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The Verdict

  by Dan Decker

(about 414 pages)
103,562
total words
74th PERCENTILE
of all the books in our library
19.03%
vividness
1st PERCENTILE
of all the books in our library
12.04%
passive voice
99th PERCENTILE
of all the books in our library
3.12%
all adverbs
65th PERCENTILE
of all the books in our library
1.10%
ly-adverbs
61st PERCENTILE
of all the books in our library
2.03%
non-ly-adverbs
65th PERCENTILE
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
looks like I’m going to prison, and it doesn’t look like there’s anything we can do about it.” “Don’t give up hope.” I studied her. “You’ve been adamant all along you didn’t do this. I believe you. Your attorney believes you. The investigators believe you. We’re all working our hardest to figure out who did this.” “The guy didn’t leave anything behind. There were no fingerprints but mine in that room. His gun is untraceable.” I nodded. It was true the situation seemed bleak. The ironic part was that the primary security cameras in the building had been faulty, so they hadn’t recorded what had happened. Other cameras were still functioning, but they hadn’t picked up anything. Barbara could have easily slipped out of the building through the garage without anybody knowing about Thomas Guyton’s death until he’d been missed. There was even a chance he could have been found by some of his own men, who might have just covered it up and continued to go about their business. I was still confident we’d made the right decision. It would’ve looked much worse for Barbara if the police had arrested her after she’d run. I didn’t even want to contemplate what the consequences might have been if they’d discovered I’d been onsite at the same time. My cover as her attorney seemed to barely be holding up. “Mitch. What am I gonna do?” “You hang on. You hang on tight. We’re gonna figure out who did this.” I didn’t steps before hearing somebody come from behind. I never saw their face. I opened my eyes but couldn’t focus on anything. I could see moving shapes. It was dark. I could also hear voices but couldn’t understand them. Suddenly, there was a loud, annoying sound that I couldn’t identify. “He’s awake!” an excited voice said, somehow cutting through the mental fog that had enveloped my mind. “Are you okay, Mr. Turner?” I inhaled deeply and smelled wet asphalt. Something hit my face. Several more pelted me. My face was wet. Is it raining? My head hurt. Had I fallen? But there was no ice. It was above freezing. “What happened?” I asked, looking around at the people standing anxiously over me. My eyes could now focus on them. One was a kid dressed in black, with a nose ring and a row of piercings in his ear. He had a slash of green in his hair. He kneeled on his skateboard. “Are you okay?” he asked. It was his voice I’d heard upon first opening my eyes. “I’m in pain.” I groaned. It occurred to me the annoying sound was an approaching siren. It was close. “Did you call the police?” The kid nodded. “They’re close. You just hang on.” The kid leaned back as a white vehicle lurched to a halt several feet away, tires squealing, siren wailing. An ambulance. Doors opened, and footsteps came toward me. “What happened?” an authoritative person demanded. “We were just coming to skate

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 2071.24 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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