab. To create this story arc, we divided the complete manuscript text into 50 equal-sized chunks, each with 5885.68 words, and then we scored each section by counting the number of strongly-emotional words, both positive and negative. The bars in the chart move downward whenever there’s conflict and sadness, and they move upward when conflicts are resolved, or when the characters are happy and content. The size of each bar represents the positive or negative word-count of that section.

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