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Chasing the Lion

  by A. J. Tata


(about 406 pages)
101,382
total words
of all the books in our library
46.47%
vividness
of all the books in our library
7.18%
passive voice
of all the books in our library
2.26%
all adverbs
of all the books in our library
0.85%
ly-adverbs
of all the books in our library
1.41%
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
said. “Garrett. What is going on? Where are you?” “You tell me, Kim,” I said. I dropped all pretense of respect. While I didn’t suspect Campbell had anything to do with my situation, I couldn’t be too careful. A day out from inauguration, she would have been in position to influence my captivity. “What do you mean?” she asked. “I was taken to Fort Detrick and detained,” I said. “I’m thinking such an action would have to be sanctioned at the highest levels.” “I have no idea what you’re talking about, Garrett. I’m not president yet. We’re friends,” she said. It was nearly midnight, and she had answered my call amid preparations for a historic inauguration. “You’re in all the briefings. You’re president in thirty-six hours.” “But I’m not in charge. Is that why you’re calling from a different phone?” she asked. “Yes. I escaped,” I said. “You escaped? What are you talking about?” “Why was Melissa interrogated by Dariush Parizad?” I asked. “Garrett, are you okay?” “I’m definitely not okay,” I said. “I’m being chased by government goons, and I’m wondering about the status of my team. Melissa’s dead and gone, and Parizad may have had something to do with it.” “We need to meet,” she said. “I don’t think that’s a good idea.” I continued to assess my surroundings by slowly turning in a circle. The man at my feet coughed and twitched. I kicked him in the head to help him sleep better. “I’m the president-elect. You’re to the ground. We both eased Van Dreeves from the tree; then we knelt, listening to our environment. The heat pump hummed lightly in the distance. A faraway siren wailed in distinctive European duotone fashion. A musty aroma of earth and tree sap hung in the air as a small animal, probably a squirrel, jumped from limb to limb high in the trees. A red fox shot across the lens of my night vision goggles as I snapped them into place. I tapped Hobart and Van Dreeves, pointed at my chest, then pointed at the southwest corner, my target, about fifty meters away. Each man gave me a thumbs-up, and I moved in a low crouch to the south. The large home in the center of the compound was to my right. It was two stories with a steep, pitched roof of dark shingles. The façade was stucco, sporting tall, rectangular windows framed by wide boards, most likely brown. The ridge of the roof ran from east to west, perpendicular to my movement, which indicated the home was wider than it was deep. Some of the windows on the first floor showed light deep within the interior. I continued to pick a path among the deadfall, groomed shrubs, and trees that minimized noise. I stopped at the base of a spruce tree, its low branches forming a solid skirt that hung three feet above the ground. Low crawling beneath the tree, I saw the shiny eyes of a fox glistening

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 2027.64 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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