Iris Hendrickx works as researcher in the field of computational linguistics, digital humanities, text mining and natural language processing. She currently affiliated with the Centre for Language Studies Radboud University in The Netherlands.
Louis Onrust is a natural language processing researcher with a focus on topic models, language models, and their applications. He works at the Centre for Language Studies at Radboud University in the Netherlands and at KU Leuven, Belgium.
Florian Kunneman is a researcher based at Radboud University, Centre for Language Studies, and has a background in communication studies and language technology. He has recently defended his PhD dissertation on modelling patterns of time and emotion in Twitter, and is currently working on several language technology projects as a postdoc, also at Radboud University.
Ali Hürriyetoğlu is a researcher working on social media and text mining in the Netherlands. His focus is on finding relevant information in document collections by applying detailed language analysis and machine learning techniques. He works at Statistics Netherlands and is a guest researcher at Radboud University.
Wessel Stoop is scientific programmer at the Centre for Language & Speech Technology, and language technologist at ICT company Davinci.
Antal van den Bosch is director of the Royal Dutch Academy of Arts and Sciences’ Meertens Institute, and professor of language and speech technology at the Centre for Language Studies at Radboud University, Nijmegen, the Netherlands. He obtained his Ph.D. in computer science at the Universiteit Maastricht, the Netherlands (1997). His research interests include memory-based natural language modeling, text analytics applied to historical texts and social media, and the Dutch language.
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We investigate what distinguishes reported dreams from other personal narratives.
The
Using automated text analysis Hendrikxc et al. investigate what distinguishes dream reports from other personal narratives.
Dreams are a fascinating phenomenon that has been studied for millennia. In
ancient Greek and Egyptian times dreams were seen as messages from the gods, and
played an important role in religion. One of the earliest recorded dream
analyses was written on clay tablets in Mesopotamia, 5000 years ago
Psychologists and social scientists have studied dream content with quantitative
methods for decades, working with the hypothesis that dreams reveal
psychological information about the dreamer. One currently dominant theory in
this area is the continuity hypothesis, which assumes
that the content of dreams reflects a person’s daily life and personal concerns
Dream descriptions are written reports of the memories of an experienced dream.
Even though much progress is made in neuroscience, it is not possible yet to
decode the dream content from a dreaming person’s brain activity. The only
possible way to gather dream contents is to study the reported recollection
produced when the experiencer was awake
We performed three different types of automatic text analysis to investigate what
typical characteristics we can discover in dream reports. We hope that our
automatic linguistic approach can demonstrate to dream analysis experts how
well-studied techniques from the field of computational linguistics can be
applied to offer insights into linguistic patterns hidden in large dream
collections. These analyses go beyond the standard word frequency analysis that
is common in corpus linguistics and that has already been applied to dream
reports
The largest available digitally curated collection of dream reports is the
DreamBank
We apply the following three methods:
Automatic textual analysis of dream reports is a relatively unexplored field.
Semi-automatic experiments have been performed by Bulkeley and Domhoff [2009],
who developed a systematic category list of word strings that can be used for
automated queries and word-frequency counts. The categories in which the words
are organized relate to the content of dreams, and are used to count mentions of
emotions, characters, perception, movement, cognition, and culture. In a more
recent follow-up study
Some work exists on automatic text classification with machine learning methods,
where the task is to assign emotion labels to dreams. In
In
The dream reports in the DreamBank were written down either by the dreamers
themselves or by researchers that interrogated the dreamers after awaking. The
written dream reports resemble oral narratives as they were described in the
seminal work of Labov and Waletzky
We also consider most dream reports as narratives, given that, in most cases,
they are
Drawing on the argumentation of this previous work we posit that dreams are personal narratives, which narrows down our research question to: what distinguishes dream reports from other personal narratives such as true stories?
As we are interested in automatically investigating textual properties, and studying what characteristics are typical for dream reports, we compare dream reports to other texts and narratives. We use dream reports from the DreamBank, and we place them in contrast with data representing personal narratives that actually happened, taken from the internet sources Reddit and Prosebox. In this section we introduce the three sources, and describe their properties.
We use the dream reports from collections as gathered in the DreamBank, a
project to combine the results of several scientific studies and resources
over the years in one online search interface
For our experiments we performed the following selection steps on the
DreamBank data, where we limited ourselves to English written collections.
Since some of the DreamBank collections overlap, we removed the duplicates
from our sample. We also removed a part of the description in the collection
“College women from the late 1940” that contained answers to specific
questions, and we only kept the dream description itself. We applied an
automatic language identification step
We noted that some collections in the DreamBank are much larger than others, and that dream descriptions of certain persons (e.g. Barb Sanders) are relatively prominent in the DreamBank content. We decided to create a sample of the DreamBank where we limit the amount of dream reports per individual dreamer to a random selection of at most one hundred dreams. This produced a sample of 6,998 dream descriptions, comprising 1.3 million tokens in total with an average of 65 words and a standard deviation of 43.7 per dream description, similar to the larger sample. We used both the large and the small sample in our experiments.
We show an example of a tokenized dream description with 97 tokens:
To discover what the typical linguistic attributes of dream reports are, we need a comparable set of contrasting reports that is as similar to dream reports as possible, both in structure and in content. Comparing dream reports to a collection of news paper articles or personal letters will lead to obvious findings such as: dreams do not report on political debates and the weather forecast, and will not end in ‘yours sincerely’. This is not the type of differences that we are interested in. We therefore aimed to find a collection of personally written recollections of true daily life events. Recall that dreams are known to reflect daily life events and activities for at least 75–80% of the cases.
Comparable collections of personal stories recollecting true events, not just fantasies or fiction, are difficult to find when looking for existing curated corpus collections. For this reason we resorted to collections of web texts to build our own corpus.
The first part of the contrasting data consists of personal stories. The
stories are crawled from Prosebox,
We collected all public posts that were available at the end of March 2015. As a result, we crawled 130 thousand posts with over 67 million tokens. We applied the same filtering pipeline to the Prosebox posts as was applied to the dreams; that is, we applied a language filter where we only kept the posts which were identified as English; second, we tokenized the posts. Since the number of tokens is much larger, we downsampled the corpus to a similar number of tokens as the DreamBank samples, i.e. the large sample and the smaller limited sample, containing 4.3 million words and 1.3 million words respectively, with an average of 64 and 63 words and standard deviations 78.8 and 94.3, respectively. In other words, we kept the average document size virtually equal to that of the DreamBank samples; the Prosebox data does exhibit a larger variance in size. We show an excerpt of a Prosebox text here:
The second part of the contrast data consists of Reddit posts. Reddit is a website where users can submit content of almost every kind. The site uses a community system, where each community is called a subreddit. We collected posts from a number of subreddits where the posts are texts about daily and personal experiences such as communities named
We applied topic modeling, text classification, and coherence tests to the aforementioned data sets in order to compare them.
As a first analysis of the text collection, we set out to train machine-learning classifiers to distinguish dream reports from personal stories automatically. Both the extent to which the classifiers succeed, and the features they use to make their decision, can lead to insights in the differences between dream reports and other narratives. Terms or groups of terms that are identified by classifiers as strong indications that a text is a dream report or not are apparently typical for their respective class.
In text classification, a machine learning classifier is fed with labeled
documents from which it learns to model the characteristics of the given
labels. Its labeling performance is tested by applying the classifier to a
held-out set of documents. For this experiment, we used the sets of 4.3
million words for both the dream data and contrasting data.
We tokenized all documents with the Stanford Tokenizer.
We compared the performance of three different classification algorithms:
Support Vector Machines (SVM), Naive Bayes, and Balanced Winnow. We used the
libsvm
We evaluated the performance of the three approaches by means of ten-fold cross-validation. To avoid author bias, the reports by the same author were kept together in either the test set or the train set during each fold. During each training phase, the 7,500 most frequent features were selected and presented as binary values.
The classification results, micro-averaged over examples, are given in Table 1. All three approaches yield a precision and recall of 0.97, which indicates that the dream and non-dream reports can easily be distinguished with a small remaining margin of error. Table 1 also displays the exact number of documents that were correctly classified. The Balanced Winnow classifier has a slightly higher number of correct classifications than Naive Bayes and SVM.
The Balanced Winnow classifier returns an interpretable model of the features
that the classifier used internally to make its predictions. Upon analysis
of the 30 most indicative features of the dream and non-dream classes, we
obtained the following insights about the two types of texts:
To discover what type of topics are typical for dream reports, we employed an
unsupervised method that is currently popular for discovering latent themes
or topics in large document collections. Latent Dirichlet Allocation (LDA)
We ran experiments with LDA on the full DreamBank sample of 22,046 dreams. We filtered the dream texts to exclude all function words and punctuation marks and only kept those content words that were automatically part-of-speech tagged by the Stanford parser as nouns, verbs and adjectives. All words have been converted to lower case. Such explicit filtering step ensures that the generated LDA topics contain only content words.
For these experiments we use the LDA implementation provided in the Mallet
toolkit
Setting the number of topics parameter is a rather arbitrary choice. We ran
experiments with 100, 200 and 400 topics as well and studied the output.
When raising the value of this parameter, more fine-grained topic
descriptions are produced. These detailed topics are still understandable
and coherent topics, but, as can be expected, they tend to have a lower
coverage in the document. As we aim to look at significant differences
between topic distributions in different sample sets and to compute g-tests
(log-likelihood tests)
LDA can give surprising insights in the data. We applied LDA to the full
DreamBank set of dreams and we present a random sample of these topics
in Table 2. The number in each row denotes the topic number and does not
express a ranking or weight. Certain topics express a specific script or
frame; in the first three topics in Table 2 we see
In a next step we zoom in on two comparable dream sets of men and women
to study the differences in topics between these groups. We use the
normative male and female dream sample present in the DreamBank
(abbreviated to
Topics were generated based on the full sample. For each topic we compute
whether the topic occurs significantly more or less in
LDA topics have been shown to express semantic coherence. Although there
is currently no metric available that could be used to optimize the LDA
settings to tune it explicitly towards human judgments, it has been
shown that the automatic topic assignment to documents matches human
preferences
These preliminary results are in line with recent research on differences
between male and female in [that] there are more appearances of tools and cars in
men’s dreams, more appearances of clothing and household items in
women’s dreams
. The main difference is that the results
presented here were obtained unsupervised, and support the current
manually found results reported in other papers.
In the next step we combined the dream sample with the Reddit and Prosebox samples into one large collection on which we ran the LDA topic modeling using the same setting of 50 topics.
To investigate what topics occur significantly more with dreams than with personal stories, we took a random sample of 2,000 dreams and 2,000 stories and computed a g-test to check whether there were significant differences in the topic distributions. In this sample of 4,000 documents we found that 42 of the 50 topics occur significantly more or significantly less with either dreams or personal stories. This shows that there are clear differences between the two sets; more so than between the male and female dreams. In Table 4 we show the top five most significantly different topics for dreams and stories.
Topic 28 is typical for what we expect to be a dream description,
mentioning words such as
For the personal stories we observe two topics that are directly linked
to the Reddit categories that were included in the sample, namely
There is some overlap in the most important words in the topic word
lists. The term
When people are asked what is typical about dreams, they will often mention
weirdness as a typical property of dreams. This might be due to the fact
that many dreams are forgotten the next morning while weird or impressive
dreams tend to stick in people’s memory
Bizarreness can emerge in different forms in dreams. The most prominent type
of oddity seems to be caused by the discontinuity of events and sudden
switches of scenes. In the study of Reinsel et al.
Interestingly, bizarre thoughts do not only occur in our sleep but can also
occur when awake. It has been shown that people in a relaxed undisturbed
awake condition produce dream-like reports when asked to recall that most
recent thoughts in the same way subjects are asked after being awakened
In our study we aimed at using a quantitative approximation of bizarreness and applying this metric to the DreamBank texts. We focus on the discontinuity of events in dreams and try to quantify this by looking at textual coherence in the dream reports. We hypothesize that dreams are less coherent in their discourse structure than personal stories. We measure two different aspects of discourse structure, namely discourse marker frequency and entity-based text coherence, using the smaller balanced sample sets of 1.3 million words.
Discourse analysis is a broad and multi-disciplinary field that studies
language in use beyond the sentence level
In this initial experiment we only focus on discourse marker occurrences and
measure whether there is a difference between discourse marker frequency in
the dream data and the personal stories. Discourse markers are words or
phrases that explicitly signal discourse structure and describe how two
sentences or phrases are related to each other. For example,
We used a list of 60 markers
In a second experiment we study entity-based coherence. Mentioned entities and chains of referring expressions in a text are core indicators of text coherence. We assume that discontinuity in dreams is expressed in sudden shifts in scenes and events, and we expect that these are linked to shifts in mentioned entities. On the basis of this assumption, we tried to measure discourse coherence by applying an existing automatic model to detect entity-based coherence.
We used the Brown Coherence Toolkit v1.0
To detect the entities in the text we used the extended entity grid based on the Wall Street Journal corpus that was automatically pre-processed with OpenNLP software, available in the Brown Coherence Toolkit.
We applied the model to each of the balanced dream and personal stories data
sets and measure its performance with a binary discrimination test as was
previously done in the work of Elsner and Charniak
The results of this test are shown in Table 5. All achieved results are substantially lower than the scores reported by Elsner and Charniak, who report an F-score of 86%, when training on
We presented three automatic text analysis studies of dream reports. First, we performed a supervised text classification experiment to see how easy or hard it is to distinguish dream reports from texts that are closely related in both content and structure, namely true personal stories. We applied three different text classification algorithms to the same task; they all succeeded in labeling the documents with a near-perfect precision. Differentiating between dreams and personal stories turned out to be an easy task. The analysis of the features used by the Balanced Winnow classifier shows that expressions of uncertainty, setting descriptions and narrative verbs are typical for dreams, while time expressions and conversational expressions are typical for the personal stories.
In the second study we aimed to explore the general topics that are present in the full DreamBank. We applied LDA topic modeling to the DreamBank to study the main themes in dreams. The results mostly showed topics describing everyday activities, settings, and characters. This unsupervised method signaled the same differences as the text classification experiments between dreams and stories: setting descriptions and uncertainty expressions are typical for dreams while time expressions and conversational expressions occur more often in stories. In our exploratory study on discontinuity in dreams, we observed that dream reports indeed use less discourse markers and have a lower entity-based textual coherence. With these experiments we are only just scratching the surface of doing automatic discourse analysis but we feel that these preliminary experiments are a starting point for further analyses in this direction.
Even though our experiments have shown some interesting and consistent findings
about the typical differences between dreams and stories, we need to be careful
with our conclusions. The fact that the text classifiers obtained such high
scores and the topics were significantly differently distributed over the
samples, could indicate that the contrasting data sample was not as
representative as we had hoped for. The emerging topic about
As a next step, we plan experiments on another sample of personal stories and dreams to investigate the effect of the sample representativeness. We also aim to collaborate with dream analysis experts to work towards better interpretations of the results that we found and to explore further research questions in the area of dream analysis.
We also believe that an in-depth study of the narrative mechanisms in dream
descriptions could be a fruitful path for future research. The overview of the
distribution of discourse markers presented in Figure 1 begs for further
analysis. Furthermore, we would like to investigate the applicability of the
Labovian Model of narrative
Furthermore, we are interested in the question whether humans can just as easily distinguish between dream descriptions and true stories as the text classifier could. We are currently working on building an online human judgment task to investigate this question.
We would like to thank Kelly Bulkeley and G. William Domhoff for their valuable feedback and suggestions. We also thank G. William Domhoff and Adam Schneider for creating the Dreambank that was the underpinning for this study. We are grateful to José Sanders and Kobie van Krieken for sharing their insights on narrative theory.