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Double Cross

  by James Patterson


(about 283 pages)
70,813
total words
of all the books in our library
36.02%
vividness
of all the books in our library
8.65%
passive voice
of all the books in our library
3.93%
all adverbs
of all the books in our library
1.26%
ly-adverbs
of all the books in our library
2.67%
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
think they’ll be cool with it?” Thompson pointed an index finger at him. “I haven’t done anything illegal. I’m well within my rights.” “Yeah,” Sampson said. “That really wasn’t my question, though. I just wondered how your employer might feel about SerialTimes. net.” “You have no right to use that information if I haven’t broken the law.” “In fact, we do,” I put in. “But we’re assuming we won’t have to, because we’re assuming you’re going to tell us where that message came from.” “First of all, Detective, I couldn’t tell you if I wanted to. DCAK’s not an idiot, okay? Haven’t you figured that out for yourselves by now? And second, I’m not fifteen years old. You’ll have to do better than you’re doing. A lot better.” “Do you mean like a subpoena for your home system?” Bree asked. “We can do that.” Thompson adjusted his glasses and sat back now, beginning to like the position he was in. I could see why. I wasn’t sure that we could get a subpoena for his home system, much less arrest him. “Actually, no. Assuming you don’t have your subpoena with you—probably because you were just too damn eager to get over here—I can make sure that my server doesn’t have anything more than Peanuts cartoons on it by the time you get there. And I don’t even have to leave this chair to do it.” He looked up at us, calm as could be now. “You obviously don’t stories. Some particularly high-spirited fans, the Hogettes, were singingHail to the Redskinsslightly off-key and with off-color lyrics liberally sprinkled in, which seemed weird since there were lots of kids in the crowd. The so-called superfans wore bright-colored wigs and polka-dot blouses and plastic hog snouts. Some of them were smoking extra-long cigars, which enhanced their piggy image. He hadn’t gone quite that far with his outfit, but he was wearing a Redskins cap and jersey, and he had his face painted burgundy and gold, the home team’s colors. His persona was that of a disgruntled fan named Al Jablonski. A good, solid role to play. Ninety-one thousand fans packed the stadium, all waiting for Al Jablonski. They just didn’t know it yet. Close to game time, the First Ladies of football scampered onto the Technicolor-green fieldmasses of flying hair and pom-poms, skimpy red halter tops and white short shorts. Family entertainment at its most all-American, the killer couldn’t help thinking. “Are you ready for some foot-ball?” he shouted from the stands. “Some foos-ball!” A few fans around him joined in or laughed at the familiar line from the Monday Night Football TV show. Al Jablonski knew his audience, and his game. The control booth for the stadium scoreboard was located underneath the huge sign. He knew the way and arrived there in time for the national anthem to be sung by a soprano marine from the base down in Quantico. Al Jablonski knocked on the metal door

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 1416.26 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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