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Tower Down

  by David Hagberg

(about 369 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:

Susan’s. “The worst of it is that no one has heard anything. The cause is still up in the air.” “It was no accident,” Pete said. “I’d be willing to bet anything on it.” “The engineers are thinking that it was an explosion on one of the lower floors,” Kamal said. “Whatever, it was, terrorists did it,” Courtney said. Kamal shrugged. “ISIS is claiming responsibility. And they may be fierce fighters but they don’t have the sophistication that bin Laden and al-Qaeda had.” “Are you suggesting it wasn’t them?” “On the contrary, there’s no reason to believe it wasn’t them,” Kamal said. “All I’m saying is that they could have gotten into the building somehow, planted explosives on one of the lower floors, and set a fuse or something.” “They would have been caught on the building’s surveillance cameras,” Courtney suggested. “But there’s been absolutely no word on that issue.” “The investigators may not have gotten to the cameras yet,” McGarvey said, watching for a reaction from Kamal. “And they don’t tell us everything. Never do.” “Or if they have, they’re not saying anything about it.” “You would have thought they’d have plastered the dirty bastards’ faces all over the television and newspapers,” Susan said. “Or the Internet,” Kamal said. “Have you heard anything from your subscribers?” McGarvey suppressed a smile. “Lots of ideas but nothing solid.” “Do you think it’ll happen again?” Susan asked. “I haven’t heard anything that makes any sense,” McGarvey said. “I’m asking, because I’ve been she looks like?” McGarvey asked. “I sent a couple of photos to your cell phone. Five-seven, slender, narrow face, short blond hair.” “We’ll watch for her.” In the elevator McGarvey opened the photos Otto had sent. In one the woman was wearing a fairly low cut evening gown, a glass of champagne in her hand, and in the other she was dressed in riding clothes, astride a horse in what could have been Central Park. “She’s an attractive woman,” Pete said, looking over his shoulder. “Forty?” “Well-put-together fifty,” Pete said. The Rose Club was an elegant space on the mezzanine level. Furnished with upholstered chairs and couches grouped around tables, Persian carpets on wood floors, floor-to-ceiling bookcases along the back wall, it opened to the ornate lobby below. A half dozen other people were having drinks. McGarvey ordered a bottle of champagne and three glasses. From where he sat they had a direct line of sight to the main entrance. When the Callahan CFO walked in, she looked nothing like her photographs. She was dressed in baggy khakis and a red Lacoste polo shirt, her hair mostly covered by a baseball cap. Not bothering with the elevator, she’d taken the stairs. McGarvey watched the entrance a bit longer, but she’d not been followed by anyone. He and Pete stood up. Nebel spotted them and came over. “You must be McGarvey,” she said. They shook hands and Mac introduced Pete, then they all sat down. “Your man said that our building

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 1843.64 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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