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Spymaster

  by Brad Thor


(about 371 pages)
92,819
total words
of all the books in our library
35.77%
vividness
of all the books in our library
9.23%
passive voice
of all the books in our library
2.81%
all adverbs
of all the books in our library
0.97%
ly-adverbs
of all the books in our library
1.84%
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
had a high level of confidence that we knew what was happening.” “A confidence that you never shared with NATO. In fact, you didn’t do anything until your equipment was targeted.” “Wrong,” corrected Harvath. “We looped in the Supreme Allied Commander from the start.” That was a piece of the puzzle she hadn’t been aware of. “Then why didn’t he do something?” “Communiqués were sent to all NATO personnel after the first attack. Everyone was warned. You know that.” “But the communiqués didn’t contain all of the information. I saw them. None of us knew what you knew.” Harvath shook his head. “It wouldn’t have made a difference.” “You don’t know that,” she pushed back. Harvath could see the deaths weighing on her. “Maybe the diplomats wouldn’t have been able to do anything with it,” she pressed, “but on the intelligence side, it could have helped the investigation. Maybe I could have done something with it.” “There’s nothing you could have done.” “You don’t know that.” “Maybe. But we couldn’t risk it.” “Why not?” “Because NATO is shot through with Russian spies,” said Harvath. “It has been since the beginning. That’s its greatest weakness. It’s like Swiss fucking cheese. For every Russian you uncover, there are two more hiding somewhere else. We couldn’t risk their learning what we know.” Monika looked at him defiantly. “How do you know I’m not a spy?” she demanded. “To be honest, I don’t.” She glared at him. “Then why am I here?” It was It was decorated in rich mahoganies, ornate carpets, red silk draperies, and ornate gold brocade. The only thing that outdid the décor was the menu. It included every Russian staple imaginable—from borscht and wild boar to kulebiaka and shashlik. Not to be outdone in the food department, the Russia House boasted an astounding collection of vodka. It was not only one of the best in D.C., but it was one of the best in the United States. The vodka menu listed more than forty different kinds from Russia and twenty from Poland, and included vodkas from Moldova, Ukraine, Lithuania, Estonia, England, Sweden, Holland, and even Israel. On top of everything else, the Russia House was less than a mile and a half from the Polish Embassy. She found Kopec at a small table on the second floor, in the cozy, seductively lit “Czar’s Bar.” In his typical fashion, he had started without her. A bottle of Chopin potato vodka sat next to a silver serving dish filled with crushed ice and chilled caviar. It was encircled on a plate by small Russian pancakes known as blini. A colorful trio of minced red onion, chopped egg, and sour cream sat on a plate to the side. When Ryan entered, Kopec stood and watched her as she walked over. She looked stunning. Though he wasn’t an expert on designer labels, he assumed the suit she was wearing was Italian. If he had to guess, Armani. It was sleek and black

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 1856.38 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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