航行金税盘

by Jordan Dworkin

通过乔丹德沃金

通过陌生事物的情感进行统计好奇心航行 (A Statistical Curiosity Voyage Through the Emotion of Stranger Things)

Like a few million other people, I spent the weekend of Oct. 27, 2017, watching Stranger Things 2, and the following week dealing with minor withdrawal.

像其他几百万人一样，我度过了2017年10月27日的那个周末，看了《陌生人事物》 2，接下来的一周是处理小额提款的事情。

To cope, and to transition back into my research, I decided to apply the statistical paddles of sentiment analysis and network analysis to the scripts from Seasons 1 and 2, and to consider what the results might say about the emotional structure of each episode of the show.

为了应对并过渡到我的研究，我决定将情感分析和网络分析的统计技巧应用于第1季和第2季的剧本，并考虑结果可能对每个情节的情感结构有何影响表演。

To begin understanding the emotion of Stranger Things, I downloaded the scripts and assigned a numeric value to each word based on its positive or negative valence. The simplest aspect of emotion to look at is the average valence of each episode. This metric is obtained by taking the mean sentiment value of all words in an episode, regardless of the order in which they are spoken.

为了开始理解“陌生人事物”的情感，我下载了脚本并根据其正负价为每个单词分配了一个数值。 情绪最简单的方面是每个情节的平均化合价。 通过获取情节中所有单词的平均情感值来获得此度量标准，而不管其说出的顺序如何。

These values are generally what you might expect from a show about missing children and inter-dimensional monsters, with most episodes showing more negative than positive emotion.

这些值通常是您可能会从有关失踪儿童和多维怪物的节目中获得的，大多数情节表现出的负面情绪多于正面情绪。

The most interesting finding seems to be the degree to which the less-than-well-received Chicago episode — number 15 — stands out as being more than twice as negative as any other. This may be due to a combination of its dark plot and the lack of any comedic relief from the Hawkins gang.

最有趣的发现似乎是在不那么受欢迎的芝加哥情节(第15号)中，其负面影响程度是其他任何原因的两倍以上。 这可能是由于其阴暗的情节以及霍金斯团伙缺乏任何喜剧性的救济所致。

However, there is a lot of temporal information missing from those averages, so let’s take a look at how the emotional tone changes over time within episodes.

但是，这些平均值缺少很多时间信息，因此让我们看一下情节中情绪随时间的变化。

One way to do this is to use the sliding window technique. For this version of a sliding window, we’re going to take the average of the 40 words surrounding a central word, then continually shift one word over and take a new average. This yields a smooth trajectory of the emotional valence over the course of each episode.

一种方法是使用滑动窗口技术。 对于此版本的滑动窗口，我们将取围绕一个中心单词的40个单词的平均值，然后连续将一个单词移至另一个平均值上。 这在每个情节的过程中产生了一个平稳的情绪价轨迹。

While it’s difficult to learn much from simply looking at the trajectories, a few things do pop out.

虽然仅通过观察轨迹很难学到很多东西，但确实有一些事情发生了。

First, with only one exception (you guessed it, the Chicago episode), even the darkest episodes typically have a few scenes that are heavy on positive sentiment.

首先，只有一个例外(您猜对了，芝加哥情节)，即使是最黑暗的情节，也通常会出现一些正面情绪沉重的场景。

Second, of the 17 episodes, only three end on a high note: the Season 1 finale, the Season 2 premiere, and the Season 2 finale.

其次，在这17集中，只有三集以高调结束：第1季大结局，第2季首演和第2季结局。

Third, there is a lot of variation in how these episodes are structured, and they don’t seem to follow a clear emotional pattern. Let’s roll with that last point and see if we can characterize some of that variation.

第三，这些情节的结构有很多差异，而且它们似乎并没有遵循明确的情感模式。 让我们继续讨论最后一点，看看我们是否可以表征某些变化。

As a baseline for comparison, we can check whether the season in which an episode appears contains enough information to explain similarities and differences across emotional trajectories.

作为比较的基准，我们可以检查某个情节出现的季节是否包含足够的信息来说明情绪轨迹之间的相似性和差异。

Unsurprisingly, it does not (p = .34). In general, both Season 1 and Season 2 have a great deal of variability in their episodes’ structures. The average trajectories both tend to hover around neutral.

毫不奇怪，事实并非如此( p = .34) 。 总的来说，第1季和第2季在情节结构上都有很大的可变性。 平均轨迹都倾向于在中性附近徘徊。

To find a better classification, let’s first conceptualize the relationships between episodes as a network by calculating the temporal correlation for every pair of episodes. In this context, the nodes are episodes and the edges represent the degree to which pairs show similar patterns of emotion.

为了找到更好的分类，我们首先通过计算每对情节的时间相关性，将情节之间的关系概念化为网络。 在这种情况下，节点是情节，边缘表示配对显示相似情绪模式的程度。

Once this network is constructed, we can apply methods borrowed from graph theory to find communities in our data. In this case, three distinct episode groups are revealed, and the within-group similarity is greater than would be expected to occur by chance (p < .001).

一旦建立了这个网络，我们就可以应用从图论那里借来的方法来在我们的数据中寻找社区。 在这种情况下，揭示了三个不同的情节组，并且组内相似性大于偶然发生的预期值( p < .0 .01)。

Now that we’ve found our communities of interest, let’s map them back onto the emotion trajectories to see if they capture any more of the variability.

现在我们已经找到了我们感兴趣的社区，让我们将它们映射回情感轨迹，以查看它们是否捕获了更多的可变性。

Unlike the division by season, these average group trajectories appear to describe three distinct patterns. They also seem to track fairly well onto their underlying episode trajectories.

与按季节划分不同，这些平均群体轨迹似乎描述了三种不同的模式。 他们似乎还可以很好地追踪其潜在的情节轨迹。

Looking at the average patterns, we can see that group 1 contains episodes that begin and end with neutral emotion and have slow fluctuations in the middle, group 2 contains episodes that begin with negative emotion and gradually climb towards a positive ending, and group 3 contains episodes that begin on a positive note and oscillate downwards towards a darker ending.

查看平均模式，我们可以看到第1组包含以中性情绪开始和结束并且中间缓慢波动的情节，第2组包含以消极情绪开始并逐渐向积极结局发展的情节，第3组包含情节以积极的音调开始，然后朝着一个黑暗的结局向下波动。

In addition to plotting the communities of emotion patterns, let’s take a look at the full network structure.

除了绘制情感模式的社区之外，我们来看一下完整的网络结构。

The first thing that jumps out is that each group contains an approximately equal number of episodes from Season 1 and Season 2. This supports the earlier finding that season is not a good predictor of episode similarity. We can also see that episode 15 again stands out from the rest. This time because it is more loosely connected to the rest of the graph than any of the other episodes.

跳出来的第一件事是，每个组包含第1季和第2季中大致相等的情节。这支持了较早的发现，即该季不是情节相似性的良好预测指标。 我们还可以看到第15集再次脱颖而出。 这次是因为与其他任何情节相比，它与图的其余部分的联系更为松散。

Perhaps most interestingly, the network reveals that episodes tend to be less like those preceding and proceeding them than you would expect to occur by chance(p = .03). Additionally, the transitions from episodes 1→2, 2→3, and 3→4 have three of the five largest shifts in emotional structure of the 16 transitions that occurred in the show.

也许最有趣的是，该网络揭示了情节倾向于不像您之前和进行的情节那样多，而与您偶然偶然发生的情节相比( p = .03) 。 此外，从剧集1→2、2→3和3→4进行的过渡，在演出中发生的16个过渡中，情感结构的五个最大变化中有三个。

Together, these results suggest that varying the emotional trajectory from episode to episode may be a strategy for getting viewers hooked.

总之，这些结果表明，在情节之间改变情感轨迹可能是吸引观众的一种策略。

Will future episodes continue to show these changes in emotional structure? Will they follow the same three dominant emotional trajectories? Will future attempts at creating boldly different episodes land better than Chicago? Chalk those up as more unanswered questions for Season 3.

未来的情节会继续表现出情绪结构的这些变化吗？ 他们会遵循相同的三个主要情感轨迹吗？ 将来尝试制作大胆不同的情节是否会比芝加哥更好？ 将其作为第3季中更多未解决的问题 。

Miscellaneous observations [some spoilers ahead]

杂项观察[前面有一些破坏者]

• Series emotional high point: Christmas at the Byers household in the Season 1 finale.系列情感高峰：第1季大结局中的Byers家庭过圣诞节。

What are you - What are you doing? [Jonathan] Documenting.Oh, why? - Because - [Joyce chuckles] - It looks great. - [Joyce] Oh, this is just so overcooked. - And look, the potatoes are runny.- [Jonathan] Mom. - [Joyce] They're so runny.- [Jonathan chuckles] Mom, it's gonna be great.

• Series emotional low point #1: Chase scene to open Season 2.系列情感低点＃1：追逐场景，开启第二季。

[Horns honking] Shit! Shit! Shit! Shit! Shit! [Cackles] [Exhales] Okay. Okay.[Police sirens wailing] - Son of a bitch! We got more! - [Mick] Oh, shit! They're headed down 7th.

• Series emotional low point #2: Steve vs. Billy in the Season 2 finale.系列情感低点2：第2季大结局的史蒂夫(Steve)与比利(Billy)。

[Steve] Get out.[Dustin] Yes! Kick his ass, Steve! - [Mike] Get him! - [Dustin] Murder the son of a bitch! - [Dustin] Now! Now! - [Mike] Get that shithead! - [Dustin] Kill the son of a bitch! - [Lucas] Steve! - [Max] Billy! - [Mike] Holy shit! Shit!

• Honorable mention for the best scene that could not be counted because of the lack of dialogue: Billy in the mirror.荣誉奖：由于缺乏对话而无法计数的最佳场景：镜中的比利。

[Billy preens]

For those interested, code for this project is publicly available here.

对于那些感兴趣的人，此项目的代码可在此处公开获得。

翻译自: https://www.freecodecamp.org/news/a-statistical-curiosity-voyage-through-the-emotion-of-stranger-things-e7bc8b2a6395/

航行金税盘