feeldb

Goodbye Mr. Loser

2015 · 104 min · Director: Da-Mo Peng, Fei Yan, Yan Fei, Peng Damo ★ IMDb 6.7 · 4,253 ratings

Show plot summary (may contain spoilers)

After attending a wedding of his high school crush Qiuya, Xia Luo gets drunk embarrassing himself and angers his wife Ma Dongmei.

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Emotional fingerprint

Goodbye Mr. Loser (2015) — Movie. Top emotional signals: Humor 35%, Nostalgia 25%, Joy 20%. Source: AI estimate. Shares describe emotional signal, not percentages of people. Your experience may differ. How we measure

35%Humor
20%Joy
  1. Humor35%
  2. Joy20%

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 Humor, Nostalgia, Joy. Genres: Comedy, Drama, Fantasy. AI estimate.

Is it worth watching?

Consider this title if you are looking for Humor, Nostalgia and Joy. Its IMDb rating of 6.7/10 suggests a generally positive audience reception. For these leading emotions, its Feel Score is 69/100.

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Emotional journey

AI · from subtitles
  • Humor
  • Love
  • Sadness

Not enough subtitle evidence

No dominant emotion

Dominant emotion: Humor

Dominant emotion: Love

Dominant emotion: Sadness

Dominant emotion: Nostalgia

Humor
Love
Sadness
Emotional journey Emotional intensity over the film’s runtime, from quieter to stronger on a fixed 0–1 scale. Hatched intervals have insufficient subtitle evidence. The table provides the values. Not enough subtitle evidence
Ending hidden

Dominant emotion

Intensity dips around 3 %, then dips around 28 %, then peaks around 93 %.Ending hidden

Emotional journey
%Approximate minuteDominant emotionOverall intensityHumorLoveSadness
2.5 %≈ 3′Humor58 %58 %3 %17 %
7.5 %≈ 8′Humor61 %58 %8 %24 %
12.5 %≈ 13′Humor63 %53 %11 %30 %
17.5 %≈ 18′Humor60 %51 %14 %24 %
22.5 %≈ 23′Humor55 %51 %14 %15 %
27.5 %≈ 29′Humor53 %49 %16 %15 %
32.5 %≈ 34′Humor53 %48 %21 %19 %
37.5 %≈ 39′Humor55 %49 %24 %16 %
42.5 %≈ 44′Humor57 %50 %29 %15 %
47.5 %≈ 49′Humor55 %48 %30 %20 %
52.5 %≈ 55′Humor56 %40 %34 %26 %
57.5 %≈ 60′Love59 %34 %36 %30 %
62.5 %≈ 65′Humor56 %35 %26 %34 %
67.5 %≈ 70′Sadness57 %35 %30 %41 %
72.5 %≈ 75′Love64 %30 %50 %48 %
77.5 %≈ 81′Nostalgia70 %31 %60 %50 %
82.5 %≈ 86′Love73 %40 %50 %51 %
87.5 %≈ 91′Sadness76 %36 %48 %57 %
92.5 %≈ 96′Love80 %20 %63 %60 %
97.5 %≈ 101′Not enough subtitle evidenceNot enough subtitle evidenceNot enough subtitle evidenceNot enough subtitle evidenceNot enough subtitle evidence
Riches to rags

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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Afterward Reveal viewer responses · may contain spoilers

Viewer responses about feelings after the title ended, separate from feelings while watching. No AI estimates.

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Cast

ST
Shen Teng

夏洛

LM
Li Ma

ML
Ma Li

马冬梅

ZY
Zheng Yin

YZ
Yin Zheng

袁华

A
Allen

大春

WZ
Wang Zhi

秋雅

SY
Song Yang

张扬

TY
Tian Yu

王老师

CY
Chang Yuan

孟特

LP
Li Ping

夏洛妈

LL
Lee Li-Chun

校长

ZY
Zhang Yiming

婚礼司仪

MX
Ma Xiaoqian

那姐

KL
Kun Liu

陈凯

HC
Huang Cailun

小舅子

CH
Chen Haoming

贝贝

WC
Wang Chengsi

同学

LJ
Liu Jin

新郎

宁
宁幻秋

王姨

ZZ
Zhou Zhanchi

台湾选手

常
常世奎

楼下老人

Details

Directed by
Da-Mo Peng, Fei Yan, Yan Fei, Peng Damo
Written by
Da-Mo Peng, Fei Yan, Yan Fei, Peng Damo
Type
Movies
Released
2015
Runtime
104 min
Country
China
Ratings
★ IMDb 6.7 · 4,253 ratings
Feels mostly
Humor
Data updated

Where to watch

Data: JustWatch · Wikidata

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Emotion overlap shows mood similarity, not a prediction of enjoyment.