Can't Buy My Love
2017 · 86 min · Director: Farhad Mann
Show plot summary (may contain spoilers)
Lilly, a hard working EMT, is only focused on her job and getting into medical school. So when wealthy slacker Jeff enters her life it throws her off course. At first, Lilly wants nothing to do with Jeff or his sense of entitlement and lack of direction. As she spends more time with him, she starts to realize that you can't always judge a book by its cover.
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Emotional fingerprint
Can't Buy My Love (2017) — Movie. Top emotional signals: Love 30%, Humor 25%, Empathy 20%. Source: AI estimate. Shares describe emotional signal, not percentages of people. Your experience may differ. How we measure
- Love30%
- Humor25%
- Empathy20%
- Surprise15%
- Satisfaction10%
AI estimate · no viewer votes yet
How we measure
AI estimate
Share of emotional signal, not the percentage of people or a probability of enjoyment.
Each viewer contributes one response, split equally across their selected emotions. When available, an automated estimate supplies a prior that is blended with those responses. Counts include distinct viewers, not automated estimates or archived anonymous votes. Fewer than 3 viewers is an early estimate; more responses do not guarantee certainty.
Why it feels this way
The strongest emotional signals are Love, Humor, Empathy. Genres: Romance, Family. AI estimate.
Is it worth watching?
Consider this title if you are looking for Love, Humor and Empathy. Its IMDb rating of 6.3/10 suggests a generally positive audience reception. For these leading emotions, its Feel Score is 65/100.
How did it make you feel?
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Pick up to three · 0/3
Emotional journey
- Love
- Hope
- Sadness
Not enough subtitle evidence
No dominant emotion
Dominant emotion: Humor
Dominant emotion: Love
Dominant emotion: Sadness
Dominant emotion: Hope
Dominant emotion: Tension
Dominant emotion
| % | Approximate minute | Dominant emotion | Overall intensity | Love | Hope | Sadness |
|---|---|---|---|---|---|---|
| 2.5 % | ≈ 2′ | Humor | 27 % | 9 % | 15 % | 2 % |
| 7.5 % | ≈ 6′ | Humor | 31 % | 11 % | 18 % | 3 % |
| 12.5 % | ≈ 11′ | Love | 32 % | 15 % | 18 % | 3 % |
| 17.5 % | ≈ 15′ | Humor | 33 % | 14 % | 17 % | 9 % |
| 22.5 % | ≈ 19′ | Sadness | 38 % | 16 % | 24 % | 16 % |
| 27.5 % | ≈ 24′ | Hope | 43 % | 27 % | 34 % | 18 % |
| 32.5 % | ≈ 28′ | Hope | 45 % | 36 % | 38 % | 16 % |
| 37.5 % | ≈ 32′ | Love | 45 % | 34 % | 32 % | 12 % |
| 42.5 % | ≈ 37′ | Humor | 44 % | 23 % | 19 % | 18 % |
| 47.5 % | ≈ 41′ | Sadness | 48 % | 21 % | 21 % | 34 % |
| 52.5 % | ≈ 45′ | Hope | 57 % | 38 % | 40 % | 31 % |
| 57.5 % | ≈ 49′ | Love | 62 % | 52 % | 48 % | 15 % |
| 62.5 % | ≈ 54′ | Hope | 64 % | 53 % | 54 % | 8 % |
| 67.5 % | ≈ 58′ | Hope | 70 % | 43 % | 48 % | 13 % |
| 72.5 % | ≈ 62′ | Tension | 70 % | 26 % | 25 % | 33 % |
| 77.5 % | ≈ 67′ | Sadness | 62 % | 17 % | 18 % | 48 % |
| 82.5 % | ≈ 71′ | Sadness | 59 % | 28 % | 28 % | 36 % |
| 87.5 % | ≈ 75′ | Love | 62 % | 43 % | 41 % | 22 % |
| 92.5 % | ≈ 80′ | Hope | 66 % | 52 % | 51 % | 21 % |
| 97.5 % | ≈ 84′ | Love | 74 % | 65 % | 55 % | 20 % |
Cinderella
Rags to riches: The emotional tone rises from negative to positive.
Riches to rags: The emotional tone falls from positive to negative.
Man in a hole: The tone falls, then recovers.
Icarus: The tone rises, then falls.
Cinderella: The tone rises, falls, then rises again.
Oedipus: The tone falls, rises, then falls again.
AI reading of the film’s English subtitles (dialogue and sound cues) — not viewer votes. How we measure ↗
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Premise onlyAfterward Reveal viewer responses · may contain spoilers
Viewer responses about feelings after the title ended, separate from feelings while watching. No AI estimates.
0 responses so far
Feeling details appear after at least 3 viewers respond, to protect privacy.
Cast
Nick
Megan
Vince
Keely
Finn
Dr. Richwood
Crystal
Carter
Robin
Fred
Calandre
Harper
Helen
Details
- Directed by
- Farhad Mann
- Written by
- Cara J. Russell
- Type
- Movies
- Released
- 2017
- Runtime
- 86 min
- Country
- United States
- Ratings
- Feels mostly
- Love
- Data updated
Where to watch
Data: JustWatch
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More like this →Emotion overlap shows mood similarity, not a prediction of enjoyment.





