From da9ee8ddbe852a3549e2c028e71f8e3f29a46501 Mon Sep 17 00:00:00 2001 From: Gede Primahadi Wijaya Rajeg Date: Thu, 20 Oct 2022 22:56:13 +0800 Subject: [PATCH] Add new section for comparison of lex. vs. corpus results; subjugator -> superior. --- ...nt-metaphors-of-anger-in-Indonesian-ms.Rmd | 24 ++++++++++++------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/salient-metaphors-of-anger-in-Indonesian-ms.Rmd b/salient-metaphors-of-anger-in-Indonesian-ms.Rmd index b5c7ab6..84ce2ce 100644 --- a/salient-metaphors-of-anger-in-Indonesian-ms.Rmd +++ b/salient-metaphors-of-anger-in-Indonesian-ms.Rmd @@ -54,7 +54,7 @@ Indonesian emotions have also been analysed using the *Conceptual Metaphor Theor ## Previous studies on [anger]{.smallcaps} in Indonesian {#previous-study-anger} -Several works have been conducted on Indonesian [anger]{.smallcaps}. Heider [-@heider_landscapes_1991, 57, Table 7] discovered that, in representing anger, figurative expressions (i.e., *palak* 'stifling; angry' and *panas hati* 'lit. hot liver; angry') received higher rating than the literal expression (i.e., *marah*). Heider [-@heider_landscapes_1991, 24-25] also proposed four [anger]{.smallcaps}-like clusters in Minangkabau Indonesian: (i) "anger" clusters (*naik darah* 'lit. rising blood; angry'), (ii) "anger/cruel" clusters (*bengis* 'cruel; harsness'), (iii) "anger/dislike" clusters (*gemas* 'irritated'), and (iv) "anger/trembling" clusters (*gemetar* 'trembling'). The elicited scenarios from the "anger/cruel" clusters revealed that the antecedents of anger "are hurtful acts by others, especially naughty children, and the outcomes are physical violence and verbal abuse" [@heider_landscapes_1991, 80, 116] (e.g., [§\@ref(verbal-behaviour-typebased)](#verbal-behaviour-typebased) and [§\@ref(violent-behaviour-typebased)](#violent-behaviour-typebased)). Heider [-@heider_landscapes_1991, 80] also noted that in the actual, spontaneous behaviour (rather than in the elicited behaviour), Indonesians "mask most anger, and the open expression of anger is strongly disapproved of and negatively sanctioned". +Several works have been conducted on Indonesian [anger]{.smallcaps}. Heider [-@heider_landscapes_1991, 57, Table 7] discovered that, in representing anger, figurative expressions (i.e., *palak* 'stifling; angry' and *panas hati* 'lit. hot liver; angry') received higher rating than the literal expression (i.e., *marah*). Heider [-@heider_landscapes_1991, 24-25] also proposed four [anger]{.smallcaps}-like clusters in Minangkabau Indonesian: (i) "anger" clusters (*naik darah* 'lit. rising blood; angry'), (ii) "anger/cruel" clusters (*bengis* 'cruel; harshness'), (iii) "anger/dislike" clusters (*gemas* 'irritated'), and (iv) "anger/trembling" clusters (*gemetar* 'trembling'). The elicited scenarios from the "anger/cruel" clusters revealed that the antecedents of anger "are hurtful acts by others, especially naughty children, and the outcomes are physical violence and verbal abuse" [@heider_landscapes_1991, 80, 116] (e.g., [§\@ref(verbal-behaviour-typebased)](#verbal-behaviour-typebased) and [§\@ref(violent-behaviour-typebased)](#violent-behaviour-typebased)). Heider [-@heider_landscapes_1991, 80] also noted that in the actual, spontaneous behaviour (rather than in the elicited behaviour), Indonesians "mask most anger, and the open expression of anger is strongly disapproved of and negatively sanctioned". Rajeg [-@rajeg_metafora_2013, 211-214] revealed that eight metaphors are significantly attracted to *amarah/kemarahan* 'anger'. They are [controlling emotion is controlling a moving object]{.smallcaps}, [emotion is pressurised substance]{.smallcaps}, [emotion is fluid in a container]{.smallcaps}, [emotion is heated fluid in a container]{.smallcaps}, [emotion is fire]{.smallcaps}, ([intensity of]{.smallcaps}) [emotion is temperature]{.smallcaps} ([hot/cold]{.smallcaps}), ([intensity of]{.smallcaps}) [emotion is verticality]{.smallcaps} ([high/low]{.smallcaps}), and [emotion is natural forces]{.smallcaps}. Six metaphors are statistically repelled: [emotion is a possessable object]{.smallcaps}, [causing emotion is object transfer]{.smallcaps}, [emotion is an accidental motion]{.smallcaps}, [emotion is a journey]{.smallcaps}, [becoming emotion is finding an object]{.smallcaps}, and [emotion is liquid]{.smallcaps}. The statistical attraction of Indonesian [anger]{.smallcaps} to the [heat]{.smallcaps}- and [substance]{.smallcaps}-related metaphors suggests the universality and centrality of these metaphors for [anger]{.smallcaps} as found in different languages [@kovecses_concept_2000], most notably English [@stefanowitsch_words_2006; @holland_cognitive_1987]. Rajeg's [-@rajeg_metafora_2013] quantitative study complements Yuditha's [-@yuditha_indonesian_2013] introspective proposal on the specific metaphors of anger. Lastly, Rajeg's [-@rajeg_metaphorical_2014] preliminary quantitative investigation demonstrates that distinctive metaphorical constructions across five [anger]{.smallcaps} synonyms prominently highlight the Intensity of anger. @@ -132,7 +132,7 @@ leipzig_size_sources <- leipzig_size %>% ``` -The dataset for the token-based, corpus approach is taken from the corpus files in the ILC (total size = `r prettyNum(sum(leipzig_size$total_tokens), big.mark = ",")` word-tokens). It is chosen since, to the best of my knowledge, ILC is the only open access source to the largest collection of Indonesian texts^[The alternative is the Indonesian corpus in *Sketch Engine* (SE), which is also from online materials as in ILC. However, SE is a paid service to which the institution I work in does not have paid subscription.] and allows downloading the raw corpus files. ILC mainly consists of randomly chosen websites (`r pull(filter(leipzig_size_sources, sources == "web"), perc_sources)`% of the total size) and online news (`r pull(filter(leipzig_size_sources, sources == "news"), perc_sources)`%), followed by the Wikipedia dumps (`r pull(filter(leipzig_size_sources, sources == "wikipedia"), perc_sources)`%) and a mixture of other sources (`r pull(filter(leipzig_size_sources, sources == "mixed"), perc_sources)`%). As in MPA [@stefanowitsch_words_2006], 1000 random concordance lines were retrieved for each *marah*, *amarah*, and *kemarahan* before manually discarding the irrelevant hits (i.e., duplicates, the predicative and attributive uses of the root *marah*, and the literal uses. Next, syntactically relevant collocations of the target terms with the potential source-domain lexical units (LUs) were manually determined [@stefanowitsch_happiness_2004, 138; @sullivan_frames_2013, 3, 5], adopting the *MetaNet* (MN) approach that integrates MPA with Construction Grammar and Frame Semantics [@sullivan_frames_2013; @oana_computational_2017; see @rajeg_metaphorical_2019 for a recent application to Indonesian]. The MIP was applied to determine whether the collocation of the target terms evoke metaphorical readings. It is determined whether the collocates' contextual meaning, when co-occurring with the [anger]{.smallcaps} terms, contrasts with their more basic meaning in other contexts, such that the "contextual meaning can be understood in comparison to the basic meaning" [@rajeg_metaphorical_2019, 64; @pragglejaz_mip_2007, 3; @sullivan_frames_2013, 36]. The KBBI was used to determine the basic meaning of the collocates with reference to MIP's features of basic meaning, namely "more concrete (what they evoke is easier to imagine, see, hear, feel, smell, and taste), related to bodily action, more precise (as opposed to vague), historically older, and are not necessarily the most frequent meanings" [@pragglejaz_mip_2007, 3]. An additional diagnostic to determine the basic meaning is a question proposed by Soriano [-@soriano_conceptualization_2005, 91]: "what exactly each expression 'was literally about'?". To illustrate, consider these two examples for two different ways to convey the existence of *kemarahan* 'anger'. +The dataset for the token-based, corpus approach is taken from the corpus files in the ILC (total size = `r prettyNum(sum(leipzig_size$total_tokens), big.mark = ",")` word-tokens). It is chosen since, to the best of my knowledge, ILC is the only open access source to the largest collection of Indonesian texts^[The alternative is the Indonesian corpus in *Sketch Engine* (SE), which is also from online materials as in ILC. However, SE is a paid service to which the institution I work in does not have paid subscription.] and allows downloading the raw corpus files. ILC mainly consists of randomly chosen websites (`r pull(filter(leipzig_size_sources, sources == "web"), perc_sources)`% of the total size) and online news (`r pull(filter(leipzig_size_sources, sources == "news"), perc_sources)`%), followed by the Wikipedia dumps (`r pull(filter(leipzig_size_sources, sources == "wikipedia"), perc_sources)`%) and a mixture of other sources (`r pull(filter(leipzig_size_sources, sources == "mixed"), perc_sources)`%). As in MPA [@stefanowitsch_words_2006], 1000 random concordance lines were retrieved for each *marah*, *amarah*, and *kemarahan* before manually discarding the irrelevant hits (i.e., duplicates, the predicative and attributive uses of the root *marah*, and the literal uses). Next, syntactically relevant collocations of the target terms with the potential source-domain lexical units (LUs) were manually determined [@stefanowitsch_happiness_2004, 138; @sullivan_frames_2013, 3, 5], adopting the *MetaNet* (MN) approach that integrates MPA with Construction Grammar and Frame Semantics [@sullivan_frames_2013; @oana_computational_2017; see @rajeg_metaphorical_2019 for a recent application to Indonesian]. The MIP was applied to determine whether the collocation of the target terms evoke metaphorical readings. It is determined whether the collocates' contextual meaning, when co-occurring with the [anger]{.smallcaps} terms, contrasts with their more basic meaning in other contexts, such that the "contextual meaning can be understood in comparison to the basic meaning" [@rajeg_metaphorical_2019, 64; @pragglejaz_mip_2007, 3; @sullivan_frames_2013, 36]. The KBBI was used to determine the basic meaning of the collocates with reference to MIP's features of basic meaning, namely "more concrete (what they evoke is easier to imagine, see, hear, feel, smell, and taste), related to bodily action, more precise (as opposed to vague), historically older, and are not necessarily the most frequent meanings" [@pragglejaz_mip_2007, 3]. An additional diagnostic to determine the basic meaning is a question proposed by Soriano [-@soriano_conceptualization_2005, 91]: "what exactly each expression 'was literally about'?". To illustrate, consider these two examples for two different ways to convey the existence of *kemarahan* 'anger'. (@kemarahan_terjadi) *Kemarahan Presiden Jokowi __terjadi__ saat meninjau Pelabuhan Tanjung Priok (...)* (ind-id_web_2015_3M: 1310067)^[At the end of the numbered example, the source of the example is given in the format "(corpus file name: sentence id)" as in (ind-id_web_2015_3M: 1310067).] @@ -658,13 +658,13 @@ The type-based analysis reveals that [`r str_replace(pull(slice_max(metonymy_typ - `r paste(paste("*", pull(filter(metonymy_typebased, str_detect(CM_BROADER, "verbal")), LU), "* '", pull(filter(metonymy_typebased, str_detect(CM_BROADER, "verbal")), LU_GLOSS), "'", sep = ""), collapse = "; ")` -These expressions are mostly verbs encoding the manner of speaking that metonymically points to the internal, emotional state of the speaker. When someone speaks in the manner conveyed by these expressions, the hearer could infer that the speaker is angry. +These expressions are mostly verbs encoding the manner of speaking that metonymically points to the internal, emotional state of the speaker. When someone speaks in the manner conveyed by these expressions, the hearer could infer that the speaker is angry. Several expressions glossed as ‘snarl/snap at’ could also reflect the metaphorical mapping “aggressive animal behaviour → angry human behaviour”. ### [Violent frustrated behaviour for anger]{.smallcaps} {#violent-behaviour-typebased} -Linguistic expressions evoking certain harsh and violent actions/behaviours can be metonymically used to refer to anger. This is motivated experientially in that frustration often leads to anger, which then brings about some irrational, harsh, dangerous, or violent actions. +Linguistic expressions evoking certain harsh and violent actions/behaviours can be metonymically used to refer to anger. This is motivated experientially in that frustration often leads to anger, which then brings about some irrational, harsh, dangerous, or violent actions. The actions can be directed to oneself or others. -- `r paste(paste("*", pull(filter(metonymy_typebased, str_detect(CM_BROADER, "frustated")), LU), "* '", pull(filter(metonymy_typebased, str_detect(CM_BROADER, "frustated")), LU_GLOSS), "'", sep = ""), collapse = "; ")` +- `r paste(paste("*", pull(filter(metonymy_typebased, str_detect(CM_BROADER, "frustated")), LU), "* '", pull(filter(metonymy_typebased, str_detect(CM_BROADER, "frustated")), LU_GLOSS), "'", sep = ""), collapse = "; ") %>% str_replace_all(fixed("typically"), "especially")` The metonymy is a sub-case of a more generic metonymy, namely [effect of emotion stands for emotion]{.smallcaps}. @@ -787,7 +787,9 @@ The token-based, corpus study reveals `r nrow(marah)` tokens of metaphorical exp # knitr::kable(filter(metaphor_salience_print, Aggregate > 5), caption = "Source domains of [anger]{.smallcaps} based on the token-based, corpus approach (Aggregate values > 5%)") knitr::kable(mutate(metaphor_salience_print, `Metaphorical source domains` = str_replace_all(`Metaphorical source domains`, "luminousity", "luminosity"), - `Metaphorical source domains` = str_replace_all(`Metaphorical source domains`, fixed("dimension"), "dimension/size")), caption = "Source domains of [anger]{.smallcaps} (Token-based, corpus approach)", row.names = FALSE) + `Metaphorical source domains` = str_replace_all(`Metaphorical source domains`, fixed("dimension"), "dimension/size"), + `Metaphorical source domains` = str_replace_all(`Metaphorical source domains`, fixed("subjugator"), "superior")), + caption = "Source domains of [anger]{.smallcaps} (Token-based, corpus approach)", row.names = FALSE) ``` The top-20 metaphors in [Table \@ref(tab:metaphor-table-token-based)](#metaphor-table-token-based) will be discussed to roughly match the similar number of metaphors found in the lexical approach ([Table \@ref(tab:metaphor-table-type-based)](#metaphor-table-type-based)). Just over half (i.e., `r round(length(metaphor_in_all_database_top20)/20 * 100)`%; N=`r length(metaphor_in_all_database_top20)`) of the metaphors in the top-20 list are shared between the two approaches. The metaphorical mappings for each metaphor will be presented in the decreasing order of their type frequencies (i.e., the number of linguistic expressions) to ease the identification of the "main meaning focus" of the metaphor via "the metaphorical linguistic expressions that _dominate_ a metaphor" [@kovecses_metaphor_2010, 140, italics is mine]. @@ -1252,7 +1254,7 @@ The metaphor occupies nearly similar rank in the two datasets (i.e., `r get_meta The [natural force]{.smallcaps} metaphor focuses on the intense effect of the natural force, as evident by the productivity of this mapping. This is a more specific way of construing the harmful effect of anger than the other [harm]{.smallcaps}-related metaphor based on different frames within the same family ([§\@ref(harm-tokenbased)](#harm-tokenbased)). -### [Anger is a subjugator]{.smallcaps} {#subjugator-tokenbased} +### [Anger is a superior]{.smallcaps} {#subjugator-tokenbased} ```{r mapping-subjugator-token, message = FALSE, include = FALSE} mapping_subjugator_token <- filter(metaphor_tokenbased_mapping, str_detect(CM_BROADER, "subjugator")) %>% @@ -1262,9 +1264,9 @@ mapping_subjugator_token_discuss <- get_mappings(metaphor_tokenbased_mapping, 's subjugator_frame_stats <- happyr::get_frames("subjugator", df = marah, frame_var = "SFRAME", metaphor_var = "CM_BROADER", lexunit_var = "MP") ``` -The [subjugator]{.smallcaps} metaphor is only attested in the corpus data and based on the mapping of anger onto the Subjugator role in the [servitude]{.smallcaps} frame. It is an alias to the [social superior]{.smallcaps} or [social force]{.smallcaps} metaphor [see @kovecses_metaphor_2000, 21, 70]. Kövecses [-@kovecses_metaphor_2000, 71] argues that this metaphor involves asymmetric control-relation between the social inferior (the Subjugated role in the [servitude]{.smallcaps} frame), namely the self (or its associates), and the social superior (Subjugator), namely the emotion. The authority element is incorporated into the [servitude]{.smallcaps} frame from the structure of the [authority]{.smallcaps} frame, which is the sub-case of the [control]{.smallcaps} frame. There are `r get_salience_stats(metaphor_salience, col_names = 'n_mapping', 'subjugator')` mappings in this metaphor, evoked by `r get_salience_stats(metaphor_salience, col_names = 'n_type', 'subjugator')` types (`r get_salience_stats(metaphor_salience, col_names = 'n_token', 'subjugator')` tokens). +The [subjugator]{.smallcaps} metaphor is only attested in the corpus data and based on the mapping of anger onto the Subjugator role in the [servitude]{.smallcaps} frame [cf. @kovecses_metaphor_2000, 21, 70]. Kövecses [-@kovecses_metaphor_2000, 71] argues that this metaphor involves asymmetric control-relation between the social inferior (the Subjugated role in the [servitude]{.smallcaps} frame), namely the self (or its associates), and the social superior (Subjugator), namely the emotion. The authority element is incorporated into the [servitude]{.smallcaps} frame from the structure of the [authority]{.smallcaps} frame, which is the sub-case of the [control]{.smallcaps} frame. There are `r get_salience_stats(metaphor_salience, col_names = 'n_mapping', 'subjugator')` mappings in this metaphor, evoked by `r get_salience_stats(metaphor_salience, col_names = 'n_type', 'subjugator')` types (`r get_salience_stats(metaphor_salience, col_names = 'n_token', 'subjugator')` tokens). -`r paste(paste("- ", unlist(str_split(get_key_mappings(mapping_subjugator_token), "___ ")), "\n", sep = ""), collapse = "")` +`r paste(paste("- ", unlist(str_split(get_key_mappings(mapping_subjugator_token), "___ ")), "\n", sep = ""), collapse = "") %>% str_replace(fixed("subjugator"), "superior/subjugator")` - being subjugated → effect of and losing control over anger (type=`r get_metaphor_mapping_n_lu(mapping_subjugator_token_stats, "being subjugated")`; token=`r get_metaphor_mapping_tokenfreq(mapping_subjugator_token, "being subjugated", lu_output = FALSE)`) @@ -1518,6 +1520,10 @@ metonymy_tokenbased_category_print %>% As in the lexical dataset ([§\@ref(metonymy-category-typebased)](#metonymy-category-typebased)), the number of metonymies in the physiological response category are greater than the ones in the social-communicative behaviour. However, the social-communicative behaviour category in total still exhibits significantly greater number of types (*p*~Binomial~ ~two~~-~~tailed~`r pval_print(metonymy_tokenbased_category_binomtest_type$p.value)`) and tokens (*p*~Binomial~ ~two~~-~~tailed~`r pval_print(metonymy_tokenbased_category_binomtest_token$p.value)`) than the physiological response category. Therefore, the lexical and corpus-based approaches converge along these distributional aspects of the metonymies. +# Comparison of the results in the lexical and corpus-based approaches + + + # Metonymical basis of [anger]{.smallcaps} metaphors {#metonymic-basis} Three of the five physiology-based metonymies suggested by Lakoff and Kövecses [-@holland_cognitive_1987, 197] motivate the two principal metaphors for anger: [anger is heat]{.smallcaps} and [anger is heated fluid in a container]{.smallcaps} [see @rajeg_metafora_2013, 211-213, for the quantitative evidence for the prominent status of these anger metaphors in Indonesian]. These three metonymies are [body heat for anger]{.smallcaps} ([§\@ref(body-heat-tokenbased)](#body-heat-tokenbased) and [\@ref(body-heat-typebased)](#body-heat-typebased)), [internal pressure for anger]{.smallcaps} ([§\@ref(internal-pressure-typebased)](#internal-pressure-typebased) and [\@ref(internal-pressure-tokenbased)](#internal-pressure-tokenbased)), and [redness in the facial area for anger]{.smallcaps} ([§\@ref(redness-typebased)](#redness-typebased) and [\@ref(redness-tokenbased)](#redness-tokenbased)). In addition to these, there are other physiology-related metonymies ([Table \@ref(tab:metonymy-category-table-typebased)](#metonymy-category-table-typebased) and [Table \@ref(tab:metonymy-category-table-tokenbased)](#metonymy-category-table-tokenbased)) motivating other metaphors. The list below shows the relevant metaphors (from the two approaches) motivated by the physiology-based metonymies.