PASA 2024

# A sensitive search for water masers associated with star formation regions
in the Local Group galaxy NGC 6822

C. J. P. Flanagan1 S. P. Ellingsen1 A. A. Cole1 1 School of Natural Sciences,
University of Tasmania, Private Bag 37, Hobart, Tasmania 7001, Australia

###### Abstract

We report the results of a sensitive search for water maser emission in the
Local Group Galaxy NGC 6822 with the Karl G. Jansky Very Large Array. The
observations provide tentative single-epoch detections of four candidates,
associated with two infrared-bright star formation regions (Hubble I/III and
Hubble IV). The candidate maser detections are all offset from the velocity
range where strong emission from Hi neutral gas is observed toward NGC 6822,
with the closest offset by $\sim$40 kms-1. Our observations include the
location of NL1K, a previous tentative water maser detection in NGC 6822. We
do not detect any emission from this location with a sensitivity limit
approximately a factor of 5 better than the original Sardina Radio Telescope
observations.

###### doi:

10.1017/pas.2024.xxx

###### keywords:

masers – stars: formation – galaxies: individual (NGC 6822)

##  1 INTRODUCTION

Barnard’s Galaxy (NGC 6822) is a dwarf irregular galaxy at a distance of
479$\pm$15 kpc (Feast et al., 2012), with a metallicity of around one-fifth
solar (Swan et al., 2016; García-Rojas et al., 2016). NGC 6822 is one of the
nearest star-forming dwarf galaxies, and is the closest dwarf irregular galaxy
that is definitely not in a bound orbit with the Milky Way. NGC 6822 hosts
seven major star-forming regions, with a total estimated star formation rate
of at least 0.04 M⊙ yr-1 (Jones et al., 2019). Similarities between dwarf
irregulars and high-redshift galaxies, such as their low metallicities and
their lack of large-scale internal structure, mean that despite the mass
difference the former is potentially a useful proxy for the latter when
investigating galactic evolution.

NGC 6822 has a compact Hi velocity distribution. As presented in Namumba et
al. (2017), the velocity dispersion of the Hi gas peaks at 10 kms-1 in the
central region of the galaxy, where the barycentric velocity is around $-$57
kms-1, and drops to zero at the edges of the galaxy. The central region has
sixteen IR-bright regions identified by Spitzer observation (as presented by
Cannon et al. (2006)). These regions differ slightly from the historical
categorisation of central NGC 6822 Hii regions, which was based on the work of
Hubble (1925).

Intense, distributed infrared (IR) emission is closely associated with star
formation, making the IR-brightness of a region a tracer of the star formation
rate (SFR) therein (see Kennicutt & Evans (2012), particularly section 3.4).
Compared to the Hα line, IR emission includes less massive stellar populations
- while Hα emission almost exclusively comes from stars less than 10 Myr old,
broader IR emission can be contributed by stars more than 100 Myr old. This
makes IR emission an imprecise tracer of instantaneous SFRs but a reasonable
tracer for star formation activity.

Young star-forming regions are well-traced by three species of interstellar
masers within the Galaxy: water, methanol and hydroxyl masers (eg. Felli et
al. (2007)). Beyond the Milky Way, the occurrence of water masers appears to
correlate well with the star formation rates of their hosts, while methanol
and OH masers are significantly underabundant in the Magellanic Clouds (MCs)
(Green et al., 2008). However, the relatively low intensity of methanol and OH
masers when compared to the 22 GHz water maser transition means the comparison
cannot be made over the entire Local Group with any degree of confidence. With
no previously detected masers of any species in NGC 6822, luminosity
considerations mean that any attempt to create a catalogue of NGC 6822 masers
should start with the 22 GHz H2O transition. The abundance of OH and methanol
masers in NGC 6822 can potentially be derived from targeted observations
toward detected water masers in that galaxy, which would allow the effects of
large-scale environmental conditions on maser populations to be studied.

Generating a catalogue of masers is also critical to determining precise
distances and kinematics of the Local Group through Very Long Baseline
Interferometry (VLBI) observations. Water masers are not the ideal target for
astrometric VLBI measurements, as their temporal instability (e.g. Breen et
al. (2013)) often stymies precise tracking and they are rarely simply
associated with the systemic motion of their host galaxy. However, water
masers are often associated with masers of other species (e.g. hydroxyl and
methanol in young star-forming regions), and their strength compared to other
species present in the same star-forming regions allows astrometry at greater
distances. Such astrometry has already been performed on Milky Way populations
(e.g. Honma et al. (2012)), Local Group galaxies M33 (Brunthaler et al., 2005)
and IC 10 (Brunthaler et al., 2007), and distant galaxy NGC 5765b (Gao et al.,
2016).

Here we present a sensitive search for water maser candidates within NGC 6822.
The primary motivation of this observation was to identify water maser
candidates that could be used to determine the proper motion of NGC 6822
through future VLBI observations. This is a potential independent check on
proper motions from the ESA Gaia mission (McConnachie et al., 2021), with very
different systematics. The data was collected at a single epoch, so the
temporal variability of the candidates, and so their feasibility for VLBI
programs, cannot be assessed from the data.

##  2 OBSERVATION AND DATA REDUCTION

The observation was conducted to search for new detections using four hours of
the Karl G. Jansky VLA in D-configuration, between 23.15 and 3.15 UTC on
October 12th 2015. Observations targeted the five IR-bright regions of NGC
6822 shown in Figure 7 and used the K-band receiver system that detects left
and right circularly polarised signals separately. The observations were split
into four blocks, each consisting of sequential observations of each region
once followed by pointing offset correction by scanning the nearby quasar
J1939-1525.

Table 1: Regions of NGC 6822 observed. Cannon labels and emission strengths are from Cannon et al. (2006); historical labels are the same regions in older literature. Cannon Label | Historical Label | Right Ascension | Declination | Hα emission | 70 $\mu$m emission
---|---|---|---|---|---
|  | Hours (J2000) | Degrees (J2000) | 10-13 erg s-1 cm-2 | mJy
3 | Hubble I-III | 19:44:32.96 | -14:42:10.71 | 110$\pm$20 | 1700$\pm$430
7 | Hubble IV | 19:44:49.47 | -14:52:40.93 | 24$\pm$5 | 2300$\pm$580
8 | Hubble V | 19:44:52.81 | -14:43:04.97 | 120$\pm$25 | 4300$\pm$1100
11 | Hubble VI-VII | 19:44:58.59 | -14:47:47.02 | 13$\pm$3 | 2000$\pm$500
14 | Hubble X | 19:45:05.01 | -14:43:17.07 | 110$\pm$20 | 2400$\pm$600

The correlator was configured to record 4096 spectral channels over a 64 MHz
bandwidth, for both left and right circularly polarised signals. The central
frequency for the observations was 22224 MHz, corresponding to a velocity
coverage of 870 kms-1 and a spectral resolution of 0.2 kms-1. Each individual
scan of a region involved 8 minutes of observation, resulting in an average
RMS image noise of approximately 2-3 mJy/beam in a 0.5 kms-1 channel for the
full 32 minutes of data per region.

Figure 1: Intensity-velocity spectra of the strongest pixel in the J1944 -1452
1 detection in Field 7, taken on 0.5kms-1 channels.

The data were reduced using standard techniques for spectral line reduction in
the National Radio Astronomy Observatory (NRAO) Common Astronomy Software
Applications (CASA) package. All velocities used in this paper are in the
heliocentric reference frame, with an adopted rest frequency of 22.23508 GHz
for the 616-523 water maser transition. For these D-array observations we
achieved a synthesised beam of approximately 4 by 4 arcseconds. Data cubes of
the entire primary beam (150 by 150 arcseconds) and a restricted velocity
range of $\pm$150 kms-1 in 0.5 kms-1 channels around the systemic velocity of
NGC 6822 (-57 kms-1) were created from both the combined scans of each region
and separate left- and right-polarised observation data. This velocity range
was chosen to cover $\pm$130 kms-1 beyond the range observed in the natal gas
in NGC 6822.

##  3 RESULTS

The data cubes were inspected both through manual visualisation within CASA
and with the DUCHAMP source-finding package (Whiting (2012)) to find potential
maser emission. For a point to be considered potential emission, it needed to
be within 120 arcseconds of the cube center (corresponding with a 2-arcminute
primary beam FWHM); have an SNR of at least 6$\sigma$ in the cube; be present
in both left- and right-polarised data cubes with an SNR of at least
4$\sigma$; and persist across multiple adjacent channels (to a width of
60kHz). Under these conditions, a total of 69 potential sources were
identified.

A properly calibrated image cube where there is no emission is expected to
have noise characteristics described by a Gaussian distribution with a mean of
zero. To assess the likely false positive rate of our method for identifying
potential maser sources, we repeated our search process on the negative half
of the image cube. Both halves contained an overabundance of 5-7$\sigma$
points compared to a true Gaussian distribution. A similar number of
’potential emissions’ were detected in the negative half as the positive half
within this intensity range. However, for ’potential emission’ beyond
7$\sigma$, the equivalence in the positive and negative halves of the cube
disappeared - Field 7 had no negative ’potential emission’ points past
8.5$\sigma$ while Field 3 had none above 7$\sigma$. While small number
statistics means that the four largest excursions from the mean in the image
cubes being found entirely in the positive half may be chance, other cubes (of
other fields where no water maser candidades were identified) show symmetry
between the positive and negative distributions. These new thresholds were
taken to be the lower limits above which positive emission could be considered
strong maser emission candidates.

Using this approach, from the 69 potential sources we have identified four
water maser candidates in NGC 6822. With a single epoch of observation, their
derived positions are accurate to within a few arcseconds. The characteristics
of the candidate detections are summarised in Table 2.

Table 2: Candidates considered strong or reasonable targets for redetection in any field, arranged by decreasing estimated candidate strength. Vbary is the barycentric velocity, while Vbary \- Vsys is the velocity offset from the systemic velocity of NGC 6822. Listed corrected intensities have been corrected for primary beam response; line spacing denotes significant gaps in predicted likelihood. Source | Candidate | Vbary | Vbary \- Vsys | SNR | Right Ascension | Declination | Flux Density | Corrected Intensity
---|---|---|---|---|---|---|---|---
|  | kms-1 | kms-1 | $\sigma$ | Hours (J2000) | Degrees (J2000) | mJy | mJy
J1944 -1452 | 1 | -109.9 | -52.9 | 9.13 | 19:44:52.0 | -14:52:23 | 20.6 | 27.3
J1944 -1442 | 2 | -116.9 | -59.9 | 7.86 | 19:44:36.5 | -14:42:13 | 18.2 | 29.9
J1944 -1452 | 3 | -169.7 | -112.7 | 9.24 | 19:44:50.6 | -14:52:53 | 20.9 | 21.2
J1944 -1442 | 4 | 73.9 | 130.9 | 9.67 | 19:44:36.7 | -14:42:53 | 22.4 | 56
Figure 2: Intensity-velocity spectra of the strongest pixel in the J1944 -1452
3 detection in Field 7, taken on 0.5kms-1 channels.

##  4 DISCUSSION

The characteristics of the candidate 616-523 water maser detections are listed
in Table 2. The four sources have maximum peak flux densities in the range
21-56 mJy and velocity offsets between 40 and 121 kms-1 from the gas content
dispersion of NGC 6822. Comparing these to the sample of water masers found by
Walsh et al. (2014) in a follow-up study of the H2O Southern Galactic Plane
Survey (HOPS), these offsets lie at extreme but plausible velocities, and
correlate with the highest reported intensities of that survey. However, it
should be noted that the HOPS survey was not targeted at known high intensity
water masers (e.g., Ellingsen et al., 2010), and the candidate detections
towards NGC 6822 are significantly lower in intensity than the brightest
Galactic sources would be were they placed at a distance of 479 kpc.

###  4.1 Luminosity distribution

Galactic water maser abundances can be estimated from the empirically
determined luminosity function of Greenhill et al. (1990)

$\textit{N}=10^{-0.6(1+log(L))},$ (1)

where $N$ is the number of water masers found above some luminosity $L$ in
units of $L$⊙. This luminosity function applies for water masers with
luminosities between 0.018 $L$⊙ and 1 $L$⊙. Further work by Brunthaler et al.
(2006) found good agreement between observed and derived abundances for
extragalactic sources beyond this range, provided $N$ is scaled by the
relative star formation rate (SFR) of the target galaxy compared to the Milky
Way. The resulting equation being

$\textit{N}=10^{-0.6(1+log(L))}\frac{\text{SFR${}_{target}$}}{\text{SFR${}_{Galactic}$}}.$
(2)

Assuming the Milky Way SFR is approximately 1.65 $M_{\odot}$year-1 (Licquia &
Newman, 2015) and NGC 6822 has an SFR of approximately 0.04 $M_{\odot}$year-1
(Jones et al., 2019), $N$ is unity for a luminosity of approximately 0.0002
L⊙.

There are no confirmed detections of masers associated with NGC 6822; the most
recent search being the work of Tarchi et al. (2020), discussed further in
section 4.3, which mapped the entire galaxy to a luminosity limit of 0.01 L⊙
(corresponding to a 4$\sigma$ limit of 320 mJy in 1.2kms-1 channels) for the
22 GHz water maser line. The lack of any strong candidate detections by Tarchi
et al. is consistent with earlier work by Brunthaler et al. (2006), who
searched the Hα-bright areas of the central region to a luminosity limit of
0.0008 L⊙ (corresponding to a 5$\sigma$ limit of 50 mJy over three 0.7 kms-1
channels). Both previous searches are consistent with Equation (2) in that
their sensitivity does not reach the 0.0002 L⊙ threshold below which one or
more maser detections are expected.

Converting between flux density and luminosity for masers depends the channel
width of the observations. For example, for the observations reported in this
paper we searched for emission in 0.5 kms-1 spectral channel, for which a
maser with an isotropic luminosity of 1 $L_{\odot}$ would have a flux density
of 379 Jy if it were located at a distance of 479 kpc. For a 0.5 kms-1
channel, $N$ = 1 under these conditions for a flux density of 75 mJy; at the
intensity of the weakest candidate (21.2 mJy), $N$ is approximately 2. The
observations presented in this paper covered a region of NGC 6822,
representing approximately half the galaxy’s SFR, so detection of 1-2 masers
with a peak intensity of 20 mJy or more is consistent with the
Greenhill/Brunthaler luminosity distribution relationship.

However, the extension of the Galactic luminosity function (1) to other
galaxies in (2) is not thoroughly empirically vetted; in a search of the Small
Magellanic Cloud (SMC) in 2014, Breen et al. (2013) report the detection of
four water masers above an intensity of 1.3 Jy in 0.5kms-1 channels, which
corresponds to a threshold of approximately 0.00007 L⊙. At this intensity, $N$
is 2.8, which underpredicts the observed maser abundance by a third.

###  4.2 Comparable masers

Another useful metric for investigating the potential nature of the observed
emission is comparison to specific known regions of water maser emission,
rather than a statistical comparison to the population of all masers of a
particular species.

The Orion KL nebula is the closest high-mass star-forming region, at a
distance of approximately 437 pc (Hirota et al., 2007). While it has
experienced extreme flare events in the past (e.g. Tolmachev (2000) and Garay
et al. (1989)), achieving flux densities on the order of 106 Jy, observation
between flares shows a relatively quiescent region (e.g. Gaume et al. (1998),
where the strongest feature is approximately 14 kJy). Assuming a distance of
479 kpc for NGC 6822, the weakest of our four candidate detections would have
a flux density of approximately 25 kJy were it located at the distance of
Orion KL. So the current observations would not be able to detect emission
from a quiescent Orion KL located in NGC 6822, however, historical flares
would be readily detected at levels between 200$\sigma$ and 2500$\sigma$. We
should also note that the water masers within Orion KL span a 50 kms-1
velocity range centred on the systemic velocity of the quiescent gas, much
more compact than that of the candidate detections.

Figure 3: Intensity-velocity spectra of the strongest pixel in the J1944 -1442
4 detection in Field 3, taken on 0.5kms-1 channels.

A more distant but more active Galactic high-mass star-forming region is the
HII region W49, at a parallax distance of approximately 11 kpc (Zhang et al.,
2013). The region, W49 North, hosts compact and bright HII sub-regions (De
Pree et al., 2000) with strong water maser activity. In McGrath et al. (2004),
a total of 316 water maser components were identified in W49N. 124 of these
components (spectral features) could have been detected were they at the
distance of NGC 6822; 23 of them were at least five times as strong as the
strongest candidate detection. The aforementioned 124 W49N water maser
components spanned a velocity range of 170 to -207 kms-1, well beyond that of
the natal CO gas, which covers a range of approximately 19 to -3 kms-1
(Miyawaki et al. (2009) identify the feature MHH-1 as corresponding to W49N).
Even considering only the sources of at least 1 kJy, the velocity range is
56.4 to -113.4 kms-1; while more compact, this is still a considerable
velocity range, on a similar scale to the candidate detections in NGC 6822.

Considering sources in galaxies other than the Milky Way, the NGC 6822
candidate detections are of a similar magnitude to those found by Breen et al.
(2013) in the SMC, with the strongest candidate at approximately 3.5 Jy (when
scaled to the distance of the SMC) and the others at 1.33, 1.71 and 1.87 Jy.
The strongest is thus slightly weaker than the strongest NGC 6822 candidate at
3.4 Jy.

Figure 4: Intensity-velocity spectra of the strongest pixel in the J1944 -1442
2 detection in Field 3, taken on 0.5kms-1 channels.

All of our NGC 6822 candidate detections have a very narrow velocity range of
a few kms-1, a smaller range than typical Galactic masers but similar to those
found in the SMC. This is not necessarily indicative of a physical difference,
as sensitivity limits mean the full extent of the emission is impossible to
determine, but may point to a qualitiative difference in environmental
conditions between Galactic masers and those in more gas-rich dwarf galaxies.

The NGC 6822 candidate detections are thus closest in spread to an atypically
active region such as W49 North, but in intensity more similar to the SMC or
the brightest edge of more typical Galactic regions such as Orion KL.

###  4.3 Previous Tentative detections in NGC6822

Tarchi et al. (2020) report tentative detections of five water masers in NGC
6822, given the placeholder names NL1K through NL5K. These tentative
detections have a similar range of line-of-sight velocities to those reported
in this paper - offset from the systematic velocity of NGC 6822 by between 50
km/s and 125 km/s.

Of these five, they identify NL1K and NL5K as being of particular interest, as
they are located close to regions of strong continuum emission in NGC 6822.
These correspond to regions 3 and 12 respectively in Cannon et al. (2006) \-
NL1K thus falls within a region observed by this project. Unfortunately, it
does not correspond to any of the candidate water maser detections presented
in this paper - the velocity of NLK1 is offset 80 km/s from the closest
candidate in that field (J1944$-$1442). Tarchi et al. also made additional
observations with the Sardinia Radio Telescope towards both NL1K and NL5K, but
in neither case were they able to confirm the tentative initial detection.

The 7-$\sigma$ detection limit for the current JVLA observations corresponds
to a peak flux density of $\sim$20 mJy. In contrast, the peak flux density of
NL1K measured by Tarchi et al. is 543 mJy around late 2015 and 2016, and
constrained to have diminished to less than 200 mJy when they attempted to
confirm the source in 2019. While significant variability is often found in
water masers associated with star formation, the JVLA observations we report
here were taken relatively close in time to the initial observations of NL1K.
A source with a peak intensity in excess of 100 mJy would have been readily
detected by our observations, strongly suggesting that NL1K was a false
positive in the Tarchi et al. search. This is consistent with Tarchi et al’s
own estimation of 0.65 false positives at 5 sigma and 72 positives at 4 sigma,
as none of the detected sources in NGC 6822 are above 4.6 sigma - and so the
number of tentative detections they report is not higher than would be
expected from Gaussian noise.

##  5 CONCLUSION

From a single epoch of targeted water maser observations conducted with the
VLA we have identified four water maser candidatess in NGC 6822. All of these
are below the detection limit of previous surveys. These candidates are
high-$\sigma$ detections, but their significant velocity offsets from the gas
content of NGC 6822 and detection at only a single epoch makes it likely that
some (perhaps all) are false positives.

The candidate masers are nearly coincident with two of the bright HII regions
in NGC 6822. Candidates 2 and 4 fall just off the western edge of Hubble
I/III, northeast of the main bar of the galaxy. Candidates 1 and 3 lie within
Hubble IV, in the southern part of the bar. The latter two candidates are
directly coincident with areas of high HI column density and radial velocity
dispersion. Candidate 3 in particular coincides with a CO detection in field 4
of Schruba et al. (2017) and is $\approx$5′′ from a candidate massive Stage I
YSO identified by Jones et al. (2019) using Spitzer photometry. Water maser
emission is not detected from the two brightest HII regions, Hubble V and X.

While the radial velocity range overlaps that of Milky Way gas at $l$ = 25∘,
it should be noted that the main spiral arms in this direction have
$v_{LSR}>0$, while three of the four candidates in Table 2 have significantly
negative velocities; furthermore, at $b$ = $-18^{\circ}$, the sightline toward
NGC 6822 is physically far below the Galactic plane at the typical distances
to known spiral arms, making it unlikely that the candidate NGC 6822 masers
are foreground Galactic sources.

If confirmed, they would be consistent with the brightest water masers
expected to be present in NGC 6822 from the luminosity function and will make
excellent targets for future VLBI observations to measure the proper motion of
NGC 6822 independently of Gaia (McConnachie et al., 2021).

###### Acknowledgements.

The Karl G. Jansky VLA is part of the National Radio Astronomy Observatory.
This research has made use of: NASA’s Astrophysics Data System Abstract
Service; the NASA/IPAC Infrared Science Archive (operated by the Jet
Propulsion Laboratory, California Institute of Technology, under contract with
the National Aeronautics and Space Administration; the SIMBAD data base,
operated at CDS, Strasbourg, France; and data products from the SINGS survey,
which is a legacy science program of the Spitzer Space Telescope, funded by
the National Aeronautics and Space Administration

Figure 5: The locations of J1944 -1452 3 and J1944 -1452 1 superimposed on
false-colour images of Wide-field Infrared Survey Explorer (Wright et al.,
2010) 3.4-4.6-12 $\mu$m data (left) and Two-Micron All Sky Survey (Skrutskie
et al., 2006) J-H-K data (right) . The circles used to indicate the sources
J1944 -1452 3 (yellow) and J1944 -1452 1 (purple) have a diameter of 6
arcseconds, approximately the longest dimension of the point-spread function,
while the dashed circle corresponds to the primary beam (120 arcsecond
diameter). Figure 6: The locations of J1944 -1442 2 and J1944 -1442 4
superimposed on false-colour images of Wide-field Infrared Survey Explorer
(Wright et al., 2010) 3.4-4.6-12 $\mu$m data (left) and Two-Micron All Sky
Survey (Skrutskie et al., 2006) J-H-K data (right) . The circles used to
indicate the sources J1944 -1442 2 (purple) and J1944 -1442 4 (yellow) have a
diameter of 6 arcseconds, approximately the longest dimension of the point-
spread function, while the dashed circle corresponds to the primary beam (120
arcsecond diameter).

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Figure 7: The locations of the five observed fields superimposed on a false-
colour image of SIRTF Nearby Galaxies Survey (Kennicutt et al., 2003), using
their historical labels.


Emily<EMAIL_ADDRESS>
# Affect as a proxy for literary mood

Emily Öhman  Waseda University, Japan  Riikka Rossi  University of Helsinki,
Finland

###### Abstract

We propose to use affect as a proxy for mood in literary texts. In this study,
we explore the differences in computationally detecting tone versus detecting
mood. Methodologically we utilize affective word embeddings to look at the
affective distribution in different text segments. We also present a simple
yet efficient and effective method of enhancing emotion lexicons to take both
semantic shift and the domain of the text into account producing real-world
congruent results closely matching both contemporary and modern qualitative
analyses.

##  1 Introduction

In this study, we explore how the literary concept of mood can be studied and
detected with computational methods. We propose to use affect as a proxy for
mood and test our hypothesis first quantitatively on different segmentations
of the different texts, and then qualitatively against expert close-readings
of the same texts. We use multiple different natural language processing (NLP)
approaches to attempt to identify the origins, construction, and location of
mood. For this purpose, we have collected a corpus of nearly 1000 literary
works published in Finnish around the year 1900. Our paper utilizes many
common NLP methods and approaches from computational literary studies and
affective computing, but besides our pilot study (Öhman and Rossi, 2022), to
our knowledge, it is the first study to combine emotion detection/sentiment
analysis with the study of mood in texts.

We focus on mood as it is one of the more ephemeral yet pervasive aspects of a
literary text. Mood is typically described as the atmosphere that the author
creates through their word choices, style, and use of imagery and can
sometimes even include tone. The line between tone and mood can be difficult
to draw, particularly when approaching the topic with computational tools, but
succinctly the difference can be explained as mood being about how the reader
feels about the text, and tone about how the implied author feels about it and
uses words to convey their own attitude towards a topic or subject111On the
concepts of tone and mood in literary studies, see Richards (1929); Ngai
(2005); Flatley (2008). (Turco, 2020).

Although many different literary tools contribute to the creation of mood,
perhaps the most important one, that influences all the other tools as well,
is the choice of words. Typically in literature, the intentionality of word
choice is higher than in most other text genres (e.g. social media posts)
(Mitchell, 2008; Öhman et al., 2020). It is therefore a great subject for
analyzing the use and intensity of emotion-associated words in text. As there
are so many components that help create mood, in a larger sense mood is not
reducible to a single aspect, but generated by a set of textual elements,
however, we can use the importance of word choice to our advantage. If we look
at the affective distribution of the words in texts using different text
segments, we can attempt to pinpoint where in a text mood is created and how
that links to affect.

We suggest that the computational study of the valence of the lexicon of a
literary text can be valuable in providing an accurate picture of the
distribution of the positive and negative valence in a text continuum, and
thus help us to better understand the relationship between the linguistic
qualities of a text and its perceived emotional effects, particularly the mood
of a text. Furthermore, we contribute to the discussion of the most suitable
tools for interdisciplinary work including the debate about lexicon-based
methods versus machine learning as the most "accurate" (van Atteveldt et al.,
2021; Öhman, 2021; Teodorescu and Mohammad, 2022).

##  2 Background & Previous Work

Around the turn of the millennium the "affective turn" took place (Smith,
2011; Armstrong, 2014). It was a shift in attitude regarding the importance of
affect in humanities and social science research, including literary studies
(Kim and Bianco, 2007). Literature can be considered a domain where the
affective functions of language are of principal importance (Hogan, 2011). The
affective turn has led to a significant increase in research that focuses on
the affective side of text in literature (Armstrong, 2014).

The affective power of literary texts has been acknowledged since Aristotle’s
Poetics, but in the 20th century, many schools of thought such as formalism,
new criticism, structuralism and post-structuralism orientated the attention
to formal and structural aspects of texts, whereas the study of emotions was
excluded and considered as susceptible to researchers’ subjective emotions.
Research topics range from the study of literature and empathy (Keen, 2007) to
the study of literature and cognition (Hogan, 2011)), negative affects and
tone in texts (Ngai, 2005) to empirical perspectives (Sklar, 2013; Van Lissa
et al., 2016), and even emotions specific to Finnish literature (Rossi, 2020;
Rossi and Lyytikäinen, 2022).

Recent studies on emotions in Finnish literature demonstrate that Finnish
literature presents us with a rich body of work for developing the general
theory of literature and emotions and to study the ways in which genre-
specific emotional effects vary culturally and historically (Rossi and
Lyytikäinen, 2022). While this research has opened up new perspectives it has
also demonstrated there are a number of gaps and complex questions to resolve.
Along with the affective turn, the question of a text’s overall emotional tone
or mood has aroused vivid interest (e.g. Ngai Ngai 2005, Lyytikäinen 2017, and
Rossi Rossi 2020). However, a systematic theory of how tone and mood are
created and triggered in the reader is still in the works.

We suggest that a study of the emotional valence of the lexicon measured
quantitatively provides a new approach that can help with understanding the
components of a text’s mood (Öhman and Rossi, 2021, 2022). Furthermore, it has
been shown that lexicon-based methods can achieve better results in emotion
classification tasks than machine learning models, especially when the text
segment size is optimized (Öhman, 2021; Teodorescu and Mohammad, 2022).

Parallel to the affective turn, sentiment analysis became an active field of
research within natural language processing and computer science (Mäntylä et
al., 2018). Sentiment analysis and emotion detection has been used with
literary works with varying levels of success; Kim and Klinger (2018) provide
a substantive overview of sentiment analysis and emotion detection as it is
used in CLS. Although many of the papers cited in the review are interesting
and innovative, virtually none of them deal with topics that are common in
more traditional literary studies. Common CLS topics are genre classification
by emotion, story-type, sentiment tracking, and sentiment recognition (see
e.g. Sprugnoli et al. 2016; Schmidt and Burghardt 2018; Amano et al. 2023).

As interesting and innovative as previous CLS work is, it is typically of
little use to literary scholars studying affect. These CLS approaches rarely
work in harmony with literary analysis in the traditional sense and usually do
not even touch upon the topics that interest literary scholars such as tone,
mood, and emotion evocation. We hope that our efforts will contribute to
merging the talents and knowledge within CLS, digital humanities, NLP, as well
as traditional literary studies.

Although there are exceptions (see e.g. Hu et al. 2021 and Herrmann et al.
2019), it is somewhat rare for studies within the field of CLS to have
literary experts working on the project, and many such projects rely heavily
on the analysis of the quantitative results conducted by experts of NLP rather
than experts of literature. This is not a problem in only CLS, but in many
other interdisciplinary fields, particularly those with a computational
element (Bartlett et al., 2018). This is why we think it is imperative to
conduct CLS (and other interdisciplinary studies) with domain experts and not
just NLP knowledge and literary data.

CLS as a field has been criticized for providing either obvious results or
ephemeral results that are not robust enough for repeat analysis (Da, 2019).
Furthermore, there seems to be a pervasive belief that state-of-the-art
methods from NLP are the most accurate when used on non-standard unstructured
data regardless of the research question of the downstream application of such
methods. It is commonly suggested that machine learning methods are more
accurate than lexicon-based ones (see e.g. van Atteveldt et al. 2021), but
several recent papers have suggested this is not the case, especially when
dealing with emotional arcs and ideal bin sizes in verbose domains (Öhman,
2021; Teodorescu and Mohammad, 2022). We have taken the criticism of both
camps to heart in an effort to produce robust and reliable but also useful and
interpretable results.

##  3 Data

Our data collection was simple and straightforward. Although there are R
packages and Python libraries in existence for handling Project Gutenberg
downloads, Project Gutenberg discourages mass downloads using such methods,
thus we used their recommended method for filtering works using http queries
and downloading a set smaller amount of books at a time. We downloaded the
first222Presumably in order of entry to the database. 1000 books from Project
Gutenberg333https://www.gutenberg.org/, with two filter criteria: (1) the
language was Finnish, and (2) the text was in utf-8 plain text format. There
is currently no method for filtering out translated works so we estimate that
the dataset consists of approximately 50% translated texts. The data is
publicly available444https://github.com/esohman/FinLit-corpus.

We used the simple gutenberg-cleaner555https://libraries.io/pypi/gutenberg-
cleaner to get rid of the preamble and the legal text at the end of the book,
then we created a regex to extract key information such as the title, the name
of the author, the year of publication, and whether the book was originally
written in Finnish. The translation status of the book was extracted based on
whether the terms suomentaja, suomennettu, suomentanut, or any version of
kääntäjä/käännös/käännetty etc.666All the terms roughly translate to
”translator” or ”translated”. were present within the first ten lines of text
after the preamble was removed.

Although we filtered books based on their encoding, a fairly large number of
the books were not actually utf-8 encoded and had to be decoded and re-
encoded. We used automatic encoding detection and tried to convert the texts
to utf-8, but for some works this failed and in the end due to these encoding
issues, our final corpus consists of 975 books instead of 1000. A vast
majority (95+%) were written or translated between the years 1850 and 1925 and
over 90% after 1880, with only a few instances of older texts, which means
that the language used in the texts can be considered Modern Finnish (Forsman
Svensson, 2011). The final data consists of 2,938,032 sentences and 41,417,116
tokens.

###  3.1 The Emotion Intensity Lexicon

We used the Finnish Emotion Intensity Lexicon (FEIL) (Öhman, 2022) as a
starting point for detecting affective terms777https://github.com/Helsinki-
NLP/SELF-FEIL. FEIL is based on the NRC emotion lexicon (Mohammad and Turney,
2013) and emotion intensity lexicon (Mohammad, 2018) and has been adapted for
Finnish. It lists words alongside the emotions they are associated with as
well as the intensity of the associated emotion as a number between 0 and 1.
The emotions roughly correlate with Plutchik’s wheel of emotions (Plutchik,
1980) and contains the emotions anger, anticipation, disgust, fear, joy,
sadness, and trust. We follow the best practices and ethical guidelines as set
forth by Mohammad (2022a, b).

##  4 Method

Finnish is a fairly easy language to work with in terms of NLP. There are
numerous high-quality resources that are actively maintained and new tools are
constantly being developed for various written standards of Finnish and
Finnish-adjacent languages. Nearly all of these resources are also open-source
and freely available (Hämäläinen and Alnajjar, 2021). Therefore, we were
privileged enough to have several different lemmatizers and tokenizers at our
disposal, some even specifically made for older Finnish texts. We also added
to this list by creating a Finnish version of the chapterize Python
package888The original: https://pypi.org/project/chapterize/ and the Finnish
version: https://github.com/esohman/chapterize-fi package.

Once the data was cleaned, we preprocessed it by lemmatizing, splitting it
into paragraphs, and tokenizing the texts. We attempted to fine-tune Finnish
BERT (Virtanen et al., 2019) to work with our texts (as per Gururangan et al.
2022), but the vocabulary was not improved sufficiently to work with data so
different from the original training data for Finnish BERT. In order to find
the best tool for our data we tried multiple different lemmatization tools.
These tools included the Turku Neural Parser (Kanerva et al., 2019), murre
(Partanen et al., 2019; Hämäläinen et al., 2021), and both the experimental
and news Finnish spaCy models. In the end we settled for the Turku Neural
Parser as the results were the most accurate (see table 1 for an example) and
all words were not only parsed but parsed correctly in context as well.

In the example, the Turku Neural Parser was the only one able to correctly
parse the nonstandard form kahvians – standard form: kahviansa – partitive
case of 3rd. pers. sing./plur. coffee. Incidentally, in the dissertation of
Airio (2009) kahviansa is discussed as an example of "parasite words" since it
can be mistakenly split into kahvi (coffee) and ansa (trap), something none of
the lemmatizers did. With careful optimism, we take this as a demonstration of
how good lemmatizers for morphologically complex languages have become in the
past decade.

Translation The provost sits down in his rocking chair, stands his pipe on the
floor against the table leg, and starts drinking his coffee Original Rovasti
istuutuu keinutuoliinsa, panee piippunsa lattialle pöydän jalkaa vasten
pystyyn ja rupeaa juomaan kahvians Murre (hist) (öljy)movasti istua keinutuoli
panna pippu latija pöytä jalka vaste pystyä ja ruveta juoma kahvis spaCy
(news_lg) Rovasti istuutua keinutuoliinsa panee piippu lattia pöytä jalka
vasten pystyyn ja rupeata juoda kahvians spaCy (exp. /w voikko) rovasti
istuutua keinutuoli panna piippu lattia pöytä jalka vasten pystyyn ja ruveta
juoda kahvians UralicNLP Rovasti|rovasti istuutua panna piippu lattia pöytä
jalka vasten pystyyn|pysty ja ruveta juoma|juoda Turku Neural Parser rovasti
istuutua keinu#tuoli panna piippu lattia pöytä jalka vasten pystyyn ja ruveta
juoda kahvi Ideal lemmmatization rovasti istuutua keinutuoli panna piippu
lattia pöytä jalka vasten pystyyn ja ruveta juoda kahvi

Table 1: Example of lemmatization using different lemmatizers for Finnish.

The different lemmatizers had different strengths and weaknesses. For example,
spaCy is very easy to use and install, it is reasonably fast for this amount
of text, and also produces other information that most of the other
lemmatizers leave out. It is therefore great for exploring other aspects of
the data such as NER and similarity scores. However, the Turku Neural Parser
produced the best results in an easy-to-use
pipeline999http://turkunlp.org/Turku-neural-parser-pipeline/. Omorfi (Pirinen,
2015) and FinPos (Silfverberg et al., 2016) might also have been good
candidates for lemmatizers, but these proved difficult to install on the
platforms available for this project.

Once we had the texts lemmatized and tokenized, we attempted to identify the
first three paragraphs of each text. This was a more complex process than
expected as even after removing the preambles/headers and footers,
miscellaneous metadata of various shape cluttered the start of the book. There
was very little uniformity or even commonly recurring patterns of where the
actual text of the book or chapters starts. This led us to create a Finnish
version of the chapterize package for Python, which enabled use to split texts
into chapter and recognize opening paragraphs when used in conjunction with
the sentence and paragraph ids provided by the conllu metadata generated by
Turku Neural Parser. We used two different text sections as targets for
overall mood detection: the first three paragraphs of each book, and the first
200 tokens from each chapter in each book.

The size of the bins was chosen based on findings by Teodorescu and Mohammad
(2022) that suggest even at a few hundred tokens, lexicon-based methods can
excel at estimating emotion arcs beyond current state-of-the-art machine
learning capabilities. They suggest that lexicon-based approaches are more
suitable "for applications where simple, interpretable, low-cost, and low-
carbon-footprint systems are desired" (Teodorescu and Mohammad, 2022, 8).

Previous studies (Öhman and Rossi, 2021) have made it clear that although the
results from lexicon-based emotion detection can be very accurate in terms of
real-world congruency, certain words can easily obfuscate the results. One of
the literary works used as quality assessment and proof-of-concept the novel
Rautatie (tr. as The Railroad) by Juhani Aho focuses on the novelty of a
railroad track coming to a peripheral rural village and naturally contains
many instances of the word railroad, the fact that the word itself was
associated strongly with trust in the lexicon skewed the results and were not
representative of the level of overall trust in the novel. Therefore we opted
to remove the term from the lexicon.

On the other hand, FEIL contains mostly contemporary words and their
contemporary emotion associations. It is reasonable to assume that these words
and their associations would have been subject to semantic shift. Hence we
needed to ensure that first and foremost the most common words in our texts
that in our subjective opinion have an emotion association are indeed in the
lexicon, and secondly that the most common words that match words in the
emotion lexicon are labeled correctly for emotions at reasonable intensities.
These two steps are iterative and continuous in that they should be repeated
whenever the lexicon or lemmatization is altered.

Removing words that carry little to no emotion association is fairly
unproblematic, however, altering emotion labels or intensity scores or adding
new words to the lexicon should be avoided. If necessary, such additive
alterations require the use of multiple annotators who are not the authors and
cross-checking the results using inter-annotator agreement scores (van
Atteveldt et al., 2021).

For this reason, we did not want to introduce new biases into the lexicon
based our own interpretations of emotion intensities. Thus we developed an
alternative method that would allow us to easily and objectively add words to
the lexicon. To enable this we created word embeddings of our texts and used
them to look up words in the lexicon with high cosine similarity to the words
we wanted to introduce to the lexicon (as per e.g. Maas et al. 2011; Yu et al.
2017; Ye et al. 2018). This lead to the association for e.g. the words kirkas,
valkoinen, and valkea (clear, white, white/light/bright often referring to
fire or morning light) to be identical. The words that needed to be added were
relatively few so we were able to manually check that their emotion
associations and intensities made sense. For future projects, we intend to
employ human annotators in conjunction with word embeddings. Nonetheless, this
approach alone showed much promise and was very accurate within the small
sample size in both recognizing semantically similar words and in mitigating
issues with semantic shift.

Ultimately we ended up removing 128 entries from the lexicon and adding 203
tokens including rakastaa, to love. As the FEIL lexicon was based on an
English lexicon, this exlusion of such a central emotional term demonstrates
the issue with noun and verb distinctions in Finnish compared to English. The
noun and verb forms are often the same in English unlike in Finnish where the
forms are distinct (cf. to love/a love, to run/a run vs. rakastaa/rakkaus,
juosta/juoksu). Many of such instances were addressed in the creation of FEIL
with the addition of verb forms copying the intensity scores and emotion
associations of the English words, yet many verb forms are still missing from
the lexicon.

We used this domain- and period-specific version of FEIL to tabulate
normalized (per token count for inter-text comparability), intensity scores
for each target text. Other future projects should include checking that both
noun and verb forms are found in the lexicon.

Figure 1: Co-occurence of emotions in FEIL.

The co-occurence matrix shows that emotion associations are not linear. A word
associated with anger is quite likely (0.5) to also be associated with fear,
but a word associated with fear is slightly less likely to be associated with
anger (0.42). Words associated with sadness are highly likely to also be
associated with fear, however, due to the very low number of sadness-
associated words in the lexicon, fear-associated words are much less likely to
simultaneously also evoke sadness (0.09). Joy is also the only purely positive
emotion in the lexicon, which means it is more likely to be associated with
many positive emotions beyond joy itself due to annotation process conducted
with best-worst scaling (Kiritchenko and Mohammad, 2017) (ranking words
associated with a specific emotion in terms of least to most associated).

As mentioned earlier, tone and mood can be difficult to distinguish from each
other and can even be intertwined to different degrees, especially if we focus
on words alone. However, we argue that the tone of a literary text tends to
shift much more within and between chapters, and therefore by focusing on the
first paragraphs of each chapter, or even the opening paragraphs of the first
chapter only, we can get a fairly accurate idea of the mood of the text due to
its more stable nature, with a smaller risk of it being confused with tone.

It is well-established that first impressions matter in literature and
beginnings tend to shape the experience of reading and set the mood for the
whole text. Tone might vary within one text depending on changing narrative
viewpoints or even narrators, or switching from descriptive language ot
dialogue. Theories of perception (e.g. Perry 1979) argue that the openings
play a crucial role in creating a text’s overall emotional disposition. The
affective language and emotional effects created at the beginning of a text
modify and adjust the reader’s general emotional orientation by shaping up
modes of perception and organization of information. For instance, the
melancholic mood created in the beginning of Aho’s Rautatie (Railroad), or the
strong effects of disgust in the beginning of Sillanpää’s Hurskas kurjuus
(Meek Heritage), are likely to influence the reader experiences later
reactions and feelings triggered by narrative events.

##  5 The Mood in Selected Texts

As the results mostly consist of a dataframe with emotion intensity scores for
each text, the results do not easily lend themselves to visual
representations. Therefore, we are focusing the presentation of our results on
a small subset of data. We chose four texts based mostly on the second
author’s area of expertise and previous intimate analyses of the affective
landscapes of these texts. The scores for the chosen texts’ first three
paragraphs are presented in table form in table 2 and f0or both approaches
comparatively in figures 2 and 3.

title | anger | anticipation | disgust | fear | joy | sadness | trust
---|---|---|---|---|---|---|---
Putkinotko | 4.22 | 8.67 | 5.42 | 11.75 | 15.69 | 1.47 | 22.07
Kauppa-Lopo | 12.45 | 5.71 | 7.40 | 19.11 | 7.66 | 0 | 0
Rautatie | 8.56 | 21.15 | 0 | 10.50 | 13.81 | 5.52 | 16.74
Hurskas kurjuus | 20.33 | 9.90 | 11.25 | 27.73 | 9.07 | 7.29 | 15.05
Table 2: Normalized emotion scores for each novel’s first three paragraphs.

###  5.1 An overview of the selected texts

The first one is Juhani Aho’s breakthrough novel Rautatie (tr. as The
Railroad, 1884). In this text, from the perspective of the implied reader, the
novel evokes emotional effects of melancholia and nostalgia, which are
characteristic of Aho’s work.

The second one is Minna Canth’s Kauppa-Lopo (no translation, the title refers
to the protagonist’s nickname, 1889), a tragic story of poverty and illness.
The beginning of the novella, set in prison, underlines the anti-hero’s ugly
appearance, but the narrative contrasts the physical ugliness with an inner
goodness: she is described as good-hearted and compassionate towards other
people. The story was met with anger in contemporary audience, and the critics
did not value her social criticism. Canth’s naturalism was considered "poor
art" by her contemporaries and she was accused of being an admirer of disgust,
“destroying the laws of beauty, unfolding ugliness in every sense” (Rossi,
2007, 52).

The third one is Frans Emil Sillanpää’s Hurskas kurjuus (tr. as Meek Heritage,
literally “Sacred Misery”, 1919), which begins with a shocking prologue which
anticipates the death of the protagonist: it describes the execution of a poor
tenant farmer who had ended up as a Red Guard soldier in the Finnish Civil War
(1918). Despite the negative emotions and the tragic events the narrator also
expresses trust and comfort in the future of the Finnish nation. When
Sillanpää’s novel appeared it was met with confusion.

The fourth and last one is Putkinotko (no translation, 1919-20) by Joel
Lehtonen. Like Sillanpää’s Sacred Misery, this novel tracks the tensions that
escalated in the Finnish Civil War in 1918. The novel’s protagonist, a good-
hearted yet self-willed tenant farmer resigns to obey the landlord and instead
resorts to illegal distillery to support the family. The novel is emotionally
ambivalent: the idyllic descriptions of Finnish summer nature and the comic
elements are likely to arouse positive emotions, while the unembellished
description of poverty intends to evoke moral anger and sadness over social
inequality.

###  5.2 Results

The results for the texts are presented in figures 2 and 3.

Figure 2: Emotion word distribution in first three paragraphs per 1000 words
Figure 3: Emotion word distribution in the first 200 tokens of each chapter
per 1000 words

In figure 2 we can see some patterns emerge. In particular, the laconism of
Rautatie is evident when compared to the other authors and the strong early
emotional impact of Hurskas Kurjuus becomes very apparent, with fear and
anger, but also sadness being particularly notable. Fear and to some extent
anger are also highly present in Kauppa-Lopo, likely due to the descriptions
of the prison environment and the appearance of the protagonist. Trust and joy
are the most notable emotions in Putkinotko, perhaps due to the detailed
descriptions of the idyllic landscape that dominate the opening chapter.

When comparing the two different segmentations, first paragraph-only vs.
opening paragraphs of each chapter, the latter shows a stronger prevalence of
positive emotions. We take this to indicate that the intended opening mood for
these novels is constructed on negative emotions intended to evoke strong
feelings in the reader. In the former segmentation, the emotion distribution
is more varied and seemingly converging on the distribution of emotion words
in the lexicon.

##  6 Discussion

Our computationally derived results correspond highly with qualitative
evaluations of the same target texts in terms of established interpretations
of mood specifically when compared to the emotion word distribution of the
first three paragraphs of a literary text. The valency and intensity of
emotions in the opening paragraphs of the book and the opening paragraphs of
all chapters the emotions approach the distribution of emotions in the lexicon
in the former segmentation. That is, they become muddled because they are not
focused enough. Only minor effects of the authors’ style can be discerned when
applying this method across all chapters. Not only are the differences between
chapters reduced with the former, all-chapters, approach, but the differences
between the texts also become less clear. This could also be in part because
the focus and narrative choices become more varied and therefore the results
average out and start to converge on the distribution of emotions in the
lexicon. We recommend that the quest for mood should begin with the opening
paragraphs of a text.

The qualitative analysis of the selected works, summarized above, has paid
attention to various aspects of depicting and triggering emotions in a
literary texts:

  1. 1.

the characters’ and the narrators’ emotions

  2. 2.

the emotional effects targeted at the implied reader

  3. 3.

the empirical readers’ reactions in contemporary reception

  4. 4.

the texts’ tone, the organizing feeling of a literary work, which is never
reducible to a reader’s emotional response to a text, nor to a text’s internal
representations of feeling (on the concept of the emotional effect, see
Lyytikäinen 2017; on the notion of tone, see Ngai 2005, 28.

It should be noted that evoking emotional effects in literature is not
restricted to emotion-associated words or to direct descriptions of the
character’s emotions. All facets of the narrative, from the description of
objects to the narrative point of view and style, including tropes and even
the rhythm of the text are important aspects in triggering emotional effects
in the reader. As an example, the melancholic tone of Juhani Aho’s text is not
generated by themes of separation and loss alone but also by Aho’s style,
which favors fragmentation and loosening of syntax, with a recurring mannerism
of three points "…", as a sign of hesitation and withdrawal, even evoking a
depressive loss of contact.

The qualitative analysis demonstrates that the selected texts depict and
trigger negative emotions in particular: feelings of deception, fear, anxiety,
disgust and hatred, anger, moral indignation and melancholia. This can be
partly explained by genre-specific emotional effects: a critical naturalist
novel tends to shock and challenge its reader by representing and inciting
strong negative emotions, which confirm the effect of the reality of a text
and direct the reader’s attention to the social defects described. For
instance, the emotion of disgust, a genre-specific emotion of the naturalist
novel, is a named emotion and salient in Sillanpää’s and Canth’s novels in
particular (Rossi, 2007, 2017, 2020).

The salience of negative emotions can be explained by the importance of
negative emotions in literature and art in general. As discussed by
Menninghaus et al. (2017) negative emotions are an important resource for the
arts, since negative emotions have been shown to be particularly powerful in
securing attention, intense emotional involvement, and high memorability, and
hence is precisely what artworks strive for. Narrative plots routinely involve
social conflicts and both represent and elicit negative emotions in response
to such conflicts: failing marriages, unhappy love, long separations,
adultery, betrayed friendship, and the like. In narratives, happiness is
generally not described in great detail but rather evoked as a peak moment to
be challenged, or as a goal to strive for: Canth and Jotuni depict the
characters’ desire for happiness and love, which is not possible in today’s
society; Sillanpää’s narrator expresses trust in and hope for the future of
Finland, and Aho concludes The Railroad with the normative happy ending of a
fairy tale - yet this happiness is only evoked, but not described in detail.

##  7 Future Work

This dataset will be used for more robust detection of tone and mood in
Finnish literature using many novel approaches. The approaches developed for
this dataset will also be used with other literary datasets in many other
languages. Our preliminary studies show that the "big data" results support
qualitative analyses and further justify the use of purely lexicon-based
methods and affect as a proxy for mood when dealing with larger collections of
text where word choice is an important factor in evoking affective states in
the reader. Explicitly, we can see that the choice of emotion-associated words
in the first three paragraphs correlates highly with established analyses of
mood in the selected texts. We hope to add established emotion categories from
literary affect studies (see e.g. Hogan 2011) to the lexicon as a measure to
further improve the usability of the FEIL lexicon for the literary domain
(Öhman, 2020). Additionally, we would like to expand on the methodologies used
in this exploratory study and hopefully create more and more robust approaches
to tone and mood detection in literature and perhaps also incorporate
intentionality detection (Guo et al., 2009) to further separate tone from
mood.

## Acknowledgements

This work was supported by JSPS KAKENHI Grant Number 22K18154.

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