Our Words in a State of Emergency: Psychological-Linguistic Analysis of Utterances on the COVID-19 Situation in the Czech Republic.

The study focuses on psychological-linguistic analysis of utterances provided by N = 2522 respondents aged 18-89 years in the period of March-May 2020, for the research of JUPSYCOR (Psychological Impacts of the Coronavirus Epidemic in the Czech Republic). The utterances relate to the interpretation of the state of emergency, the COVID-19 epidemic, and its subjectively perceived impacts. Simultaneously, the study examines the relationship between the analysed texts and the results of the SEHW (Scales of Emotional Habitual Subjective Well-being) questionnaire, which determines the valence of experienced emotions. The aim of the study is to analyse the lexical and morphological layers of the utterances, especially which specific words resonated in the individual questions, what is their emotional load, and which linguistic features of the texts may refer to the respondents' positive/negative emotional response. One of the outputs based on the results of the quantitative analyses determines that the most distinctive words are connected to negative emotions and most frequently relate to social environment, anxiety, and inhibition. Furthermore, the study proves a positive correlation between a fear scale and a higher occurrence of future tense and use of emotionally negatively loaded words, especially in women. Numerous differences among the individual age and gender cohorts were also proved. The significance of the study lies predominantly in the combination of the linguistic and psychological levels of the analysis, in the utilization of two mutually complementary utterances, and in the presentation of new insights on how people use words when they face an unexpected and emotionally disturbing situation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12646-021-00613-y. © National Academy of Psychology (NAOP) India 2021.

Introduction

The situation of the COVID-19 pandemic that occurred in the spring of 2020 significantly impacted the lives of people in many aspects of their everyday lives. The individual countries’ measures as they were striving to contain the epidemic differed both in timing and severity. As for the Czech Republic (EU), the reaction was relatively fast and strong. Between 10 and 16 March 2020, severe restrictions were adopted, especially restrictions on free movement, closure of schools, shops, restaurants, bans on sporting, cultural, and other activities. As of 19 March, all persons were obligated to wear a face mask in public. The borders were closed completely and Czech nationals staying abroad were predominantly repatriated. All these measures were highly progressive and radical within the context of the situation in Europe, although similar restrictions were subsequently adopted by numerous other countries.

The state of emergency had a major impact on the behaviour and emotional experience of people. Certain studies published so far show an extensive psychological impact, e.g. Twenge and Joiner (2020) demonstrated that the level of mental distress in the US population is three times higher than in 2019 or 2018 using the Kessler‐6 scale. Wang et al. (2020) reported that 53.8% of all respondents in their Chinese study scored as medium to serious psychological impact, out of whom 28.8% scored as medium to high seriousness and 8.8% scored as very serious anxiety level symptoms. The first studies from the Czech Republic also refer to an increased level of anxiety and fear (e.g. 40% of the respondents experienced the fear that they or their loved ones will fall ill with COVID-19 with serious symptoms; Rabušic, 2020). The emergency brought significant changes on the level of interpersonal contact as well. Personal meetings were substituted by phone calls and video conferencing, in the media as well as in day-to-day communication, such words as “face mask”, “lockdown”, and “quarantine”, very rare up to that point, resonated strongly. However, experiencing negative emotions did not necessarily influence only the explicit content of the communication (language content), but also language style (i.e. lexical and morphological layers of the communication; Chung & Pennebaker, 2007).

The present study aims to analyse selected parameters of communication in the form of thematic verbal utterances which might be significant in relation to the psychological and social aspects of the emergency state of COVID-19 epidemic in the Czech Republic. The basic research hypothesis postulates that an unexpected and emotionally disturbing situation will be reflected in the content and form of people’s utterances on the situation (for similar studies, see, e.g. Fiehler, 2002; Peters et al., 2009; Sun et al., 2019; Bernard et al., 2016). This hypothesis is important not only in terms of a possible description of emotional experiences of speakers at a specific time, but also in terms of verifying the potential of an analysis of psychological processes from a quantitative linguistic perspective, i.e. through natural language processing.

The hypothesis is followed by three research questions:

Which words resonate most in the thematic utterances, i.e. which lexical–semantic basis do people use to describe the existing situation and the emotional experience thereof?

What are the specifics of the utterances on the lexical–morphological level in terms of respondents’ gender and various age cohorts?

In which manner is the lexical–morphological level of the utterances influenced by the respondents’ current emotional experience?

The study consists of performing a series of psychological–linguistic analyses that relate to the utterances of the respondents (N = 2552) to two posited questions: “What does the current situation mean to you: Has it changed your life in any way? If so, how?” (this question focused primarily on the interpretation of the situation) and “How do you currently experience the situation: What do you consider the worst? On the other hand, what helps you?” (this question focused primarily on the impact of the situation and the related coping strategies). The data further include demographic descriptors of the respondents and the results of the SEHW (Scales Emotional Habitual Subjective Well-being) questionnaire which determines the valence of the experienced emotions.

Verbal Communication Analysis: Psychology of Word Use

A person’s verbal communication is the subject of study in several disciplines and especially a subject of long-term research in psychology (Gray, 1991). The relationships between specific communication patterns and a person’s interpersonal and intrapersonal functioning have been established in a large number of studies, e.g. screening and diagnostics of disorders through the analysis of speech products (Crystal, 1987), revealing the identity of anonymous communication (Matoušková, 2013), the prognosis of an author’s text or communicator’s behaviour (Canter & Youngs, 2009), or automatic extraction of opinions and attitudes from a text (Rodríguez-Puente et al., 2016). Studies inquire into the specific linguistic markers of gender (e.g. Sboev et al., 2016), emotionality (e.g. Brewer & Gardner, 1996), relationships (e.g. Newman et al., 2008), temperament (e.g. Mairesse et al., 2007; Schwartz et al., 2013), or pathological characteristics (e.g. Demjén, 2014).

Psychological analysis of language use usually differentiates between what a person says (language content) and how the person says it (language style; Chung & Pennebaker, 2007). The importance of language variability of a single person (language style) was repeatedly described on the level of general language usage (e.g. Chen & Bond, 2010), but also in specific word manipulations (e.g. Ireland & Mehl, 2014; Newman et al., 2008).

In terms of the relationship between specific linguistic features (language style) and the characteristics of the communicator, it is most often cited that, for example, women more frequently use “involved” parts of speech (e.g. more pronouns, present tense verbs) in comparison with men who prefer “informative” language (e.g. more nouns, long words, numbers, articles, prepositions; Biber, 1991; Newman et al., 2008). Women also more often use words in first person singular (Mehl & Pennebaker, 2003), negations and verbs (Newman et al., 2008), and more frequently express emotions and self-disclosure tendencies in the text (Holtgraves, 2014). In terms of communicators’ age, the documented differences include, for example, a higher ratio of words with positive emotional load and future tense in older people (Pennebaker & Stone, 2003).

If we focus on the connections between language style and the specifics of emotional experience (emotional state), numerous approaches attempting to successfully detect emotions in the text have been introduced (see, e.g. Pang & Lee, 2008; Ribeiro et al., 2016; Sun et al., 2019). Most of the studies utilize the traditional quantitative dictionary for detection of words, most frequently using the LIWC language analysis software (Linguistic Inquiry and Word Count; Pennebaker et al., 2015; see below). This research documented, for example, a close connection between emotionally loaded words (negative emotional experience resulted in higher use of negatively loaded words; Bernard et al., 2016) and the occurrence of pronouns (the same relationship with personal pronouns in the first person) (see meta-analysis; Holtzman, 2017). These conclusions are also supported by studies focusing on the issue of manifestations of depression and anxiety (e.g. Anderson et al., 2008; Arntz et al., 2012).

The aforesaid methods of quantitative natural language processing, in which both lexical–semantic and morphological analyses are employed, are substantial for our research. As in the above-mentioned studies, we also apply computational and statistical methods to search for relationships between language style and descriptors of the emotional experience of respondents (based on their psychological test results). In this case, however, we use an updated set of techniques, designed with respect to the Czech language specifics and higher linguistic variability.

The majority of the published studies were conducted in the English language, which brings certain risks to the transferability of the results to other non-English speaking populations. This paper focuses on the analysis of the Czech language. The Czech language, a member of the West-Slavic language group, is spoken by relatively few native speakers (10.7 million native speakers; cf. 379 million first-language speakers in English), but it is the 20th most frequently used language on the internet (W3Techs, 2020). In terms of psychological research, several studies on the Czech language, associated under the CPACT project (Computational Psycholinguistic Analysis of Czech Text; Kučera, 2018b), have been conducted in the past few years, focusing on the relationship between the morphological and lexical aspects of the text and the Big Five personality traits (Havigerová et al., 2018), dominance (Kučera et al., n.d.), and depressivity (Havigerová et al., 2019). The results of these studies imply comparability of a large part of the discoveries with the results of the anglophone studies (see Havigerová et al., 2018). However, the type of the text and the comparability (similarity) of the communication situation (to be more precise, the comparability and variability of the text registers selected for the study) plays an important role (see e.g. Biber, 1993; Kučera, 2020).

Method

Data Collection

The data collection was carried out within the JUPSYCOR project, “Research on the Psychological Impacts of the Coronavirus Epidemic in the Czech Republic” (www.jupsycor.cz). The open online interface was promoted through social networks and e-mails sent by cooperating institutions. The interface enabled two types of data collection—one for individual respondents and the other one for assistant interviewers who received online training and collected utterances from other respondents (i.e. the respondents who agreed to participate but would not be able to participate online were interviewed in the very same format through a phone call and their utterances were typed into the web form; see below). The respondents were fully informed about the nature of the survey, asked for informed consent, and participated voluntarily. They provided demographic data, answered two open questions related to their perception and experience of the COVID-19 situation (i.e. utterances), and completed self-reporting questionnaires capturing their emotional states in recent days. The data were collected for 68 days, from 18 March to 25 May 2020. This period was chosen with reference to the development of the pandemic situation in the Czech Republic (from the adoption of major restrictions in March to their decrease in May; Vlada, 2020) (Fig. 1).

Fig. 1

Research period and situation milestones in the Czech Republic (www.jupsycor.cz)

Sample

A total of 2552 respondents participated in the research, men and women aged 18–89 years. This sample was obtained via opportunity sampling (see Data collection). The respondents were divided into six categories based on age (age groups; Table 1). We also included the respondents’ representation in terms of their highest achieved level of education (primary school, secondary school, university) and social classification (student, pensioner/retired, other) (Table 2). For the needs of further analysis, the respondents were divided into six groups according to age and gender (cohort groups, Table 3).

Table 1

Sample: Age groups

Gender	Age (years)
	18–21	22–25	26–34	35–44	45–64	65+	Total
Female	388	492	296	241	327	271	2015
Male	76	121	108	53	91	88	537
Total	464	613	404	294	418	359	2552

Table 2

Sample: Education level and social classification

Gender	Education
	Basic	Secondary	Tertiary	Total
Female	58	1182	775	2015
Male	37	286	214	537
Total	95	1468	989	2552

Table 3

Sample: Cohort groups

Gender	Cohort group
	1	2	3	4	5	6	Total
Female	880	537	598	0	0	0	2015
Male	0	0	0	197	161	179	537
Total	880	537	598	197	161	179	2552

Cohort groups: 1 = females at the age of 18–25 years; 2 = females 26–44  years; 3 = females 45+  years; 4 = males 18–25 years; 5 = males 26–44 years; 6 = males 45+ years

Due to the data collection method, the sample could not be balanced with respect to various demographic variables or time of participation (see CSU, 2020). The sample has a significantly increased proportion of females (79%) and young persons under 26 years (42%). The sample also shows an increased proportion of people with a university degree (38% compared to 22% of the population aged 25–64 years as reported by the National Education Fund in 2015; NVF, 2015).

Text Materials (utterances)

The respondents were asked two questions focusing predominantly on (Q1) the interpretation of the COVID-19 situation by the respondent and (Q2) the negative and positive impacts on the respondents and their coping strategies. The respondents could write any utterance in reply to this question (no min./max. length of the text was specified). These utterances were typed into a web form.

Q1: What does the current situation mean to you: Has it changed your life in any way? If so, how?

Q2: How do you currently experience the situation: What do you consider the worst? On the other hand, what helps you?

As stated above, some utterances are based on a literal transcription of the respondent’s utterance by the assistant interviewers (N = 561); however, most were entered directly by the respondent (N = 1991). Only editing of the materials was performed solely in relation to typos in the text.

Linguistic Analysis

The basis of the linguistic analysis is the use of three referential dictionaries, SYN2015, SENS, and LIWC2007, and a set of linguistic applications for the analysis of text in the Czech language (PMA).

SYN2015: Representative Corpus of Contemporary Written Czech (Křen et al., 2016) is a 100-million-word corpus. It was created as a representation of printed language from 2010 to 2014, containing a wide range of text types (fiction, professional literature, newspapers, etc.). The corpus is lemmatized, morphologically and syntactically annotated by a combination of stochastic and rule-based methods. In terms of this study, SYN2015 was used for frequency analysis.

SENS: Dictionary of Emotionally Loaded Words was created by the adjustment of Czech SubLex 1.0 dictionary (Veselovská & Bojar, 2013), performed by the Institute of Theoretical and Computational Linguistics (UTKL; Faculty of Arts, Charles University). The adjustment consisted of deleting 94 words without a sufficient and/or completely obvious emotional load. SENS features 928 words (lemmas) altogether, annotated by a positive, negative, or undetermined emotional load.

LIWC2007: Linguistic Inquiry and Word Count (Pennebaker et al. 2007) is a text analysis program which functions on the basis of the closed-vocabulary approach. Its dictionary is composed of circa 4,500 words and word stems. Each word or word stem is defined by one or more word categories or subdictionaries (see ibid.).For example, the word “cried” is a part of five word categories: sadness (130 Sad), negative emotion (127 Negemo), overall affect (125 Affect), verb (11 Verbs), and past tense verb (13 Past). For this study, synonymous expressions were identified in the dictionary (after translating Czech words into English), specifically in the relevant categories of Psychological Processes (Sects. 121–253) and Personal Concerns (Sects. 354–360). In comparison with the used LIWC2007 version, the newer LIWC2015 version features some modified parts of the dictionary (moreover, certain categories were deleted, e.g. the morphological category of Tense); nevertheless, both versions demonstrate comparable parameters and a high degree of congruity (Pennebaker et al., 2015).

PMA: Prague Morphological Analysis (Hajič, 2001). All obtained texts were further processed via UTKL applications (Jelínek, 2018), collectively termed as PMA. These applications represent a Czech variant of the LIWC (see the comparison in Kučera & Haviger, 2019). However, except for one specific category (Emotions), they primarily focus on morphological analysis. The outcome of this process is the allocation of morphological tags to every lexical unit of the text with an average of 95% accuracy and, in the case of detection of various linguistic variables (e.g. part of speech), as high as 99.5% accuracy (Skoumalová, 2011). This study utilized such linguistic categories that show high compatibility with the anglophone LIWC, i.e. the grammatical categories of Part of speech, Person, Tense, Degree, and Negation, and the semantic category of Emotions that is based on the implementation of the SENS dictionary (see above). All these categories are processed in terms of values of the relative frequency occurrence (i.e. the ratio of the given category to the number of words in the utterance). The overview of the categories and subcategories is included in Table 4.

Table 4

Linguistic categories in the analysis

Category	Variable	Coding
Part of speech	Noun	POS–N
	Adjective	POS–A
	Pronoun	POS–P
	Numerals	POS–C
	Verb	POS–V
	Adverbs	POS–D
	Preposition	POS–R
	Conjunction	POS–J
	Particles	POS–T
	Interjection	POS–I
	Punctuation	POS–Z
	Unknown	POS–X
Person	First	Per–1
	Second	Per–2
	Third	Per–3
Tense	Future tense	Ten–F
	Present tense	Ten–P
	Past and present tenses	Ten–R
Degree	First degree (base form)	Deg–1
	Second degree (comparative)	Deg–2
	Third degree (superlative)	Deg–3
Negation	Affirmative (without negative prefix “ne-”)	Neg–A
	Negation (form with a negative prefix “ne-”)	Neg–N
	Verb negation	Vneg
Emotionsa	Emotionally loaded	Em2.*
	Emotionally positively loaded	Em2. +
	Emotionally negatively loaded	Em2.-

aLexical–semantic category

Morphological/lexical–semantic* category

Psychological Measures

To ascertain the respondents’ emotional experience, SEHW: Scales of Emotional Habitual Subjective Well-being questionnaire (Džuka, 2015; Džuka & Dalbert, 2002) were used. The SEHW questionnaire is a ten-item questionnaire focused on the emotional component of subjective well-being (Diener, 1994), with the Positive Affect Scale consisting of four descriptors (pleasure, happiness, joy, and physical freshness/energy/briskness) and the Negative Affect Scale comprising six descriptors (anger, guilt feelings, shame, fear/anxiety, pain, and sadness/sorrow). Respondents were answering the question: “How often have you experienced these affects in the past few days?”. The explicitness of questionnaire statements and simplicity of answering for respondents were the main reasons for choosing SEHW. The questionnaire has been used in numerous studies on well-being in different populations (e.g. Džuka & Dalbert, 2006; Gurková et al., 2012). The respondents indicated how often they experienced each affect state in recent days on a six-point frequency scale ranging from 1 (almost never) to 6 (almost always). The internal consistency estimate for the Positive Affect Scale was Cronbach’s alpha = 0.83, and for the Negative Affect Scale, it was Cronbach’s alpha = 0.67 in the validation study (Džuka & Dalbert, 2002). In the present study, the respective Cronbach’s alphas were 0.85 and 0.74.

Results

Description of Utterances

Table 5 describes the numbers of words, sentences, and tokens (individual occurrences of a linguistic unit) that were recorded within the utterances (texts Q1 and Q2) while employing PMA (Prague Morphological Analysis). All texts which featured at least one word in both utterances (Q1 and Q2) were included in the analyses. The results demonstrate that both men and women wrote utterances of similar length, equivalent to short commentaries, slightly longer in the case of text Q2.

Table 5

Utterances: Q1 and Q2 texts description for the whole sample, females and males

N = 2552	Q1_Words	Q1_Sentences	Q1_Tokens	Q2_Words	Q2_Sentences	Q2_Tokens
N	2552	2552	2552	2552	2552	2552
M	21.772	1.936	25.851	26.930	2.417	32.016
Mdn	18.000	1.000	22.000	26.000	2.000	31.000
Mode	9.000	1.000	11.000	45.000	2.000	52.000
SD	15.021	1.227	17.696	14.396	1.321	16.985
Min	1.000	1.000	1.000	1.000	1.000	1.000
Max	56.000	9.000	70.000	54.000	11.000	68.000
Female
N	2015	2015	2015	2015	2015	2015
M	22.169	1.937	26.326	27.376	2.393	32.512
Mdn	18.000	1.000	22.000	27.000	2.000	32.000
Mode	9.000	1.000	10.000	44.000	2.000	52.000
SD	14.945	1.232	17.614	14.266	1.295	16.842
Min	1.000	1.000	1.000	1.000	1.000	1.000
Max	54.000	9.000	69.000	54.000	9.000	66.000
Male
N	37	537	537	537	537	537
M	20.281	1.933	24.069	25.257	2.507	30.156
Mdn	15.000	2.000	18.000	24.000	2.000	28.000
Mode	7.000	1.000	11.000	7.000	2.000	14.000
SD	15.227	1.209	17.904	14.766	1.410	17.402
Min	1.000	1.000	1.000	1.000	1.000	1.000
Max	56.000	9.000	70.000	54.000	11.000	68.000

Words = number of individual words; Sentences = number of sentences (usually ending in punctuation); Tokens = number of occurrences of specific realizations of words and symbols

Description of SEHW Questionnaire

Table 6 features an overview of the SEHW (Scales of Emotional Habitual Subjective Well-being; Džuka & Dalbert, 2002) questionnaire results according to the individual items and the average score for negative emotions (SEHW_N) and positive emotions (SEHW_P). Table 7 features a division of respondents into two groups—low scoring and high scoring in SEHW_N negative emotions (high = mean > 2.3) and low scoring and high scoring in SEHW_P positive emotions (high = mean > 3.5), determined according to the sample median.

Table 6

SEHW questionnaire descriptives (SEHW)

	SEHW1	SEHW2	SEHW3	SEHW4	SEHW5	SEHW6
	Anger	Guilt	Pleasure	Shame	Energy	Fear
N	2552	2552	2552	2552	2552	2552
M	2.587	1.898	3.472	1.587	2.250	3.049
Mdn	3	2	3	1	3	3
Mode	3	1	3	1	3	3
Std. Deviation	1.183	1.073	1.08	0.917	1.261	1.345

Table 7

SEHW questionnaire: High and low scores

	Gender
SEHW_N	Female	Male	Total
Low	1046	397	1443
High	969	140	1109
Total	2015	537	2552
Low	1210	299	1509
High	805	238	1043
Total	2015	537	2552

In previous studies, the positive affect mean score SEHW_P ranged from 3.00 (elderly people; Džuka & Dalbert, 2006) to 3.85 (high school students; ibid.) and negative affect mean score SEHW_N ranged from 2.28 (nurses; Gurková et al., 2014) to 2.96 (elderly people; Džuka & Dalbert, 2006). The values gathered in the present study fall within these ranges.

Most Distinctive Words: Results of Frequency Lexical Analysis

After performing the frequency analysis, words occurring most frequently in the utterances (Q1 and Q2) of the whole sample (N = 2552) were detected, namely in the Part of speech category: Nouns, Adjectives, Verbs, and Adverbs. These words were identified in the frequency dictionary of the SYN2015 corpus, determining the rank in which they occur in this corpus. Subsequently, S/J ratio was calculated (dividing rank 2 in SYN2015 by rank 1 in JUPSYCOR Q1/Q2 texts). For instance, the noun “restriction” is listed with rank 2 = 903 in the SYN2015 corpus, but it ranked rank 1 = 9 in our Q1 responses. It was therefore mentioned approximately 100 × more frequently in the utterances than in common Czech communication. The overview in Tables 8 and 9 includes words with S/J ratio ≥ 25. This value was determined ad hoc, regarding the legible arrangement of the list and visualization (approximately 25 words for all parts of speech related to one question). It was ascertained for each word whether it appears in the SENS dictionary (and if yes, the emotional load of the word was included) and whether the same or synonymous word appears in the LIWC dictionary (if yes, semantic–psychological connotations of the word were included).

Table 8

Question 1 (Q1): Most distinctive words in respondents’ utterances (N = 2552)

English	Czech	Rank 1	Rank 2	Ratio	Emo	LIWC	Example
Nouns
Face mask	rouška	16	4995	312			Things have changed, I have to wear a face mask and my glasses are fogging up
Lockdown	karanténa	33	9414	285			My life has changed. We’re under lockdown and I ‘m not going to school
Restriction	omezení	9	903	100	N	137 Inhib	There is a restriction on free movement, visiting doctors and going to shops and offices
Part-time job	brigáda	28	2716	97		354 Work	I’ve lost my part-time job and I don’t have any income now. On the other hand, I’m able to save more money now
Stress	stres	25	2105	84	N	125 Affect	This situation has disrupted my work and increased stress levels
Contact	kontakt	11	694	63		121 Social	I miss the social contact, especially with my family and friends
Uncertainty	nejistota	32	1881	59	N	128 Anx	It is a huge change for me and also quite full of certainty
Schooling	výuka	23	1183	51		354 Work	The way of schooling had to change
Isolation	izolace	42	1731	41	N	130 Sad	The isolation doesn’t have a positive impact on us
Home	domov	15	514	34		357 Home	I have to work from home
Job	zaměstnání	30	982	33		354 Work	I’m not travelling to my job in Prague, I’m working from home
Shopping	nákup	27	709	26		358 Money	I can’t go shopping by myself
Fear	strach	12	305	25	N	128 Anx	I can feel some level of fear in our population
Adjectives
Stressful	stresující	56	5975	107	N	128 Anx	I consider seeing people with face masks stressful
Limited	omezený	3	284	95	N	137 Inhib	We have limited ways how to work
Closed	zavřený	10	552	55	N	252 Space	The borders are closed
Online	online	36	1677	47			I don’t know what to expect from online oral exams
Used to	zvyklý	11	458	42		133 Cause	Nothing has really changed. I am used to home office
Relative	příbuzný	43	1271	30		121 Social	I stay at home and I’m worried about my relatives
Verbs
Socialize	stýkat	26	1491	57		121 Social	I don’t feel free because I can’t socialize with my closest ones
Meet	vídat	28	1295	46		121 Social	I can’t meet my sister and cousin
Spend	trávit	14	522	37			I can’t spend time with my family
Limit	omezit	15	424	28	N	137 Inhib	Restrictions limit my life
Visit	navštěvovat	27	710	26		121 Social	I haven’t been able to visit my family since March
Adverbs
As	jako	3	1169	390		18 Conj	My daily routine is the same as before
Only	jen	6	467	78		16 Adverbs	The only thing that has changed are the restrictions
At home	doma	2	74	37		357 Home	We have to stay at home all day

Rank 1 = word ranking in JUPSYCOR; Rank 2 = word ranking in SYN2015; Ratio = ratio SYN2015/JUPSYCOR; Emo = emotional valence based on SENS and LIWC (negative = N); LIWC = LIWC2007 classification; Example = representative sentence translation

Table 9

Question 2 (Q2): Most distinctive words in respondents’ utterances (N = 2552)

English	Czech	Rank 1	Rank 2	Ratio	Emo	LIWC	Example
Nouns
Face mask	rouška	9	4995	555			Wearing the fabric face mask is the worst
Uncertainty	nejistota	14	1881	134	N	128 Anx	The uncertainty is the worst of that all
Contact	kontakt	6	694	116		121 Social	Regular social contact helps me
Walk	procházka	19	1498	79		251 Motion	I’m fine, staying at the cottage and enjoying gardening and going for walks
Panic	panika	36	2509	70	N	128 Anx	The overall panic and fear, caused by the media, are the worst
Isolation	izolace	30	1731	58		130 Sad	Social isolation and the absence of my daily routine are the worst things for me
Restriction	omezení	16	903	56	N	137 Inhib	The number of restrictions that we have to follow is the worst
Fear	strach	7	305	44	N	128 Anx	I can tell people feel fear about the future
Chill	pohoda	39	1261	32	P	356 Leisure	At the beginning, I was quite chill with staying at home, but now it bothers me
Calmness	klid	15	464	31	P	125 Affect	I am quite calm. Most of the things in my life are the same
Nature	příroda	13	363	28			Spring nature helps me
Adjectives
Infected	nakažený	15	2667	178		148 Health	The number of infected people is the worst
Online	online	31	1677	54			Studying at least online helps me
Closed	zavřený	9	353	39	N	250 Relativ	I can’t travel because the borders are closed
Loved	blízký	2	73	37		121 Social	I can’t be in touch with my loved ones
Limited	omezený	8	284	36	N	137 Inhib	I have limited hobbies that I can do at home
Bad	špatný	1	29	29	N	125 Affect	I feel bad every time I leave my home
Verbs
Infect	nakazit	31	1736	56		148 Health	The worst thing is that you can’t be sure who can infect you
Socialize/Be with	stýkat	39	1491	38		121 Social	I would love to be able to socialize more./I would love to be with my loved ones
Follow	dodržovat	22	768	35		250 Relativ	I am responsible and I follow the announced restrictions
Manage	zvládat	26	871	34	P	354 Work	I still haven’t figured out how to manage this situation
Sew	šít	66	1964	30			I’ve finally learned how to sew
Meet	vídat	47	1295	28		121 Social	The worst is that I don’t meet my friends
Adverbs
As	jako	3	1169	390		18 Conj	Life is basically the same as before the coronavirus
Only	jen	7	467	67		16 Adverbs	My life has changed, I can only do things at home
At home	doma	2	74	37		357 Home	I have two studying children at home

Rank 1 = word ranking in JUPSYCOR; Rank 2 = word ranking in SYN2015; Ratio = ratio SYN2015/JUPSYCOR; Emo = emotional valence based on SENS and LIWC (positive = P, negative = N); LIWC = LIWC2007 classification; Example = representative sentence translation

For the illustration of significant words (in terms of all parts of speech), a visualization of Q1 (Fig. 2) and Q2 (Fig. 3) was performed in a word cloud form. The font size corresponds to the S/J ratio value.

Fig. 2

Q1: Most distinctive words (word cloud)

Fig. 3

Q2: Most distinctive words (word cloud)

The most significant words appearing are, for instance, “as”, “face mask”, “contact”, and “uncertain”. The significant words with negative emotional load according to the SENS dictionary are in the absolute majority, except for two words with positive load in Q2 (“calmness” and “manage”). In terms of psychological connotations of words according to the LIWC dictionary, words in the categories of Social (9 words), Anxiety (6 words), Inhibition (5 words), and Work (4 words) are in majority.

Comparison of Respondent Groups in Terms of Linguistic Categories Usage

Further analyses were aimed to compare the use of linguistic categories (in what way are the utterances phrased) between men and women (Table 2) and between 6 cohorts (Table 3). A Mann–Whitney U test and a Kruskal–Wallis H test were run to determine whether there were significant differences in the relative frequencies of linguistic categories between these groups. The effect sizes (Cohen’s d) of the presented results are within a range that Cohen (1988) reports as a small effect (0.1–0.3), as given in Tables 10 and 11.

Table 10

Gender and linguistic categories for both Q1 and Q2 (Mann–Whitney U test)

Gender	n	Q1_POS–V	Q2_POS–V	Q1_POS–R	Q2_POS–R	Q1_Em2.-	Q2_Em2.-
Mdn (female)	2015	0.200	0.205	0.107	0.100	0.000	0.020
Mdn (male)	537	0.184	0.200	0.091	0.091	0.000	0.000
M (female)	2015	0.206	0.205	0.107	0.101	0.031	0.032
M (male)	537	0.188	0.195	0.092	0.094	0.021	0.029
U		493,827	507,898.5	474,822	497,008	505,897	497,182.5
z		− 3.116	− 2.184	− 4.380	− 2.904	− 2.755	− 3.104
p		0.002	0.029	0.000	0.004	0.006	0.002
d		− 0.135	− 0.107	− 0.196	− 0.119	− 0.122	− 0.063
CI (95%)		− 0.231 – − 0.04	− 0.203 – − 0.012	− 0.291 – − 0.1	− 0.214 – − 0.024	− 0.217 – − 0.026	− 0.159 – 0.032

For explanatory note on linguistic categories, see Table 4

Table 11

Cohort groups and linguistic categories for both Q1 and Q2 (Kruskal–Wallis H test for independent samples)

Groups	1	2	3	4	5	6	1–6
Ling. cat.	Q1_POS–N
n	880	537	598	197	161	179	2552
Mdn	0.228	0.25	0.225	0.211	0.235	0.214	0.227
M	0.241	0.274	0.258	0.242	0.269	0.235	0.253
χ2 (5)							20.397
p							0.001
d							0.156
Ling. cat.	Q1_POS–P
n	880	537	598	197	161	179	2552
Mdn	0.118	0.095	0.115	0.128	0.095	0.095	0.108
M	0.122	0.097	0.117	0.125	0.103	0.113	0.113
χ2 (5)							32.403
p							0.000
d							0.209
Ling. cat.	Q1_POS–V
n	880	537	598	197	161	179	2552
Mdn	0.214	0.182	0.204	0.2	0.167	0.182	0.192
M	0.219	0.184	0.206	0.205	0.174	0.182	0.195
χ2 (5)							51.863
p							0.000
d							0.274
Ling. cat.	Q1_POS–Z
n	880	537	598	197	161	179	2552
Mdn	0.167	0.182	0.2	0.167	0.189	0.19	0.183
M	0.177	0.195	0.203	0.173	0.188	0.2	0.189
χ2 (5)							27.239
p							0.000
d							0.188
Ling. cat.	Q1_Per–1
n	880	537	598	197	161	179	2552
Mdn	0.136	0.111	0.131	0.133	0.086	0.094	0.115
M	0.135	0.11	0.128	0.136	0.098	0.107	0.119
χ2 (5)							50.718
p							0.000
d							0.27
Ling. cat.	Q1_Ten–F
n	880	537	598	197	161	179	2552
Mdn	0	0	0	0	0	0	0,000
M	0.003	0.002	0.002	0.002	0.001	0.0003128	0.002
χ2 (5)	880	537	598	197	161	179	23.225
p							0.000
d							0.17
Ling. cat.	Q1_Ten–P
n	880	537	598	197	161	179	2552
Mdn	0.125	0.111	0.125	0.125	0.095	0.1	0.114
M	0.125	0.109	0.117	0.119	0.096	0.099	0.111
χ2 (5)							36.100
p							0.000
d							0.222
Ling. cat.	Q1_Vneg
n	880	537	598	197	161	179	2552
Mdn	0.024	0	0	0.023	0	0	0.008
M	0.048	0.035	0.042	0.047	0.042	0.05	0.044
χ2 (5)							35.716
p							0.000
d							0.221
Ling. cat.	Q1_Em2.*
n	880	537	598	197	161	179	2552
Mdn	0	0	0	0	0	0	0,000
M	0.026	0.026	0.025	0.025	0.029	0.027	0.026
χ2 (5)							14.995
p							0.010
d							0.126
Ling. cat.	Q2_POS–N
n	880	537	598	197	161	179	2552
Mdn	0.228	0.25	0.225	0.211	0.235	0.214	0.227
M	0.2353	0.2635	0.2675	0.2371	0.287	0.2675	0.26
χ2 (5)							34.122
p							0.000
d							0.215
Ling. cat.	Q2_POS–P
n	880	537	598	197	161	179	2552
Mdn	0.149	0.125	0.13	0.143	0.125	0.139	0.135
M	0.148	0.127	0.1327	0.1459	0.1208	0.1374	0.135
χ2 (5)							37.915
p							0.000
d							0.229
Ling. cat.	Q2_POS–V
n	880	537	598	197	161	179	2552
Mdn	0.215	0.196	0.2	0.2	0.196	0.194	0.2
M	0.2159	0.1922	0.1997	0.2047	0.1901	0.1894	0.199
χ2 (5)							34.644
p							0.000
d							0.217
Ling. cat.	Q2_POS–Z
n	880	537	598	197	161	179	2552
Mdn	0.176	0.179	0.194	0.182	0.194	0.19	0.186
M	0.1847	0.1897	0.2011	0.1921	0.2075	0.2139	0.198
χ2 (5)							16.402
p							0.006
d							0.134
Ling. cat.	Q2_Per–1
n	880	537	598	197	161	179	2552
Mdn	0.122	0.111	0.128	0.125	0.1	0.118	0.117
M	0.1261	0.1065	0.1279	0.1238	0.1021	0.1186	0.118
χ2 (5)							34.178
p							0.000
d							0.215
Ling. cat.	Q2_Ten–F
n	880	537	598	197	161	179	2552
Mdn	0	0	0	0	0	0	0,000
M	0.004975	0.003669	0.003174	0.003162	0.002447	0.003587	0.004
χ2 (5)	880	537	598	197	161	179	12.059
p							0.034
d							0.105
Ling. cat.	Q2_Ten–P
n	880	537	598	197	161	179	2552
Mdn	0.15	0.143	0.148	0.143	0.143	0.143	0.145
M	0.1523	0.1349	0.148	0.1442	0.145	0.1466	0.145
χ2 (5)							16.573
p							0.005
d							0.135
Ling. cat.	Q2_Vneg
n	880	537	598	197	161	179	2552
Mdn	0.022	0	0	0.022	0	0	0.007
M	0.03125	0.02694	0.02884	0.03056	0.0282	0.02678	0.029
χ2 (5)							16.189
p							0.006
d							0.133
Ling. cat.	Q2_Em2.*
n	880	537	598	197	161	179	2552
Mdn	0.038	0.026	0.025	0.031	0.033	0.026	0.03
M	0.04357	0.03569	0.0362	0.04425	0.04573	0.03559	0.04
χ2 (5)							13.066
p							0.023
d							0.113

Groups: 1 = females at the age of 18–25 years; 2 = females 26–44 years; 3 = females 45+  years; 4 = males 18–25 years; 5 = males 26–44 years; 6 = males 45+ years. For explanatory note on linguistic categories, see Table 4

The influence of gender on phrasing the utterances was proven in three linguistic categories (POS–V, POS–R, and Em2.-), in both Q1 and Q2. In their utterances, men used a significantly lower number of verbs, fewer prepositions, and fewer emotionally negatively loaded words (Table 10).

The diversity of the cohort groups was proven in nine linguistic categories for Q1 and Q2 simultaneously (Table 11). The groups’ general tendencies in linguistic categories usage (group means/medians) are highly comparable for both texts (Q1 and Q2). The most distinctive in this regard are categories: prepositions (POS–P), used more frequently by younger people (men and women) in contrast to middle-aged people; verbs (POS–V), which are used to a higher degree by young people (especially women), and, on the other hand, less by especially older men; first person (Per–1), once again used primarily by young people, but also older women.

The Polarity of Respondents’ Emotional Experience in Relation to Usage of Linguistic Categories

Another set of analyses focused on the differences in linguistic categories usage between respondents who scored either high or low on the SEHW_N (negative emotions) and SEHW_P (positive emotions) scales (Table 7). A Mann–Whitney U test was run to determine whether there were significant differences in the relative frequencies of linguistic categories between these groups for both Q1 and Q2. However, the effect sizes (Cohen’s d) of the presented results are within the range that Cohen (1988) reports as a very small effect (0.1 on average), as given in Tables 12 and 13.

Table 12

SEHW_N score (negative emotions) and linguistic categories for both Q1 and Q2 (Mann–Whitney U test)

SEHW_N	n	Q1_POS–A	Q2_POS–A	Q1_POS–D	Q2_POS–D	Q1_Ten–F	Q2_Ten–F
Mdn (SEHW_N low score)	1443	0.061	0.083	0.095	0.089	0.000	0.000
Mdn (SEHW_N high score)	1109	0.068	0.091	0.087	0.080	0.000	0.000
M (SEHW_N low score)	1443	0.080	0.092	0.117	0.097	0.002	0.003
M (SEHW_N high score)	1109	0.082	0.099	0.103	0.085	0.003	0.005
U		838,716	856,402	763,062	748,819	816,748.5	828,066
z		2.121	3.054	− 2.027	− 2.796	2.328	3.011
p		0.034	0.002	0.043	0.005	0.020	0.003
d		0.021	0.089	− 0.115	− 0.140	0.123	0.129
CI (95%)		− 0.058 – 0.099	0.011 – 0.168	− 0.194 –− 0.037	− 0.218 –− 0.062	0.044 – 0.201	0.05 – 0.207

For explanatory note on linguistic categories, see Table 4

Table 13

SEHW_P score (positive emotions) and linguistic categories for both Q1 and Q2 (Mann–Whitney U test)

SEHW_P	n	Q1_Deg–2	Q2_Deg–2
Mdn (SEHW_P low score)	1509	0.000	0.000
Mdn (SEHW_P high score)	1043	0.000	0.000
M (SEHW_P low score)	1509	0.017	0.007
M (SEHW_P high score)	1043	0.024	0.009
U		834,059	820,086.5
z		3.352	2.693
p		0.001	0.007
d		0.124	0.085
CI (95%)		0.045–0.203	0.006–0.164

For explanatory note on linguistic categories, see Table 4

Although the differences between the groups are minor, people reaching a higher score in SEHW_N (negative emotions) exhibit a significantly higher usage of adjectives (POS–A) and future tense (Ten–F) in both texts (Q1 and Q2), and, contrastingly, lower usage of proverbs (POS–D) (Table 12). Regarding SEHW_P (positive emotions), the differences are again very minor; however, there is an obvious difference between the groups in the category of Deg–2 (second degree, comparative), more often used by people with a higher ratio of positive emotions (in both texts; Table 13).

Relationships Between Emotional Experience and Linguistic Categories Usage in Various Respondent Groups

A Spearman’s rank-order correlation was run to assess the relationship between 27 linguistic categories (relative frequencies, Table 4) and 12 SEHW (SEHW results, Table 6) for Q1 and Q2. The test was performed on nine different sample groups altogether: on the whole sample (N = 2552), on the six cohorts (Table 3), and on men and women (Table 2).

A high number of small but significant correlations (p < 0.05) were found within all groups, usually with the value of rs (< 0.1 and > − 0.1). Three hundred and fifty-one significant correlations were found in Q1, and 336 significant correlations in Q2. Ninety-four correlations thereof were significant in both texts, and 93 of these demonstrated the same correlation direction (see Supplement 1).

After making Šidák’s adjustment (Šidák, 1967) to the level padj < 0.0001583, 48 significant relationships were found in Q1 across all groups and 10 significant relationships in Q2. Six relationships thereof fulfilled the p-adjustment conditions in both Q1 and Q2 (Table 10). The relationships proven by Šidák’s statistical correction are linked primarily to the linguistic category Em2.- (emotionally negatively loaded words), which positively correlates with the scales SEHW_6 (fear) and SEHW_N (negative emotions mean). It is therefore apparent that the respondents experiencing negative emotions use a higher number of negative words. The only confirmed morphological category was Ten-F (future tense), which showed positive correlation with the scale SEHW_6 (fear) within the whole sample (N = 2552). People experiencing fear therefore use a higher number of words related to the future (Table 14).

Table 14

SEHW and linguistic categories for Q1 and Q2 (Spearman’s rank-order correlation; Šidák’s adjustment)

Group	SEHW	Ling. cat	Q1	Q2
			rs*	rs*
Cohort_1	SEHW6_Fear	Em2.-	0.171	0.144
N = 2552	SEHW6_Fear	Em2.-	0.220	0.137
	SEHW6_Fear	Ten–F	0.098	0.089
	SEHW_N	Em2.-	0.199	0.100
Female	SEHW6_Fear	Em2.-	0.211	0.117
	SEHW_N	Em2.-	0.196	0.085

*p < 0.00001

In terms of relationships concerning solely morphological categories, Table 15 features an overview of 11 significant relationships that are related to both Q1 and Q2 and scales SEHW_N (negative emotions mean) and SEHW_P (positive emotions mean). The complete correlation matrix is included in Supplement 2.

Table 15

SEHW_N and SEHW_P scales and morphological categories for Q1 and Q2 (Spearman’s rank-order correlation)

Ling. cat	Group	SEHW	Q1		Q2
			rs	p	rs	p
POS–N	Female	SEHW_P	− 0.046	0.041	− 0.057	0.011
POS–A	N = 2552	SEHW_P	− 0.052	0.008	− 0.040	0.042
POS–A	Female	SEHW_P	− 0.051	0.023	− 0.048	0.032
POS–A	N = 2552	SEHW_N	0.056	0.005	0.045	0.023
POS–D	Female	SEHW_P	0.068	0.002	0.073	0.001
POS–D	Female	SEHW_N	− 0.046	0.039	− 0.048	0.031
Ten–F	N = 2552	SEHW_N	0.063	0.001	0.064	0.001
Ten–F	Female	SEHW_N	0.051	0.023	0.054	0.016
Deg–2	Cohort_1	SEHW_P	0.096	0.004	0.083	0.014
Deg–2	N = 2552	SEHW_P	0.058	0.004	0.052	0.008
Deg–2	Female	SEHW_P	0.081	0.000	0.053	0.017

For an explanatory note on linguistic categories, see Table 4.

Discussion

The previous text introduced the results of a study focusing on word usage in a reflection of the state of emergency, COVID-19 epidemic in the Czech Republic, and the connections these words have to the emotional experience in 2552 respondents. The importance of the study in this regard lies in two aspects—first, it describes the specifics of thematically focused utterances and their linguistic parameters in different respondent groups, and, second, it documents those linguistic features that refer to the respondents’ emotional experience.

Before interpreting the results as such, it is necessary to point out to the specifics of the research sample, the specifics of the time framework of the data collection, and the specifics of the Czech language. As mentioned above, the sample features a majority of women and young people, predominantly students. It is, therefore, necessary to consider the extent of the influence of selection bias on the results. Nevertheless, we appreciate, in comparison with other COVID-19-themed researches (see, e.g. Özdin & Bayrak Öydin, 2020; Rodríguez-Rey et al., 2020), the relatively large representation of older people. This representation was achieved also because the older respondent group was often questioned via assistant interviewers (see above), without relying on contacting respondents solely via social media. The time frame selected for the research covered 68 days. It is therefore apparent that the respondents’ utterances might have been (and undoubtedly partially were) influenced by the situation at that time. From 18 March to 5 April, the situation was at its most serious in the Czech Republic (adoption of major emergency measures, e.g. closure of schools, restaurants, shops, imposed face masks, restrictions of free movement, etc.); afterwards, the restrictions were softening, and in late May, the situation in the society was relatively optimistic (albeit with milder restrictions still applicable). In this aspect, it was difficult to set a clearer limit than the one relating to the adoption of government measures (see Vlada, 2020). The use of Czech language analysis also resulted in certain compromises, connected predominantly to the necessity of key word translation (including presentations of relevant examples and tracing relationships with the English expressions) and the selection of suitable linguistic categories (which were selected especially regarding their compatibility with English). The central point in this regard was the transparency of the whole process while also striving for a maximum transferability of the results to other languages.

If we focus on the first research question (What words resonate the most in the thematic utterances, i.e. which lexical–semantic basis do people use to describe the current situation and their emotional experience thereof?), it is not surprising that across all utterances, the words that resonated most were words connected with the social situation and with negative connotations. Words related to anxiety and inhibition and references to social environment and work are prevalent. However, in the second utterance (focused, among else, on coping), words suggesting activities perceived positively appeared as well (e.g. “calmness, nature, walk, chill”). It is also not surprising that the highest ranking positions of lexically unique words are occupied by such words as “face mask, lockdown, infected”, which were omnipresent in the media in the Czech Republic at that time as well (see, e.g. Trait, 2020). The adverb “as” (“jako” in Czech) is an interesting phenomenon, because it appeared in both utterances 390 times more than in regular communication. This word may have fulfilled several roles in the utterances—the common usage (e.g. She works “as” a teacher), to express similarity (e.g. He behaves “like” a mad man), to connect (e.g. In winter “as well” as in summer), to present an example (e.g. Some people, “such as” old persons), and colloquially to express aloofness (e.g. So what?). The word may thus indicate a tendency to refer to another fact or parallel, or the inability to specify the content of the communication. The transition towards unspecified cognitive categories and metaphorical language might mean that the situation is cognitively more complex than is common, or that it is not sufficiently cognitively processed by the respondent (which manifests also on the verbalization level; see e.g. Lupyan & Casasanto, 2015).

In terms of the second research question (What are the specifics of the utterances on the lexical–morphological level in terms of respondents’ gender and various age cohorts?), the analysis of differences in linguistic categories usage in the utterances among various respondent groups proved several significant results, albeit with a relatively low effect size. In their utterances on the perception of the COVID-19 situation, men used fewer verbs, fewer prepositions, and fewer emotionally negatively loaded words. However, these findings generally conform to the referential research focusing on a common text, i.e. communication outside of an exceptional situation (e.g. Biber, 1991; Newman et al., 2008). That the presented findings are more of a result of common gender differences is supported by comparing the results with studies on the Czech language carried out within the CPACT project (Kučera, 2018b), i.e. the use of verbs and the more frequent use of the first person can be generally considered as a relatively reliable gender indicator (Kučera, 2020, p. 84). In terms of comparing the six cohorts (based on gender and age), the distinctive differences include the parts of speech of prepositions, which were more frequently used by younger people (men and women) in comparison with middle-aged people, verbs, which were more frequently used by young people (especially women; in contrast, less frequently by older men), and first person, which was again used predominantly by young people, but also older women.

The results related to the third research question (In which manner is the lexical–morphological level of the utterances influenced by the respondents’ current emotional experience?), which concentrated on the relationship between the linguistic categories usage in the utterances and the score in the SEHW questionnaire (negative and positive emotions), confirm the premises presented in the referential research. Regarding the lexical–semantic basis of the utterances, it is apparent that negative emotional experience positively correlates with the usage of emotionally negatively loaded words (see Bernard et al., 2016). This relationship is even more distinctive in the sixth item of the SEHW questionnaire, which asks, within the negative emotions group, directly about the experience of fear, standing out in the group of younger women in particular. It is necessary to mention that especially the female group, namely younger women, generally scored highest in the negative emotions scales in comparison with other groups (although the negative emotions scores generally fell within the SEHW test norms). The congruence between words and emotions is therefore the most pronounced here. The importance of the aforementioned relationships is supported also by the comparison with the results of studies carried out within the CPACT project (see above), which attest to a significantly lower occurrence of emotionally negatively loaded words in a common text in contrast to their occurrence in the studied thematized utterances. The evidence of higher scores of negative emotional experiences in women (especially in younger women) is well documented in many cross-cultural studies (see, e.g. De Bolle et al., 2015; Klimstra et al., 2009). It could therefore be assumed that even in the state of emergency, the general trend is similar.

Regarding purely morphological variables, 93 significant correlations (without performing statistical correction) were found appearing in both utterances in the same manner, with six significant correlations thereof after performing Šidák’s p-adjustment. One of the interesting findings is, for instance, the higher usage of future tense in persons who describe more negative emotions, and, contrastingly, a higher usage of comparatives in persons who express more positive emotions. Let us add that both these morphological categories appear in the respondents’ utterances to a degree significantly different from common Czech text—they might therefore present a potentially interesting psychological indicator. A higher degree of future tense usage was documented, for example, in research focused on observing respondents’ confusion (see D’Mello & Graesser, 2012). Simultaneously, this relationship may be supported by the reasoning that the worries of respondents scoring higher in the negative emotions scale will be directed primarily towards the future (e.g. anticipatory anxiety, Butler & Mathews, 1987), and therefore refer to future. In terms of the use of comparative, the use of the second degree (comparison or gradation of the meaning) may be an expression of a certain aloofness of the communicator, related to experiencing the situation in a more positive manner. Nevertheless, a more precise interpretation must be verified by further research. It is pertinent to add that in contrast to anglophone research, no relationship of higher significance between the respondents’ characteristics and pronouns usage was detected. However, it is probably a specific characteristic of the Czech language, which does not require the use of a pronoun in a sentence. (The pronoun can be implicitly expressed by the verb form.) Additionally, the low frequency of these relationships is confirmed by previous research on Czech texts (see, e.g. Kučera, 2018a).

Several key findings arise from the presented study. The situation related to COVID-19 modified the respondents’ (personal) vocabularies in connection with the description of their emotional experience. The respondents presumably adapted to the general discourse, and a distinctive preference for words with a negative connotation appeared. Negatively emotionally loaded words occurred more frequently in women’s utterances and positively correlated with experienced negative emotions, especially with fear. This relationship was also confirmed within the whole sample. The experience of fear also positively correlated with the morphological category of future tense, where the highest scores were detected in the younger age category of 18–25 years.

The benefits of this study in comparison with big-data analyses of online communication (e.g. Yu et al., 2020; Madria & Kabir, 2020) lie in the emphasis on the psychological level of communication and the usage of standardized psychological measures, and the more precise thematic specification of the analysed texts (the relationship to subjective interpretation, emotional experience, and the respondents’ coping with the situation). Owing to the data collection procedure, it was also possible to ensure a higher representation of older people, who are especially important with regard to the topic of the study. Another valuable aspect of the presented research lies in the use of the combination of lexical–semantic and morphological analyses of the texts, in contrast to, for example, stand-alone sentiment analysis (e.g. Liu, 2015).

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 119 kb)