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Palace of Treason

  by Jason Matthews

(about 697 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:

rezident Yulia Zarubina. The rezident forwarded this latest TRITON report to the Center, eyes only Line KR, which was read by Zyuganov and his deputy, Yevgeny Pletnev, the former with a solar flare of suspicion, the latter with a douche of fear. The only way the Americans could know about Solovyov is if they had another mole. And the “plan to turn him in,” was that a garble? Egorova had returned miraculously from Athens and immediately requested to go on sick leave, claiming she had been attacked on the street and slightly injured. Quick recovery, they said: The CI analysts wanted to talk to her; the director wanted to see her; the Kremlin had summoned her. It all stank to Zyuganov. When Zyuganov eventually heard that Eva Buchina had been found dead in the Athens hotel room, he was truly amazed that she had been bested in a struggle. How could thin, elegant Egorova manage to beat her? Did the skinny ballerina have someone with her for protection? Nothing would be said about it again; it had to be that way. Whatever happened, Eva had missed, and now, as useful as she had been, her demise was in one way welcome. Eva was uncontrollable: She would have been the pet snake, growing in length and girth, that one day starts looking at you through the glass of the terrarium as if you were the mouse. Egorova would have praise heaped on her, and Zyuganov would wait, and watch. He was himself, with the aid of the Americans. And who searches loudest and most noisily for the mole? The mole himself, Mr. President.” Putin’s blue eyes never left her face, but his cerulean halo pulsed, and Dominika knew he believed her. That night in her bedroom at the mansion Dominika could not sleep. The medieval and massively heavy dinners continued: Tonight it had been carved roast beef, veal medallions, buzhenina, baked ham, roast duck and patychky, breaded Ukrainian meat skewers served with a fiery Moldovan adzhika pepper sauce. Cream- and butter-sauce boats sailed in formation between silver candlesticks. There were platters of herring, salmon, and sturgeon in dill and sour cream, and kulebyaka, salmon in puff pastry. Pelmeni and vareniky dumplings were ladled from tureens like hatchlings poured out in a fish farm. Chafing dishes of buttered vegetables; terrines of pork, salmon, and boar; and casseroles of mushrooms, truffle-laden, steaming when spooned out, covered the table. Govormarenko had joked loudly to Putin that delicacies from Ukraine, Georgia, and Moldova were all the more savory, to appreciative laughter from guests with full mouths. A yellow haze enveloped the table. Dominika lay in the four-poster bed under a spectacular rose-colored goose-down comforter, listening to the ticking of an antique ormolu Empire mantel clock and the competing faint buzz of the sea outside her window. She had one more day in the extended weekend and was positively itching to get back to Moscow, to spin up her SRAC equipment, and to send a flurry

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 3482.88 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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