# Extracting Moods from Songs and BBC Programs  Based on Emotional Context

Authors: Michael Kai Petersen; Andrius Butkus
Journal: International Journal of Digital Multimedia Broadcasting
 (2008)

---

## Abstract

The increasing amounts of media becoming available in converged
digital broadcast and mobile broadband networks will require intelligent interfaces capable of personalizing
the selection of content. Aiming to capture the mood in the content, we construct a semantic space based on tags,
frequently used to describe emotions associated with music in thelast.fm social network. Implementing latent semantic analysis (LSA), we model the affective context of songs based on their lyrics, and apply a similar approach to extract
moods from BBC synopsis descriptions of TV episodes using TV-Anytime atmosphere terms. Based on our early results,
we propose that LSA could be implemented as machinelearning method to extract emotional context and model
affective user preferences.


---

## Body

## 1. Introduction

When both
digital broadcast streams and the content itself are adapted to the small
screen size of handheld devices, it will literally translate into hundreds of
channels featuring rapidly changing mobisodes and location-aware media, where it
might no longer be feasible to select programs by scrolling through an
electronic program guide. In order to automatically filter media according to
personalized preferences, this will require metadata which not only defines
traditional genre categories but also incorporates parameters capturing the
changing mobile usage contexts. Since 2005, the broadcaster BBC has made their
program listings available as XML formatted TVA TV-Anytime [1] metadata, which
allows for describing media using complementary aspects, such as content genre,
format, intended audience, intention, or atmosphere. We have previously in a
related paper [2] analyzed how especially atmosphere metadata describing
emotions may facilitate identifying programs that might be perceived as similar
even though they belong to different genre categories. Also in music it appears
that despite the often idiosyncratic character of tags, defined by hundred
thousands of users in social networks like last.fm, people tend to agree
on the affective terms they attach to describe music [3, 4]. A mounting question
might therefore be: could we possibly apply machine learning techniques to
extract emotional aspects associated with media in order to model our
perception, and thus facilitate an affective categorization which goes beyond
traditional divides of genres?


## 2. Related Works

In usage scenarios involving DVB-H mobile TV, where
shifting between a few channels might be even more time-consuming than watching
the actual mobisode, new text mining approaches to content-based filtering have
been suggested as a solution. Reflecting preferences for categories like
“fun,” “action,” “thrill,” or “erotic,” topics and emotions are
extracted from texts describing the programs and incorporated into the EPG
electronic program guide data as a basis for generating user preferences [5].
In broadcast context, a similar approach has been implemented to extract both
textual and visual concepts for automatic categorization of TV ad videos based
on probabilistic latent semantic analysis (pLSA) [6]. As a machine learning
method similar to latent semantic analysis (LSA) [7], it captures statistical
dependencies among distributions of visual objects or brand names, and thus
enables unsupervised categorization of semantic concepts within the content.
Recent neuroimaging experiments, focused on visualizing human brain activity
reflecting the meaning of nouns, have demonstrated a direct relationship
between the observed patterns in brain scans of regions being activated, and
the statistics of word cooccurrence in large collections of documents. The
distinct patterns of functional magnetic resonance images (fMRIs) triggered by
specific terms seem not only to cause similar brain activities across different
individuals [8], but also makes it possible to predict which voxels in the
brain will be activated according to semantic categories based on word
cooccurrence in a large text corpus [9]. Or in other words, the way LSA
simulates text comprehension by modelling the meaning of words as the sum of
contexts in which they occur appears to have neural correlates.Over the past decade, advances in neuroimaging
technologies enabling studies of brain activity have established that musical
structure to a larger extent than previously thought is being processed in
“language” areas of the brain [10]. Neural resources between music and
language appear to be shared both in syntactic sequencing and also semantic
processing of patterns reflecting tension and resolution [11–13], adding
support for findings of linguistic and melodic components of songs being
processed in interaction [14]. Similarly, there appears to be an overlap
between language regions in the brain and mirror neurons, which transfer
sensory information of what we perceive by reenacting them on a motor level.
The mirror neuron populations mediate the inputs across audiovisual modalities
and the resulting sensory-motor integrations are represented in a similar form,
whether they originate from actions we observe in others, only imagine or actually
enact ourselves [15, 16]. This has led to the suggestion that our empathetic
comprehension of underlying intentions behind actions, or the emotional states
reflected in sentences and melodic phrases are based on an imitative
reenactment of the perceived motion [17].Aspects of musical affect have been the focus of a
wide field of research, ranging from how emotions arise based on the underlying
harmonic and rhythmical hierarchical structures forming our expectations
[18–20], to how we consciously experience these patterns empathetically as
contours of tensions and release [21], in turn triggering physiological changes
in heart rate or blood pressure as has been documented in numerous cognitive
studies of the links between music and emotions [22]. But when listening to
songs our emotions are not only evoked by low-level cognitive representations
but also exposed to higher level features reflecting the words which make up
the lyrics. Studies on retrieving songs from memory indicate that lyrics and
melody appear to be recalled from two separate versions: one storing the melody
and another containing only the text [23], while further priming experiments
indicate that song memory is not organized in strict temporal order, but rather
that text and tune intertwine based on reciprocal connections of higher-order
structures [24].Taking the above findings into consideration, could we
possibly extract affective components from textual representations of media
like song lyrics, and model them as patterns reflecting how we emotionally
perceive media? Applying LSA as a machine learning method to extract moods in
both song lyrics and synopsis descriptions of BBC programs, we describe in the
following sections, the methodology used for extracting high level
representations of media using emotional tags, the early results retrieved when
mapping emotional components of song lyrics and synopsis descriptions, and
conclude with a discussion of the potential for automatically generating
affective user preferences as a basis for mood-based recommendation.


## 3. Emotional Tag Space

When investigating how unstructured metadata can be
used to describe media, the social music networklast.fm provides an
interesting case. The affective terms which are frequently chosen as tags by last.fm users to describe the emotional context of songs seem to form clusters around
primary moods like mellow, sad, or more agitated feelings like angry and happy.
This correlation between social network tags and the specific music tracks they
are associated with has been used in the music information retrieval community
to define a simplified mood ground-truth, reflecting not just the words people
frequently use when describing the perceived emotional context, but also which
tracks they agree on attaching these tags to [3, 4]. We have selected twelve of
these frequently used tags for creating an emotional semantic space. Drawing on
standard psychological parameters for emotional assessment, we map these
affective terms along the two primary dimensions of valence and arousal [25], and use these two axes to outline an emotional plane for dividing them
within an affective semantic space containing four groups of frequently used last.fm tags:
(i)
happy, funny, sexy;(ii)
romantic, soft, mellow, cool;(iii)
angry, aggressive;(iv)
dark, melancholy, sad.Within this emotional plane, the dimension ofvalence describes how pleasant something is along an axis going from positive to
negative associated with words like happy or sad, whereas arousal captures the amount of involvement ranging from passive states like mellow and
sad to active aspects of excitation as reflected in tags like angry or happy.
Applying the selected last.fm tags as emotional buoys to define a
semantic plane of psychological valence and arousal dimensions, we apply latent
semantic analysis (LSA) to assess the correlation between the lyrics and each of
the selected affective terms. Applying these affective terms as markers also
enables us to compare the LSA-retrieved values against the actual tags users have
applied in the last.fm tag clouds associated with the songs in our
analysis. Additionally, when analyzing the synopsis descriptions of BBC
programs we have complemented the last.fm tags with a large number of
TV-Anytime atmosphere terms similarly used as emotional buoys. Though the two
sets of markers are clearly affected differently by the synopsis, a comparison
shows that despite the higher degree of detail in the TV-Anytime vocabulary,
the overall emotional context is reflected similarly by the last.fm tags
and the atmosphere terms. Or in other words, the last.fm and TV-Anytime
markers provide different granularities for capturing emotions but the larger
tendencies in the resulting patterns remain the same.As a machine learning technique, LSA extracts meaning
from paragraphs by modelling the usage patterns of words in multiple documents
and represent the terms and their contexts as vectors in a high-dimensional
space. The basis for assessing the correlations between lyrics and emotional
words vectors in LSA is an underlying text corpus consisting of a large
collection of documents which provides the statistical basis for determining
the cooccurrence of words in multiple contexts. For this experiment, we chose
the frequently implemented standardTASA text corpus, consisting of the
92409 words found in 37651 texts, novels, news articles, and other general
knowledge reading material that American students are exposed to up to the
level of their 1st year in college. The frequency at which terms appear and the
phrases wherein they occur are defined in a matrix with rows made up of words
and columns of documents. Many of the cells made up by rows and columns contain
only zeroes, so in order to retain only the most essential features, the
dimensionality of the original sparse matrix is reduced to around 300
dimensions. This makes it possible to model the semantic relatedness of song
lyrics and affective terms as vectors, with values toward 1 signifying degrees
of similarity between the items and low or minus values typically around 0.02
signifying a random lack of correlation. In this semantic space lines of lyrics
or emotional words which express the same meaning will be represented as
vectors that are closely aligned, even if they do not literally share any
terms. Instead, these terms may cooccur in other documents describing the same
topic, and when reducing the dimensionality of the original matrix, the
relative strength of these associations can be represented as the cosine of the
angle between the vectors.


## 4. Results: Song Lyrics

Whereas the user-defined tags atlast.fm describe a song as a whole, we aim to model the shifting contours of tension
and release which evoke emotions, and therefore project each of the individual
lines of the lyrics into the semantic space. Analyzing individual lines on a
timescale of seconds also reflects the cognitive temporal constraints applied
by our brains in general when we bind successive events into perceptual units
[26]. We perceive words as successive phonemes and vowels on a scale of roughly
30 milliseconds, which are in turn integrated into larger segments with a
length of approximately 3 seconds. We thus assume that lines of lyrics
consisting of a few words each correspond to one of these high-level perceptual
units. Viewed from a neural network perspective, projecting the lyrics into a
semantic LSA space line by line, could also in a cognitive sense be interpreted
as similar to how mental concepts are constrained by the amount of activation
among the neural nodes representing events and associations in our working
memory [27]. In that respect, the cooccurrence matrix formed by the word
frequencies of last.fm tags and song lyrics might be understood as
corresponding to the strengths of links connecting nodes in a mental model of
semantic and episodic memory.


### 4.1. Accumulated Emotional Components

Projecting the
lyrics of thirty songs selected from the weekly top track charts atlast.fm,
we compute the correlation between lyrics and tags against each of the twelve
affective terms used as markers in the LSA space, while discarding cosine
values below a threshold of 0.09. And in order to compare the retrieved LSA
correlation values of lyrics and affective terms against the user-defined tags
attached to the song at last.fm, we sum up the accumulated LSA values
retrieved from each line of the lyrics.Taking the song “Nothing else matters” as an
example, the user defined tags attached to the song as atlast.fm,
include less frequently used tags like love, love songs, chill, chillout,
relaxing, relax, memories, and melancholic which are not among the
markers we used for our LSA analysis. We therefore subsequently combine these
tags into larger segments of tags in order to facilitate a direct comparison
with the LSA-retrieved values (Figure 1). Comparing the accumulated LSA values
of emotional components against the user-defined tags at last.fm, the
terms melancholy, and melancholic, which describe the most
dominant emotions in the tag cloud, could be understood as captured by the
affective term sad in the LSA analysis. Similarly, if interpreting love from the last.fm tag cloud as associated with the term happy (based on a cosine correlation of 0.56 between the words love and happy), the
LSA analysis could be understood to retrieve also aspects of this emotion.
Likewise, if chill in the last.fm tag cloud is understood as
associated with soft and mellow (based on cosine correlations of
0.36 and 0.35, resp.), the LSA analysis also here appears to capture that mood.Figure 1
Accumulated LSA correlation between (a) the lyrics of the song “Nothing else matters” and 12
affective terms, compared to (b) the actual user-defined emotional tags at
last.fm.Applying a similar approach to a set of thirty songs,
we grouped semantically closelast.fm tags into larger segments
consisting of sad, happy, love, and chill aspects to facilitate a
comparison with the LSA-derived correlations between song lyrics and the
selected affective terms. Though there is an overlap between the retrieved LSA
values and user-defined last.fm tags in most of the songs, there is no
overall significant correlation between LSA-retrieved values and the exact
distribution of tags in the user-defined last.fm tag clouds.
Essentially, the individual tags in a cloud are “one size fits all” and apply
to the song as a whole, whereas the LSA correlation between lyrics and semantic markers
reflects the changing degrees of affinity between the song
lines and affective components over time. But for a third of the set of songs, as exemplified
by “Now at last” (Figure 2), the distribution of last.fm tags resembled
the LSA values if grouped into larger segments. While in the remaining two
thirds of the set of songs, as exemplified by the song “Mad World” (Figure 3),
the overall distribution in last.fm tags while clearly overlapping
remain overly biased toward sad type of components.Figure 2
Accumulated LSA correlation between (a) the lyrics of the song “Now at last” and 12 affective
terms, compared to (b) the actual user-defined emotional tags at last.fm.Figure 3
Accumulated LSA correlation between (a) the lyrics of the song “Mad world” and 12 affective terms,
compared to (b) the actual user-defined emotional tags at last.fm.


### 4.2. Distribution of Emotional Components

Instead of grouping the emotional components into
larger segments, we subsequently maintained the LSA values retrieved from each
of the individual lines in the lyrics, and proceeded by plotting the values
over time to provide a view of the distribution of emotional components. The
plots can be interpreted as mirroring the structure of patterns of changing
emotions in the songs along the horizontal axis. Vertically, the color
groupings indicate which of the aspects of valence and arousal are triggered by
the lyrics as well as their general distribution in relation to each other. Any
color will signify an activation beyond the cosine similarity threshold level
of 0.09, and the amount of saturation from light to dark signifies the degree
of correlation between the song lyrics and each of the affective terms. The
contribution of each emotional component apparent in the overall LSA values of
the lyrics can be made out when considering their distribution as single pixels
over time triggered by the individual lines in each of the songs. When
analyzing which emotional components appear predominant and overall contribute
the most, the LSA plots can roughly be grouped into three categories which can
be characterized asunbalanced distributions, centered distributions, and uniform distributions.Going back to the song “Nothing else matters,”
Figure4, the plot exemplifies the first unbalanced category by in this
case having a bottom-heavy distribution of emotional components biased toward melancholy.
The below curve of accumulated LSA values indicates the contribution of each
component over the entire song, where the significant aspects of melancholy are clearly separated from the other components.Figure 4
LSA correlation between (a) the lyrics of the song “Nothing else matters” and 12 affective terms,
with (b) accumulated values plotted over the entire length of the song.Thecentered distribution distribution as found
in “Now at last” (Figure 5) shows a lack of the more explicit emotions like
“happy” or “sad” apart from the very beginning, while instead the main
contribution throughout the song comes from more passive “mellow” and
“soft” aspects. In contrast to the former example, the below curves of
accumulated emotional contributions reflect a pattern combining the activation
of “happy” or “sad” elements which remain at the initial level, whereas the
more passive aspects “mellow” and “soft” are continuously accumulating
throughout the song.Figure 5
Summed up values of LSA correlation between (a) the lyrics
of the song “Now at last” and 12 affective terms, with (b) accumulated
values plotted over the entire length of the song.Auniform distribution of a wide range of
simultaneous emotional components is exemplified by “mad world,” Figure 6,
simultaneously juxtaposing emotional areas around “happy” against “sad”
components. This pattern can also be made out in the below curves, where
additionally the sudden steep increase in accumulated values starting roughly a
third into the song also illustrates how the emotional components reflect the
overall structure in the song.Figure 6
Summed up values of LSA correlation between (a) the lyrics
of the song “Mad world” and 12 affective terms, with (b) accumulated values
plotted over the entire length of the song.The overall saturation defining the amount of
correlation between lyrics and emotional markers, as well as the distributional
patterns of emotional components throughout the songs seem consistent. Lyrics
that appear more or less saturated in relation to the emotional markers used
for the LSA analysis remain so over the entire song. The distributional
patterns of emotional elements seem throughout the songs to form consistent
schemas of contrasting elements, which appear to form sustained lines or
clusters that are preserved as pattern once initiated. We suggest that these
elements form bags of features, which could be used to categorize and infer
patterns as a basis for building emotional playlists. From these
features, general patterns emerge, as in the distributions of emotional
components in the songs “Wonderwall” and “My Immortal,” Figure7, which
appear similar due to a sparsity of central aspects like “soft,” while
instead emphasizing the outer edges by juxtaposing elements around “happy”
against “sad.” The opposite character can be seen in the distributions of
central elements stressed in the songs “Falling slowly” and “Stairway to
heaven,” Figure 8, which underline the aspects of “soft” and “mellow” at
the expense of “happy” and “sad.” Whereas these elements in the songs
“Everybody hurts” and “Smells like teen spirit,” Figure 9, appear as
structural components grouped into clusters, either providing a strong
continuous activation of complementary feelings or juxtaposing these emotional
components against each other.Pairwise comparison of patterns reflecting LSA
correlation values in the lyrics of the songs (a) “Wonderwall”, and (b) “My
immortal” against 12 affective terms.
(a)(b)Pairwise comparison of patterns reflecting LSA correlation
values in the lyrics of the songs (a) “Falling slowly”, and (b) “Stairway to
heaven” against 12 affective terms.
(a)(b)Pairwise comparison of patterns reflecting LSA
correlation values in the lyrics of the songs (a) “Everybody hurts”,  and (b) “Smells
like teen spirit”  against 12 affective terms.
(a)(b)


## 4.1. Accumulated Emotional Components

Projecting the
lyrics of thirty songs selected from the weekly top track charts atlast.fm,
we compute the correlation between lyrics and tags against each of the twelve
affective terms used as markers in the LSA space, while discarding cosine
values below a threshold of 0.09. And in order to compare the retrieved LSA
correlation values of lyrics and affective terms against the user-defined tags
attached to the song at last.fm, we sum up the accumulated LSA values
retrieved from each line of the lyrics.Taking the song “Nothing else matters” as an
example, the user defined tags attached to the song as atlast.fm,
include less frequently used tags like love, love songs, chill, chillout,
relaxing, relax, memories, and melancholic which are not among the
markers we used for our LSA analysis. We therefore subsequently combine these
tags into larger segments of tags in order to facilitate a direct comparison
with the LSA-retrieved values (Figure 1). Comparing the accumulated LSA values
of emotional components against the user-defined tags at last.fm, the
terms melancholy, and melancholic, which describe the most
dominant emotions in the tag cloud, could be understood as captured by the
affective term sad in the LSA analysis. Similarly, if interpreting love from the last.fm tag cloud as associated with the term happy (based on a cosine correlation of 0.56 between the words love and happy), the
LSA analysis could be understood to retrieve also aspects of this emotion.
Likewise, if chill in the last.fm tag cloud is understood as
associated with soft and mellow (based on cosine correlations of
0.36 and 0.35, resp.), the LSA analysis also here appears to capture that mood.Figure 1
Accumulated LSA correlation between (a) the lyrics of the song “Nothing else matters” and 12
affective terms, compared to (b) the actual user-defined emotional tags at
last.fm.Applying a similar approach to a set of thirty songs,
we grouped semantically closelast.fm tags into larger segments
consisting of sad, happy, love, and chill aspects to facilitate a
comparison with the LSA-derived correlations between song lyrics and the
selected affective terms. Though there is an overlap between the retrieved LSA
values and user-defined last.fm tags in most of the songs, there is no
overall significant correlation between LSA-retrieved values and the exact
distribution of tags in the user-defined last.fm tag clouds.
Essentially, the individual tags in a cloud are “one size fits all” and apply
to the song as a whole, whereas the LSA correlation between lyrics and semantic markers
reflects the changing degrees of affinity between the song
lines and affective components over time. But for a third of the set of songs, as exemplified
by “Now at last” (Figure 2), the distribution of last.fm tags resembled
the LSA values if grouped into larger segments. While in the remaining two
thirds of the set of songs, as exemplified by the song “Mad World” (Figure 3),
the overall distribution in last.fm tags while clearly overlapping
remain overly biased toward sad type of components.Figure 2
Accumulated LSA correlation between (a) the lyrics of the song “Now at last” and 12 affective
terms, compared to (b) the actual user-defined emotional tags at last.fm.Figure 3
Accumulated LSA correlation between (a) the lyrics of the song “Mad world” and 12 affective terms,
compared to (b) the actual user-defined emotional tags at last.fm.


## 4.2. Distribution of Emotional Components

Instead of grouping the emotional components into
larger segments, we subsequently maintained the LSA values retrieved from each
of the individual lines in the lyrics, and proceeded by plotting the values
over time to provide a view of the distribution of emotional components. The
plots can be interpreted as mirroring the structure of patterns of changing
emotions in the songs along the horizontal axis. Vertically, the color
groupings indicate which of the aspects of valence and arousal are triggered by
the lyrics as well as their general distribution in relation to each other. Any
color will signify an activation beyond the cosine similarity threshold level
of 0.09, and the amount of saturation from light to dark signifies the degree
of correlation between the song lyrics and each of the affective terms. The
contribution of each emotional component apparent in the overall LSA values of
the lyrics can be made out when considering their distribution as single pixels
over time triggered by the individual lines in each of the songs. When
analyzing which emotional components appear predominant and overall contribute
the most, the LSA plots can roughly be grouped into three categories which can
be characterized asunbalanced distributions, centered distributions, and uniform distributions.Going back to the song “Nothing else matters,”
Figure4, the plot exemplifies the first unbalanced category by in this
case having a bottom-heavy distribution of emotional components biased toward melancholy.
The below curve of accumulated LSA values indicates the contribution of each
component over the entire song, where the significant aspects of melancholy are clearly separated from the other components.Figure 4
LSA correlation between (a) the lyrics of the song “Nothing else matters” and 12 affective terms,
with (b) accumulated values plotted over the entire length of the song.Thecentered distribution distribution as found
in “Now at last” (Figure 5) shows a lack of the more explicit emotions like
“happy” or “sad” apart from the very beginning, while instead the main
contribution throughout the song comes from more passive “mellow” and
“soft” aspects. In contrast to the former example, the below curves of
accumulated emotional contributions reflect a pattern combining the activation
of “happy” or “sad” elements which remain at the initial level, whereas the
more passive aspects “mellow” and “soft” are continuously accumulating
throughout the song.Figure 5
Summed up values of LSA correlation between (a) the lyrics
of the song “Now at last” and 12 affective terms, with (b) accumulated
values plotted over the entire length of the song.Auniform distribution of a wide range of
simultaneous emotional components is exemplified by “mad world,” Figure 6,
simultaneously juxtaposing emotional areas around “happy” against “sad”
components. This pattern can also be made out in the below curves, where
additionally the sudden steep increase in accumulated values starting roughly a
third into the song also illustrates how the emotional components reflect the
overall structure in the song.Figure 6
Summed up values of LSA correlation between (a) the lyrics
of the song “Mad world” and 12 affective terms, with (b) accumulated values
plotted over the entire length of the song.The overall saturation defining the amount of
correlation between lyrics and emotional markers, as well as the distributional
patterns of emotional components throughout the songs seem consistent. Lyrics
that appear more or less saturated in relation to the emotional markers used
for the LSA analysis remain so over the entire song. The distributional
patterns of emotional elements seem throughout the songs to form consistent
schemas of contrasting elements, which appear to form sustained lines or
clusters that are preserved as pattern once initiated. We suggest that these
elements form bags of features, which could be used to categorize and infer
patterns as a basis for building emotional playlists. From these
features, general patterns emerge, as in the distributions of emotional
components in the songs “Wonderwall” and “My Immortal,” Figure7, which
appear similar due to a sparsity of central aspects like “soft,” while
instead emphasizing the outer edges by juxtaposing elements around “happy”
against “sad.” The opposite character can be seen in the distributions of
central elements stressed in the songs “Falling slowly” and “Stairway to
heaven,” Figure 8, which underline the aspects of “soft” and “mellow” at
the expense of “happy” and “sad.” Whereas these elements in the songs
“Everybody hurts” and “Smells like teen spirit,” Figure 9, appear as
structural components grouped into clusters, either providing a strong
continuous activation of complementary feelings or juxtaposing these emotional
components against each other.Pairwise comparison of patterns reflecting LSA
correlation values in the lyrics of the songs (a) “Wonderwall”, and (b) “My
immortal” against 12 affective terms.
(a)(b)Pairwise comparison of patterns reflecting LSA correlation
values in the lyrics of the songs (a) “Falling slowly”, and (b) “Stairway to
heaven” against 12 affective terms.
(a)(b)Pairwise comparison of patterns reflecting LSA
correlation values in the lyrics of the songs (a) “Everybody hurts”,  and (b) “Smells
like teen spirit”  against 12 affective terms.
(a)(b)


## 5. Results: BBC Synopsis

Repeating the approach, but this time to extract
emotions from texts describing TV programs, we take a selection of short BBC
synopses as input, and compute the cosine similarities between a synopsis text
vector and each of the selectedlast.fm emotional words. While the
previously analyzed lyrics could be seen as integral parts of the original
media, a synopsis description is clearly not. It only provides a brief summary
of the program, but it nevertheless offers an actual description complementary
to the associated TV-Anytime metadata genres. We initially analyzed a
number of standalone synopsis descriptions to see if would be possible to
capture emotional aspects of the BBC programs.An analysis of the program “News night,” based on
the short description:News in depth investigation and analysis of the
stories behind the day('s) headline, triggers the tags “funny” and
“sexy” which might not immediately seem a fitting description, probably
caused by these emotional terms being directly correlated with the occurrence
of the words stories and news within the synopsis. The atmosphere of the
lifestyle program “Ready Steady Cook!” might be somewhat better reflected in
the synopsis: Peter Davidson and Bill Ward challenge celebrity chefs to
create mouth watering meals in minutes, which triggers the tag “romantic”
as associated with meals. Another singular emotion can be retrieved from the
documentary “I am a boy anorexic,” which based on the synopsis: Documentary
following three youngsters struggling to overcome their obsessive relationship
with food as they recover inside a London clinic and then return to the outside
world, triggers the affective term “dark.” We find a broader emotional
spectrum reflected in the lifestyle program “The flying gardener” described
by the text: The flying gardener Chris travels around by helicopter on a
mission to find Britain('s) most inspirational gardens. He helps a Devon couple
create a beautiful spring woodland garden. Chris visits impressive local
gardens for ideas and reveals breathtaking views of Cornwall from the air.
The synopsis triggers a concentration of passive pleasant valence elements related to the words “soft, mellow” combined with “happy.” In this
context also the tag “cool” comes out as it has a strong association to the
word air contained in the synopsis, while the activation of the tag
“aggressive” appears less explainable. This cluster of pleasant elements is
lacking in the LSA analysis of the program “Super Vets” which instead evokes
a strong emotional contrast based  on the text: At the Royal Vet College
Louis the dog needs emergency surgery after a life threatening bleed in his
chest and the vets need to find out what is causing the cat fits, where
both pleasant and unpleasant active terms like “happy” and “sad” stand out
in combination with strong emotions reflected by the tag “romantic.” And as
can be seen from programs like “The flying gardener” and “Super Vets”
(Figure 10), the correlation between the synopsis and the chosen tags might
often trigger both complementary elements as well as contrasting emotional
components.LSA cosine
similarity between the synopsis descriptions of “The flying gardener” and
“Super Vets” against 12 frequently usedlast.fm affective terms.
(a)(b)We proceeded to explore whether we could sum up a
distinct pattern reflecting an emotional profile pertaining to a TV series, by
accumulating the LSA values of correlation between synopsis texts and emotional
tags over several episodes. Similar to our previous approach when analyzing
lyrics, where we held the LSA results against the user definedlast.fm tag clouds, we here compare the LSA values of the synopsis against the TV-Anytime atmosphere genres used in the BBC metadata. This classification scheme offers
53 different terms which might be included in the genre metadata to express the
atmosphere or perceived emotional response when watching a program. Projecting the
synopsis descriptions against 53 TV-Anytime terms, used as emotional
markers in the LSA analysis, allows for defining more differentiated patterns.
At the same time also projecting the BBC synopsis against the previously used last.fm tags in the LSA analysis, makes it possible to compare to what extent the
choice of using either TV-Anytime atmosphere terms or last.fm tags as emotional markers in the semantic space is influencing the results.For analyzing the emotional context in a sequence of
synopsis descriptions of the same program, we chose the soap “East Enders,”
the comedy “Two pints of lager,” and sci-fi series “Doctor Who.” Initially,
plotting the LSA analysis of the soap “East Enders” and comedy “Two pints of
lager” against 12last.fm tags (Figures 1 and 2, increased color saturation
corresponds to degree of correlation), the distributions of emotional
components appear unbalanced in both cases. But whereas the soap has a
bottom-heavy bias toward “sad” and “angry” outweighing “happy,” the
balance is reversed in the comedy which shifts towards predominantly “happy”
and “funny” complemented by “soft” and “mellow” aspects. Overall, the
distribution in “East Enders” is much more dense and emotionally saturated as
exemplified in elements like “angry” reflecting high arousal. In contrast,
the lighter character of  “Two pints of lager” comes out in the clustering of
positive valence elements such as “happy” and “funny,” coupled with a
general sparsity of excitation within the matrix.As a second step, projecting the synopsis descriptions
against the 53TV-Anytime atmosphere terms of course results in more
differentiated patterns. Users at last.fm frequently describe tracks as
“angry” but as music is rarely described as scary, feelings of fear are
lacking. Otherwise, so with the TV-Anytime metadata which also captures
these aspects in a synopsis with atmosphere terms like “terrifying.” Some of
these elements are essential for describing the content as is evident in the
sci-fi series “Doctor Who,” Figure 13. Lacking words for these feelings, the last.fm tags “Melancholy” and “dark” are triggered, whereas it takes
the increased resolution of the TV-Anytime atmosphere terms to capture
the equally “spooky” and “silly” aspects.AltogetherTV-Anytime adds a large number of
terms, which rather than describing emotions capture attitudes or perceived
responses like “stylish” or “compelling,” and as such trigger vast amounts
of elements contributing to the atmosphere. In “East Enders” adding elements
like “frantic” and “exciting” to the pattern. Similarly, the larger number
of comical elements exemplified by words like “crazy, silly,” or “wacky”
provides a much higher emotional granularity in the description of  “Two pints
of lager”. However, the overall bias toward positive or negative valence and
arousal within the distributions seem largely preserved, independent of whether last.fm or TV-Anytime terms are used as emotional markers in the
LSA analysis.Comparing the emotional components retrieved from the
LSA analysis of the synopsis texts against the actualTV-Anytime atmosphere terms in the BBC metadata, they seem to be largely in agreement. The
comedy has been indexed as “humorous, silly, irreverent, fun, wacky, crazy,”
while based on the synopsis texts alone, most of these components also come out
in the LSA analysis. In the case of the soap “East Enders,” the episodes are
annotated as “gripping, gritty, gutsy.” Although these terms are also
triggered from the synopsis texts, these aspects might be even more reflected
in the stark accumulated contrasts of “happy” and “sad” components
retrieved by the LSA analysis. Similarly, in “Doctor Who” the actual TV-Anytime atmosphere terms applied in the BBC metadata spooky, exciting are also
captured, while the grey patterns of perceived responses seem to add a lot more
nuances to this description.


## 6. Conclusions

Projecting BBC synopsis descriptions into an LSA space,
using bothlast.fm tags and TV-Anytime atmosphere terms
as emotional buoys Figures 11–13, we have demonstrated an ability
to extract patterns reflecting combinations of emotional
components. While each synopsis triggers an
individual emotional response related to a specific episode, general patterns
still emerge when accumulating the LSA correlation between synopsis and
emotional tags over consecutive episodes, which enables us to differentiate
between a comedy and a soap based on textual descriptions alone. Applying more
semantic markers in the analysis allows for capturing additional elements of
atmosphere in terms of perceived attitudes or responses to the media being
consumed. However, the overall balance of affective components reflecting the
media content seems largely preserved, independent of whether last.fm or TV-Anytime terms are used as emotional markers in the LSA analysis.Figure 11
LSA correlation
values of 10 episodes of (a) “Two Pints of lager” against 12 last.fm tags,  and (b) 53 tva atmosphere terms.Figure 12
LSA correlation values of 18 episodes of (a) “East
Enders” against 12 last.fm tags, and (b) 53 tva atmosphere terms.Figure 13
LSA correlation values of 12 episodes of (a) “Doctor
Who” against last.fm tags, and (b) 53 tva atmosphere terms.Moving beyond the static LSA analysis of consecutive
synopsis descriptions, plotting the components over time might provide a basis
for modelling the patterns of emotions evolving when we perceive media. We
hypothesize that these emotional components reflect compositional structures
perceived as patterns of tension and release, which form the dramatic
undercurrents of an unfolding story line. As exemplified in the plots of song
lyrics each matrix column corresponds to a time window of a few seconds, which
is also the approximate length of the high-level units from which we mentally
construct our perception of continuity within time [26]. Interpreted in that
context, we suggest that the LSA analysis of textual components within a
similar size of time window is able to capture a high level representation of
the shifting emotions triggered by the media. Or from a cognitive perspective, the dimensionality reduction enforced by LSA might be
interpreted as a simplified model of how mental concepts are constrained by the
strengths of links connecting nodes in our working memory [27].Finding that the emotional context of media can be
retrieved by using affective terms as markers, we propose that LSA might be
applied as a basis for automatically generating mood-based recommendations. It
seems that even if we turn off both the sound and the visuals, emotional
context as well as overall formal structural elements can still be extracted
from media based on latent semantics.



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*Source: 289837-2008-10-21.xml*