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Zero Hour

  by Tom Clancy

(about 372 pages)
total words
of all the books in our library
of all the books in our library
passive voice
of all the books in our library
all adverbs
of all the books in our library
of all the books in our library
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:

be more careful. If I could pick him up, so could the guy in the van,” Ricci said. He shrugged. “There’s some other meaning of ‘busy’ you want to discuss, I’m all ears.” Noriko was quiet a moment. “I got your advance billing,” she said. “The tough-guy attitude. The lone wolf bit. But I hadn’t heard what a truly pathetic human being you are.” Ricci’s smile slashed at her. “Guess we’ll stick to talking work,” he said. Noriko had kept looking steadily into his eyes, and she still didn’t flinch. “I don’t care how you operate in San Jose, or what you’ve gotten away with under people’s noses out there,” she said. “But this is my city, and I’ve got no long leashes for anybody. Heading out on a surveillance last night wasn’t something you should have done without authorization. It wasn’t something you had any right doing in secret… and just so there’s no confusion, my problem isn’t with you getting your neck hacked open without anybody having a clue what’s happened. The important thing is that you could have put our whole investigation in jeopardy.” Ricci stared back across the desk, shrugged his shoulders. “I was worried about keeping secrets from you, I’d have gotten myself a Hertz rental car instead of ticketing that one out of the req lot, where I knew you’d make sure somebody would notice.” He shrugged again and gestured toward the file folder that had remained spread open in front of her. “What’s a single inference or conclusion. Tied into knots of frustration that made his back and shoulders ache, Lenny decided he’d stretch his lunch hour and walk a few blocks over to Chinatown to purchase his nerve medicine from Yan. The Snow Mountain Mart on Canal Street was a multistoried Far Eastern emporium that had been a neighborhood fixture seemingly forever, its shelves, counters, and clothes racks displaying imported goods in astonishing quantities—a wild and colorful diversity of curios, kimonos, furniture, bedding, beaded curtains, bird cages, parasols, Chinese lanterns, enormous ceremonial masks and paper dragons, miniature jade Buddhas and crystal lotus vases, incense and incense burners, chopsticks and chopstick holders, bamboo steamers, electric rice cookers, green tea sold loose by the ounce, soap bars wrapped together in packages of several dozen, folk instruments, electric guitars, harmonicas, noisemakers, nail clippers, nose-hair trimmers, magnifying glasses, portable CD players, personal pagers, radar detectors, cameras, film, generic batteries… lots of batteries… name the item, there was a decent chance you’d find it somewhere in stock. Today Lenny had found Yan in his usual spot at the end of a cluttered aisle of herbal remedies around the corner from the music and video department. A bald, bespectacled guy in his seventies, Yan always wore a long white frock unbuttoned over his slight paunch. Lenny had been paying him occasional visits for maybe five, six years, but wasn’t absolutely sure if Yan was his first or last name. Lenny was likewise unsure whether the white coat

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 1859.14 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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