=Paper=
{{Paper
|id=Vol-3171/paper37
|storemode=property
|title=Keyword-based Study of Thematic Vocabulary in British Weather News
|pdfUrl=https://ceur-ws.org/Vol-3171/paper37.pdf
|volume=Vol-3171
|authors=Nataliya Bondarchuk,Ivan Bekhta,Oksana Melnychuk,Olha Matviienkiv
|dblpUrl=https://dblp.org/rec/conf/colins/BondarchukBMM22
}}
==Keyword-based Study of Thematic Vocabulary in British Weather News==
https://ceur-ws.org/Vol-3171/paper37.pdf
Keyword-based Study of Thematic Vocabulary in British
Weather News
Nataliya Bondarchuk 1, Ivan Bekhta 2, Oksana Melnychuk3, Olha Matviienkiv4
1
Lviv Polytechnic National University, Lviv 79013, Ukraine
2
Lviv Polytechnic National University, Ivan Franko National University of Lviv, Lviv 79013, Ukraine
3
Rivne Medical Academy, Rivne, Ukraine, 33017, Ukraine
4
Ivan Franko National University of Lviv, Lviv, 7900, Ukraine
Abstract
The survey centers on the examination of keywords and related themes representing
weather in four daily British newspapers (The Times, The Guardian, The Daily Mail, The
Sun) between 2014 and 2017. The articles in this period mentioning weather news
represent the corpus of our research. The goals of the research are the following: expose
frequently occurring words (keywords) in the corpus, categorize them into groups
according to relevant themes in the text, identify the quantitative content of each lexical-
thematic group, as well as determine dominant themes of weather news. The computer
software that was used to establish keywords is WordSmith Tools 7.0 with the British
National Corpus as a reference corpus. A method for automatic cataloging of keywords is
described. The corpus contains 746 324 words taken from 180 newspaper articles under
research.
Despite the necessity of keyword study, thematic and quantitative analyses provide deeper
insight into text-specific weather-related vocabulary and its textualizing role. The analysis
of quantitative data helps to select two dominant lexical-thematic groups ‒ “Weather
extremes” and “Weather and people”, giving evidence of central themes discussed in
weather news. Hence, the resulting major themes are the depiction of adverse weather
conditions affecting people’s daily life; the representation of the effects of weather
disasters on people and their environment. The obtained results highlight the link between
a theme/themes and lexical level of the text proving the efficiency of keyword analysis in
the research.
Keywords 1
keyword analysis, lexical-thematic groups, quantitative analysis, weather, WordSmith
Tools, corpus, vocabulary
1. Introduction
Keyword analysis is a widely used method in various sciences and fields, in particular corpus
linguistics. Egbert and Biber suggest that it is used “to identify the words that are especially
characteristic of the texts in a target discourse domain” [1]. Keyword extraction is an optimal way while
clustering, classifying, indexing and visualizing texts of different discourses, genres or text types.
However, the application of keyword analysis to the text or corpus requires further interpretation of the
results since keywords which are blindly extracted on the basis of their frequencies do not convey
1
COLINS-2022: 6th International Conference on Computational Linguistics and Intelligent Systems, May 12–13, 2022, Gliwice, Poland
EMAIL: nataliia.i.bondarchuk@lpnu.ua (N. Bondarchuk); ivan.bekhta@lnu.edu.ua (I.Bekhta); melnychuk_oksanadm@ukr.net (O.
Melnychuk); olha.matviyenkiv@lnu.edu.ua (O. Matviienkiv);
ORCID: 0000-0002-5772-8532 (N. Bondarchuk); 0000-0002-9848-1505 (I. Bekhta); 0000-0003-4619-363x (O. Melnychuk); 0000-0002-
4791-0277 (O. Matviienkiv)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
relationship with other words/keywords or texts. Considering the key words at the linguistic level, the
main idea, as stated by M.Scott, is that keyness is not language dependent, but text dependent [2].
Drawing from this, the advantage of the use of keyword analysis lies in the extraction of text-specific
vocabulary. Therefore, in our research generation of keywords is a starting point of the analysis for
further categorization of keywords into thematic groups representing weather. We understand thematic
groups as groups of lexical units used within the text interchangeably to convey certain semantic
meaning [3].
Prior surveys of such nature concentrated more on sentiment and quantitative analysis of weather
vocabulary [4, 5, 6], corpus-based analysis of weather metaphors [7] and climate representation [8].
The absence of relevant computer-based studies on the topic constitute the topicality of the survey. This
paper presents an interdisciplinary approach that incorporates linguistic and computer-based (statistical)
techniques to the analysis of weather-related vocabulary to define dominant themes that are specific to
weather news of British press, thus offering prospects for a better understanding of its contextualizing
and textualizing role in newspaper discourse.
The primary objective of this paper is to present an argument for the definition of keywords
according to different approaches. The secondary aim is to automatically extract keywords form the
research corpus and organize them into lexical-thematic groups, as well as to find out their quantitative
composition. As a result, dominant themes circulating in the texts of weather news may be defined.
2. Related works
The study of keywords is associated with the works of G. Matore [9], R. Williams [10], M. Scott
[11, 12], Tribble [13], P. Baker [14; 15], T. Berber Sardinga [16], M. Bondi [2], A. Wierzbicka [17], L.
Jeffriesand, B. Walker [18], J. Sinclair [19, 20], J. Firth [21], N. Fairclough [22], Gries [23], A.Wilson
[24], M. Stubbs [25, 26], M. Nelson [27], G. Leech [28], M. Mahlberg [29], J. Culpeper [30], T.
McEnery [31], G. Wilcock [32]. The notion of “key word” is multi-faceted and understood in different
senses in various disciplines. From a sociological point of view, key words are part of the vocabulary
of culture and society [10]. These are the words that have a special status, express an important social
meaning and play a special role. From a linguistic point of view, they contribute to the long-lasting
search for meaning [19] and are the most important units of the semantic and stylistic structure of the
text. In corpus linguistics, keyword is defined as a word which occurs with significantly high frequency
in one corpus when compared to some appropriate normative corpus (Scott, 1997; Scott & Tribble,
2006).
Paying attention to the importance of keywords in creating textual content and meaning, it is
believed that keywords are lexical units with the greatest semantic content contributing to the structure
and semantic framework of the text [19]. This makes keywords an effective method for identifying
lexical characteristics of texts [34]. The new research shows a tendency for ambiguity in the
terminological definition of keywords, which can be seen in three approaches:
Cultural (Matore, J. Firth, R. Williams, A. Wierzbicka). The first researchers (J. Firth, R. Williams),
who discussed key words, were intuitively focused on words which, in their opinion, contain important
notions reflecting social or cultural problems. Already in the 1930s, J. Firth proposed to study socially
important words that could be called "focal" or "pivotal", and advocated an analysis of the distribution
of words, the meanings of which characterize the society in specific contexts, with specific associations
and values [21]. R. Williams tried to analyse modern culture by studying key words and established a
close link between key words and discursive society [10]. However, while performing this analysis, he
focused on historical and social macro contextual factors without paying special attention to text and
genre and leaving the methodological tools of text analysis completely out of consideration.
Quantitative (M. Scott). Based on the concept of corpus linguistics, M. Scott differentiated key
words by means of statistical characteristics. A word is deemed key if it is used in the text at least as
many times as the minimum frequency of occurrence is estimated by the user [11], or key words are
words whose frequency of occurrence in the text is exceptionally high, if we compare them with other
words [33]. Identification of elements that are repeated with statistically significant frequency is not an
analysis or interpretation of the text or corpus, but indicates the elements that need to be investigated
and explained. M. Scott distinguishes three types of key words: proper names, words that people
themselves consider to be key words and are indicators of the “aboutness” of a particular text, and
especially high-frequency words that are more indicative of style than of subject matter [12]. When
talking about the topic and style of the text, as well as the role of key words in their identification,
attention is paid to what semantic structures are indicated by key words and in what way the author's
view influences them in the process of text creation [2]. M. Scott compares the theme ("aboutness")
with the mental meta functions of M. A. K. Halliday [11]. The words gain meaning not from the link
between the word and the meaning, but from the intrinsic interaction with other words. Later, P. Baker,
using M. Scott's classification, described lexical key words (nouns, adjectives,) as subject words, i.e.
words that can be used to identify the topic of the text [15]. However, the key words are not only
elements of the conceptual, but also of the grammatical structure of the text. Apart from informational
conjugation, they are indicators of communicative intention and micro- or macrostructure of the text.
The text is stored in the memory in a set of key words, which are then revealed during its retrieval.
Therefore, the notions of key and subject words are not identical.
Lexical-thematic groups include words that constitute components of one main thematic line, the
elements of which realise a certain idea, while the key words either serve individual thematic blocks of
the text (local thematic words) or implement, together with other text elements, the ideological idea of
the whole work (universal key words) [22]. Consequently, key words of the lexical-thematic group are
frequently varied units of the lexical organization of the text. They play an important role in the lexical
structure of the text, they take part in shaping the content and creating the meaning for an adequate
comprehension. The key word as a stimulus word, a source of textual associations, based on linguistic
(paradigmatic, syntagmatic) and extralinguistic (thematic) links of lexical units, performs the function
of a core, which directs the process of text comprehension. This approach appears to meet the tasks of
our research the most.
Phraseological (M. Stubbs). Key words are defined as phraseological units and phrases that are
constructed according to similar word models [25]. Assumption of key meaning through frequency can
also be seen in word forms, lemmas, and word sequences. This definition can easily be applied to more
complex units than words, pointing to current trends in descriptive and theoretical linguistics, in
particular phraseology. In essence, key words are not necessarily individual words, they can be clusters
or even phrases [20, 29]. A quite different approach was taken by M. Hoey, who, taking the category
of text as a basis, showed that lexical links in a speech can be considered as indicators of text structure
or potential acceleration or lexical models can reveal textual (as opposed to grammatical) models [36].
Key words are not necessarily the main ones in the textual sense, but they can help to understand the
idea of the text by repetition. M.Toolan mentioned repetition as one of the key word figures, which has
"a very rich semantic meaning" [37]. The key words are intended to focus the reader's attention on the
necessary state of speech in the production of a coherent text. They can act as markers of coherence of
this or that text and at the same time give the texts a unique author’s style.
3. Methodology
The procedure of our analysis involves the choice of relevant material and methods of the research
which combine a computer-based model of keyword analysis with traditional qualitative (in particular,
thematic analysis) and quantitative analysis. Thus, in our framework we use three procedural steps:
corpus compilation, keyword analysis, thematic and quantitative analyses.
The first step of the investigation was to compile the corpus of the research. The data used in the
study is the corpus of weather news selected from British online daily newspapers between 2014 and
2017 (The Times, The Guardian, The Daily Mail, The Sun) which consists of 746 324 words taken
from 180 newspaper articles. While compiling a corpus the following criteria were considered: firstly,
the timeframe of four years (2014‒2017) to represent recent use of the related themes, secondly, open
access articles to be easily downloaded. Finally, the texts from theguardian.com/uk [38],
thedailymail.co.uk [39], thesun.co.uk [40], thetimes.co.uk [41] were selected by using the search term
“weather”. The timeframe and the amount of words testify the representativeness and validity of the
results. The dataset was made into a text file (.txt) and later imported to the WordSmith software.
Our next step was to extract keywords using this software. For this reason, the British National
Corpus (hereinafter ‒ BNC) was chosen as a suitable reference corpus since all data are specific to
British English. In addition, BNC is one of the largest corpora which contains 100 million words of text
from a wide range of genres (e.g. spoken, fiction, magazines, newspapers, and academic) [42]. The aim
of a keyword analysis was to retrieve the words which are statistically relevant for the investigation.
Consequently, we constructed two word frequency lists with the help of WordList tool: of a target
corpus of weather texts and of a reference corpus (BNC) and generated a keyword list.
According to P. Baker, keyword extraction requires “a way that combines the strength of key
keywords with those of keywords but is neither too general or exaggerates the importance of a word
based on the eccentricities of individual files [14]. Therefore, we have taken into account both
statistically significant (positive, high-frequency) and negative (unusually low frequency) keyword
items using log likelihood ratio (Dunning 1993) [43]. The cells in the generated keyword list with
negative keywords were shaded in red and had a negative log likelihood value. The reason for taking
into consideration words with low frequencies is that our reference corpus consists of a collection of
rather small texts. Consequently, the distribution of some words in the text may be uneven and some of
the thematic lines might be lost. As stated by Gries, “corpora are inherently variable internally”[23] and
low frequency keywords may help us find additional „local‟ themes of weather news. In this case, the
issue may be solved by generating a wordlist for each single text in the research corpus, but it would be
very time-consuming.
It is important to note here that the analysis is also restricted to content words only, which, being the
units of meaning, we define to be directly related to the identification of the theme. Function words
cannot demonstrate the link of lexical units and themes [45]. The extraction of keywords provides
insights into their further grouping by themes since the potential of the key lexical units is realized
within the whole text: the semantic influence of a single word (sign) is determined only by the whole
text [44]. This idea is also supported by Morris and Hirst, who explain that “when a unit of text is about
the same thing there is a strong tendency for semantically related words to be used within that unit”
[46].
Thus, our last step was to group the keywords into lexical-thematic groups to define dominant
themes related to the representation of weather in the news of online press. To this end, we worked out
the following procedure: the context of each word from the keyword list is checked using the
concordancer and the word is put into the appropriate group manually.
Two problematic issues that arose during this step were that of how to group 1) the words that could
fit in multiple thematic groups and 2) the words that do not fit into any one of them. We applied a
systematic hierarchical decision-making procedure and critical analysis to solve this issue: if a word
could fit into several thematic groups, it was categorized into each of them; and if a word did not have
an appropriate thematic group to be categorized, it was left out. In this step quantitative analysis was
also used to further explore and identify dominant/prevalent themes of weather news by finding
quantitative content of each thematic group. According to G. P. Cantos, in quantitative research,
linguistic features are classified and counted [47]. In recent years commensurate attention has been paid
to mixed-method studies of a text which use both quantitative and qualitative data [44]. As a result, the
combination of keyword, lexical-thematic and quantitative approach in our research opened new
opportunities for in-depth analysis of British weather news.
4. Experiment
Having compared two word frequency lists, we have obtained a computed list of keywords (Table
1). The list of keywords was limited to 300 words. The presented list in Table 1 provides only the first
45 keywords and their frequencies, as they will be further investigated during their thematic grouping.
Table 1
The list of keywords and their frequencies
Keyword/Frequency Keyword/Frequency Keyword/Frequency
SNOW 119 COOLER 15 CLIMATE 32
COUNTRY 57 SPELLS 13 TRAFFIC 11
SHOWERS 46 MORNING 39 WARNED 27
HEAVY 56 DOWNPOURS 15 GALES 15
WINDS 46 POLICE 25 DELAYS 17
FORECASTERS 38 CAR 24 COAST 17
CONDITIONS 57 CHILD 8 ICY 11
WARNING 37 PM 8 FLOODING 23
UPDATED 17 DORIS 8 SUNSHINE 28
ARCTIC 14 BRITS 18 STORM 34
WEEKEND 50 COMMUTERS 10 RAIN 68
HIGHS 13 CHRISTMAS 9 HEATWAVE 33
TODAY 40 FLOODS 34 EXTREME 39
HEAT 66 TRANSPORT 14 DAMAGE 28
LOCALS 21 DISASTER 19 BRITAIN 19
Scotts’ classification of keywords into proper nouns, aboutness keywords and high-frequency words
[12] is relevant for our study. The first type of keywords is usually represented in the corpus of our
research by place names (Britain), names of nationalities (Brits) and names of storms (Doris).
Aboutness keywords, the words that have semantic correlations with the main ideas and central themes
of the text, are the most numerous. The third type constitutes words with high frequencies that are
considered to be more indicators of style than theme. However, the objective of our study consists in
grouping keywords into thematic groups rather than classifying them by types.
As defined earlier, our next step was to group the keywords by thematic categories representing
weather, which consists in classifying the lexical units according to the thematic groups and quantifying
them. Thus, the computed list of keywords was divided into five groups, each of which was classified
thematically. As a result, the following groups were organized: “Weather extremes”, “Climate”,
“Weather and people”, “Weather and nature”, “Weather phenomena”.
5. Results
The first thematic group “Weather extremes” consists of key lexical units that denote weather
catastrophes that cause destruction of material objects, casualties and even death of people. In the corpus
of the studied texts we find weather cataclysms of hydro- (floods, tsunami) and atmospheric
(blizzards/snowstorm, drought, hailstorm, heatwave, snow avalanche, showers, downpours,
thundersnow, storm, duststorm) origin, which accordingly constitute two subgroups of the group. This
group also collects words denoting: protective equipment (shelter, sandbag); locations (nomenclatures:
homes, businesses, region, village, area, country, town, adjectives: local, tropical, central, coastal);
means of transport and infrastructure (road, bridge, building, traffic, speed, travel, highway, train, boat,
van); general vocabulary (extreme, cancellation, delay, condition, incident, recovery, collapse, highs);
meteorological terms (icy, Doris, cyclone, warning); size/description (adjectives: thick, heavy, massive,
large, arctic), victims of the disaster (locals, mountaineer, immigrant, son, eyewitness, people, refugees,
children, driver, traveller, residents, civilians, kayakers, victims); organisations or political actors,
officials (minister, police); other actors involved in the disaster (commuters, coastguard, ambulance,
volunteers, paramedics, army, evacuees) and observation/analysis (expert, forecasters, meteorologist);
the consequences of the disaster (disaster, chaos, damage, mud, debris); emotional perception of the
disaster (alarm, alert, threat, fear, risk, danger, alarmed, terrified, fearful), actions (drop, force, leave,
move, block, trigger, halt, batter, collapse, damage, destroy, devastate, disrupt, kill, ruin, strike, warn).
The second thematic group “Climate” is composed of the words denoting results and consequences
of climate problems, their interrelation and impact on weather conditions. The next Table 2 presents
the words that form this thematic group.
Table 2
Thematic group “Climate”
Subgroups Keywords
1) the effects of climate warming, flooding, greenhouse, ozone, hole, drought, deficit,
change fire, glacier, thawing
2) the causes of climate urbanization
change
3) scientific terminology ElNino, research, study, science, scientist, emissions, fossil,
climate
4) general vocabulary impact, adaptation, conclusion, character, effects,
assessment, committee
5) descriptive characteristics disastrous, climatic, man-made, annual, fossil, human-
6) cause induced, global, vulnerable
exacerbate, pollute, emit, alter, modify, affect, devastate
Total quantity: 41
The keywords, the meaning of which thematically reflects the impact of weather conditions on
people’s comfortable living environment, their safety and health, we refer to the third thematic group
“Weather and people”. The keywords which are included into this group are shown in Table 3.
Table 3
Thematic group “Weather and people”
Subgroups Keywords
1) emotions anger, grim, laugh, like, distressed, deranged, glee, happy,
worried, devastated, shocked, hopeless, mad
2) transport and traffic train, car, boat, vehicles, lights, tailback, sign, driving, speed,
situation delays, diversions, cancellation
3) places roads, motorway, parks, station
4) people passenger, the elderly, commuter, children, driver, sun-
lovers, sunbathers, sunseekers, children, adults
5) tourism/holidays bookies, holidays, Christmas, Easter, football, vacations,
weekend, match, holidaymakers, camper, traveler, festival-
goer, beach, barbeque
6) health/safety inhaler, death, dehydration, cardiovascular, illness,
respiratory, diabetes, discomfort, faint, coughing,
hypothermia, cramp, dizziness, chronic, irritation, sensitive
7) location country, homes, businesses, region, village, area, town
Total quantity: 77
Thematic group “Weather and nature” is characterized by the use of nominations of different species
of plants and animals, as well as natural processes which are directly influenced by the weather. Table
4 shows the keywords that refer to this group.
Table 4
Thematic group “Weather and nature”
Subgroups Keywords
1) plants/flora eucalupts, pines, sycamore, oak, ash, beech
2) animals/fauna chiffchaffs, dog, frigate, seabirds, fish, sparrow, starling,
chaffinches, greenfinches, blackbird, woodpigeon, dove, tit,
puffin
3) vegetable products apples, strawberry, blackberry, fruit
4) agriculture crops, harvest, agriculture
5) natural processes breeding, flowering, leafing, ripening, budburst, growing,
fruiting
6) other words related to the wildlife, habitat, species, population, birds
field of nature
Total quantity: 40
The fifth thematic group “Weather phenomena” includes key lexical units denoting different weather
phenomena and their manifestations. The lexical composition of this group is given in Table 5.
Table 5
Thematic group “Weather phenomena”
Subgroups Keywords
1) atmosphere phenomena rain, flurry, thunder, snow, winds, heat, temperature, humid,
fog, tempest, gale, sunshine
2) meteorological terms ElNino, Met, mercury, updated
3) description of weather heavy, strong, clear, scorching, cold, conditions, humidity
phenomena
4) evaluation of weather unseasonable, awful, terrible, glorious, topsy-turvy, freak,
phenomena crazy, driving, ropy, cooler
5) action and movement rise, blow, batter, hit, smite, move, soar, bask, plummet, grip,
batter, swoop, slam, loom, pummel, stop, finish, end, begin,
start
Total quantity: 54
Some of the keywords (morning, today, spells, pm) do not fit any of the groups and their number is
not enough to group them separately, that is why there were not classified. Quantitative composition
of each group is presented in the pie-chart diagram (Figure 1).
Quantitative composition of thematic groups
41
111
77
40 54
“Weather extremes” “Weather phenomena" “Weather and nature”
"Weather and people" "Climate"
Figure 1: Quantitative composition of thematic groups
6. Discussion
Keywords appear as effective tools for the analysis of thematic focus/foci of the text or corpus.
While the analysis of individual keywords does not provide a consistent and exhaustive text analysis,
their grouping into categories according to the themes they represent establishes a link between the
lexical level of the text and its themes.
A quantitative analysis of the keywords representing the weather allowed us to identify dominant
thematic groups and, therefore, to recognize the themes that prevail in weather news of British online
press. The identification of lexical-semantic groups [5] and related themes enables a more in-depth
understanding of the texts of weather news. This survey paves the way for the development of more
rigorous methodology for the analysis of relationship between keywords and other words in contexts.
It would also be interesting to closer examine weather vocabulary through keywords extracted using
other statistical tests (e.g. t-test, the Wilcoxon-Mann-Whitney test) or software packages and compare
their results, as well as to find out whether dominant themes related to the representation of weather
differ from newspaper to newspaper or particular season.
7. Conclusions
Having analysed the obtained data, we may conclude that weather news has anthropocentric
character, since dominant thematic groups are “Weather extremes” and “Weather and people”. Thus,
within the corpus of the research two thematic lines can be outlined: (1) the depiction of adverse weather
conditions affecting people’s daily life; (2) depicting the effects of weather disasters on people and their
environment. The overall study reveals the following conclusion: everything that happens in the sphere
of weather, in any case influences people, their physical, moral, psychological and emotional state.
The method presented in this research provides more options for further collocational analysis of
weather-related vocabulary in weather news of British online press on the basis of concordances and
might be used as a cornerstone for the study of other vocabulary through keywords in different
discourses. Further scrutiny can be combined with a more interpretative approach to the analysis of
weather news in line with stylistic research.
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