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In season 3, episode 4 ("Conflict of Interest") of the hit TV show Suits (2013), Harvard-trained corporate attorney Louis Litt visits an investment banker's office in order to confront Tony G. about his business empire. Louis encounters another man who intends to keep Louis away from Tony. Louis then drops several folders and has a brief but powerful conversation that explains his leverage. The investment banker's gatekeeper rebukes all of Louis' leverage and indicates that Tony is not present, but in Monaco. Between the insults exchanged, the banker smiles and says "We're not lawyers ... we didn't go to Harvard, we went to Wharton (notable business school) and we saw you coming a mile away." Louis feels defeated as the scene ends. Despite Louis' formal attire and direct clarity, he uses colorful language on two occasions and calls his counterpart "Spank." These juvenile insults demonstrate an above-average example of the Formality attribute.
Feels Score: 6 in
LINGA – Our business is language itself™

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LINGA is a psycholinguistics technology company that provides authorship identity verification as a service via our proprietary Linguistic Fingerprint™ technology. Through the LINGA web application, guests complete writing prompts in order to build their Linguistic Fingerprint™. Once built, guests can discover their true selves as expressed in the form-factor of writing. Guests can also scan written documents in order to generate a unique Authorship Verification Certificate, which is certified by LINGA and available as a downloadable PDF file. This certificate indicates whether or not the scanned document matches our guest's Linguistic Fingerprint™, and is backed by our $1,000,000 Authorship Integrity Defense Guarantee (SM · Terms apply). Individual guests can use LINGA to verify and defend authorship integrity for important written works like books, articles, papers, speeches, and more.

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Mister Terrific does not understand why Lois thinks his spheres are circles.
Feels Score: 8 in

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On February 13, 2005, superstar rapper Kanye West won a Grammy award at the 47th Grammy Awards for Best Rap Album as a result of his previously released album called College Dropout (2004). Kanye approached the podium to deliver his acceptance speech in a white outfit, and proceeded to give thanks to several individuals. The highlight of his speech was at the end when Kanye announced "everybody wanted to know what I would do if I didn't win. I guess we'll never know." Overall, the speech was brief, lacked details, and ended on a cliffhanger about what he would do. Kanye West's reluctance to share more information demonstrate a below-average example of the Volubility attribute.
Feels Score: 4 in

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In the hit Netflix movie Happy Gilmore 2 (2025), golf caddie Oscar Mejías is helping golfer Happy Gilmore finish a tough hole during a golf tournament. As Happy asks Oscar for his help, Oscar's responses are brief despite what's going on in his imagination. Oscar's low overall volume of details and information shared demonstrate the Volubility attribute.
Feels Score: 3 in

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In the hit poker movie Rounders (1998), soon-to-be dropout law school student Mike McDermott is facing a stressful poker game against Teddy KGB, a Russian mobster with his own poker club. Mike previously lost his funds to Teddy KGB and has loan sharks after him. During the final poker showdown between the two, Mike is folding good hands because he notices that Teddy KGB has him beat when he splits open and eats an Oreo cookie. This cue allowed Mike to dominate most of the hands until Teddy KGB figured it out. The outburst indicates that the Russian realized his own tell. In using phrases like "Lays down a monster. The f*** did you lay that down. Should have paid me off ...," Teddy vocalizes his own inability to use cues, logical reasoning, and predictive consideration. This is reinforced by the expletives. Teddy KGB's self-admitted mistake after speaking in a confident manner demonstrates a near-bottom example of the Inference attribute.
Feels Score: 2 in

Ultra Low

0–5% percentile
An ultra low attribute score is exceptionally rare because it represents 5% of the entire population. In a room with 100 other people, a person with an ultra low attribute score would be lower than 95 of them and higher than none of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

Very Low

5–10% percentile
A very low attribute score is rare because it represents 5% of the entire population. In a room with 100 other people, a person with a very low attribute score would be higher than five of them and lower than 90 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

Low

10–20% percentile
A low attribute score is somewhat uncommon and represents 10% of the entire population. In a room with 100 other people, a person with a low attribute score would be higher than ten of them and lower than 80 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

Slightly Low

20–40% percentile
A slightly low attribute score is common and represents 20% of the entire population. In a room with 100 other people, a person with a slightly low attribute score would be higher than 20 of them and lower than 60 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

Average

40–60% percentile
An average attribute score is typical and represents 20% of the entire population. In a room with 100 other people, a person with an average attribute score would be higher than 40 of them and lower than 40 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

Slightly High

60–80% percentile
A slightly high attribute score is common and represents 20% of the entire population. In a room with 100 other people, a person with a slightly high attribute score would be higher than 60 of them and lower than 20 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

High

80–90% percentile
A high attribute score is somewhat uncommon and represents 10% of the entire population. In a room with 100 other people, a person with a high attribute score would be higher than 80 of them and lower than 10 of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

Very High

90–95% percentile
A very high attribute score is rare because it represents 5% of the entire population. In a room with 100 other people, a person with a very high attribute score would be higher than 90 of them and lower than five of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.

Ultra High

95–100% percentile
An ultra high attribute score is exceptionally rare because it represents 5% of the entire population. In a room with 100 other people, a person with an ultra high attribute score would be higher than 95 of them and lower than none of them.
Note: Feels uses a 9-point scoring scale that ranges from Ultra Low to Ultra High according to a normal distribution. See our methodology.