In season 1, episode 4 of the hit Netflix show called Wednesday (2022), teenager Wednesday Addams is discussing plans for going to a school dance with a certain someone. In typical Wednesday fashion, she redirected the conversation from her feelings to her priorities. This was highlighted when Wednesday said that it's not her fault for being unable to interpret "emotional morse code." Her lack of interest in discussing feelings and emotions and word choices reflect the Emotion attribute.
It’s time to go beyond legacy methods of detecting plagiarism or AI-generated content. The Linguistic Fingerprint™ by LINGA is a revolutionary language analysis technology that sits at the intersection of linguistics, psychology, and computer science. It’s an entirely new way of identifying a person in the form-factors of speech and writing. Linguistic self-discovery awaits.
In season 6, episode 6 ("Hop, Skip and a Week") of Sex and the City (2003), Charlotte finds Harry at a singles event at the synagogue and he proposes to her. Before he did, Charlotte let Harry know how much she missed him and adored him. Her strong affection and teary-eyed condition demonstrates the Emotion attribute.
In Season 7, Episode 6 ("Beyond the Wall") of the hit HBO show Game of Thrones (2017), a battle rages between Jon Snow's group and the White Walkers, led by the Night King. As the dragon Viserion is flying around, the Night King is handed a spear by one of his own soldiers. The Night King then aims at the dragon and strikes it, taking the beast down. Later, the Night King walks up to the line separating the two camps and makes eye contact with Snow as he raises his arms. The Night King never speaks or makes any sounds, thus giving no verbal information to anyone, and this demonstrates an extreme-low example of the Volubility attribute.
Feels – Laugh. Learn. Language. Our mission is to promote objective analysis of real 🗣️human language via fun, short videos and the 🪄magic of psycholinguistics. We do this by organizing, analyzing, and making freely available a growing collection of Feels, or highly structured short-form videos that explain the contents of a given conversation between two or more people. Plus GIFs and lots of action.
In the hit Netflix documentary Famous Last Words: Dr. Jane Goodall (2025), Jane Goodall shared her thoughts on a variety of subjects before she passed away on October 1, 2025. Throughout the promotional clip, Goodall reflects on her life, accomplishments, and trials, all before giving advice to the next generation about living a meaningful life. The depth and extent of information shared by Goodall demonstrates a well above-average example of the Volubility attribute.
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.