On the Relationship between Frames and Emotionality in Text (2023)

Abstract: Emotions, which are responses to salient events, can be realized in text implicitly, for instance with mere references to facts (e.g., “That was the beginning of a long war”). Interpreting affective meanings thus relies on the readers background knowledge, but that is hardly modeled in computational emotion analysis. Much work in the field is focused on the word level and treats individual lexical units as the fundamental emotion cues in written communication. We shift our attention to word relations. We leverage Frame Semantics, a prominent theory for the description of predicate-argument structures, which matches the study of emotions: frames build on a “semantics of understanding” whose assumptions rely precisely on peoples world knowledge. Our overarching question is whether and to what extent the events that are represented by frames possess an emotion meaning. To carry out a large corpus-based correspondence analysis, we automatically annotate texts with emotions as well as with FrameNet frames and roles, and we analyze the correlations between them. Our main finding is that substantial groups of frames have an emotional import. With an extensive qualitative analysis, we show that they capture several properties of emotions that are purported by theories from psychology. These observations boost insights on the two strands of research that we bring together: emotion analysis can profit from the event-based perspective of frame semantics; in return, frame semantics gains a better grip of its position vis-à-vis emotions, an integral part of word meanings.

## introduction human life is interwoven with emotions. they echo in our brain, body, behaviors, and attract for this reason a diverse range of disciplines @xcite . psychology, among others, has entered a century-long endeavor to explain how emotions arise, with appraisal theories @xcite providing a viewpoint that is widely accepted today: emotions are responses to (internal or external) events, specifically to circumstances evaluated as salient by their experiencers @xcite . understanding how humans evaluate events is thus fundamental to discuss this affective phenomenon, and appraisal theories offer many fertile insights on the matter. they spell out, for instance, some human reactions to events, like neurophysiological changes, motor expressions and motivational tendencies @xcite . from the perspective of an observer, these hint at what other people feel: the blushing on one's cheeks might reveal an episode of shame, the raising of a brow could indicate disappointment. emotions also pervade the sphere of verbal communication, where an observer infers the mental state of others by interpreting their utterances. decoding emotions from words is key to successful communication, since emotions represent an important aspect of the meaning that speakers and writers intend to convey @xcite . this is the idea that fuels (computational) emotion analysis in natural language processing @xcite , a research field geared towards the creation of systems that sense emotions like humans do. emotion analysis mainly approaches its task as text classification. it models the import of verbal expressions either as discrete categories, like anger and joy, or through scalar features such as valence and arousal @xcite . a central challenge in this regard is that emotions are expressed in language in a myriad of ways. at times they emerge explicitly, with words that point to an emotion state by definition (e.g., "i'm happy"). other times, however, emotions can be expressed without unequivocal cues, mental states or evaluative attitudes: writers can describe a stimulus event (e.g., "my granddad died", "my team won the match", which likely spark sad-ness and joy), or their reaction to it (e.g., "i cried", "i smiled"), trusting that the correct emotional interpretation of their production will be drawn by the readers via pragmatic inference @xcite . how can emotions be associated with such factual statements? psychology explains the link via empathy and affective role taking @xcite @xcite , and natural language processing connects emotion decoding more directly to world knowledge. its starting point is that words possess specific connotations in the collective imagination @xcite ) -e.g., die: sadness, win: joy, ghost: fear. accordingly, it stores such connotations as dictionaries of word-to-emotion associations @xcite . word-level dictionaries leverage the assumption that individual words are the crucial, emotion-revealing linguistic units. this view is practically useful, but it neglects an important point, namely the impact of the context in which words occur, and thus the paradigmatic and syntagmatic information that allow people to infer emotion meanings. for instance, the surrounding verbal context of "boiling" helps disambiguate if this predicate refers to a heat reaction with a nonemotional tone ("the water is boiling") or to an emotional turmoil ("she is boiling with anger"). much work in emotion analysis disregards this type of background knowledge. approaches that embed emotion meanings into latent vector spaces @xcite ) capture contextual information, but they are less transparent to investigation than lexical methods. in this article, we consider frame semantics (fillmore, 1982) as a source of lexical abstractions that is appropriate for specifying emotions in a dictionary. frame semantics proposes a formalism (viz., frames) and a practical resource (berkeley framenet, @xcite ) to describe linguistic meanings with a combination of predicates (i.e., frames) and arguments. this "semantics of understanding" or u-semantics @xcite explains the difference in meaning between "the water is boiling" and "she was boiling with anger" in terms of reference to two different frames that are evoked by the sentences, respectively. this framelevel disambiguation arguably makes use of knowledge about how the world is organised that is necessary to recognise which of the two sentences is emotional. it also suggests that frames bear a potential value for studies in emotion analysis, even though they are usually dismissed in the computational study of emotions. we believe that there are many affinities between emotions and frames. not only does framenet dedicate multiple frames to emotions (e.g., emotion directed and emotion of mental activity), but it pays attention to events, similar to appraisal theories. figure 1 il- appraisal theories frame semantics event evaluation emotion event properties frame my team won the first prize figure 1 : a comparison between the two fields we tap on. frame semantics studies texts by focusing on events and their characteristics. appraisal theories, interested in how emotions emerge in humans, also start from the consideration of events; they pay further attention to how event characteristics, as evaluated by individuals, lead to specific emotion reactions. based on this parallel between the two blocks in figure 1 , this article investigates the relationship between frames and emotions. as a first step, we refrain from analysing different emotions, and concentrate our attention on emotionality 1 , i.e., whether a text has an emotion content, irrespective of what it is. we ask: are framenet frames associated with emotionality? our expectation is that emotionality represents an integral part of more frames than those indicated in framenet as emotion-related ones. by borrowing the definition of emotional experiences from appraisal theories (i.e., emotions as processes engaging events, event evaluations, personal reactions), and assuming that all such diagnostic features can be communicated via language (e.g., events: "my team won the match", evaluations: "victory was well deserved", reactions: "i'm happy", "i'm all steamed up!"), one can conjecture that many frames that are apparently affect-less correspond in fact to the conceptualization of some emotion components. verifying our conjecture is relevant from two complementary perspectives. for researchers in emotion analysis, we put framenet up to scrutiny as a suitable tool to tackle the emotional import of sentences. this could provide insights into the linguistic level at which an affective meaning comes to actualise (e.g., in the relation between words rather than words in isolation), and guide the field towards better automatic text interpretations. for frame semanticists, on the other hand, we inspect whether emotions are an underlying component of the meaning of frames. at the methodological level, we avoid making assumptions as to which frames are emotional, but exploit an automatic procedure to identify them at scale. we start from a large unlabelled corpus of contemporary american english, on which we add two independent layers of automatic annotation, to label sentences both with binary emotion categories and the frames that they evoke. then, by investigating the mutual information between the two, we provide evidence that emotionality is an important aspect of frames (by association, not by definition). besides frames with no emotional import (e.g., storing) and frames that are associated with some degree of emotion (e.g., cure), framenet includes a substantial group of strongly emotional frames. among these are instances evoked by unambiguously emotional predicates (e.g., emotion directed, fear), and others expressing strongly emotionally loaded events (e.g., dying), bolstering our perspective on the affective dimension of language as described by appraisal theories. as a concrete result of our analysis, we release a resource 2 with frames-to-emotion associations that can be employed in alternative to typical word-to-emotion lexicons. the paper starts with an overview of relevant fields. section 2 introduces emotions, with a focus on how they are studied in text, and section 3 describes framenet, in relation to emotions and the task of semantic role labelling. section 4 presents the experimental setting used to address our research question. our main contribution is presented in section 5, which also elaborates on a possible grouping of the framenet emotion vocabulary with a qualitative analysis grounded in appraisal theories, followed by an extensive discussion of its implications in section 6. we conclude with a summary of the present work and indicate viable ventures for future research. ## emotion in language emotions in psychology. the body of psychological literature on emotions is extensive and controversial. the field has long established that these states can be investigated systematically (cf. @xcite , but it has reached little consensus on the details, specifically concerning what emotions are, and whether (and which) can be considered cross-cultural universals. several theories focus indeed on diverse sets of emotions, motivated by specific views on their evolutionary relevance @xcite , or on their underlying dimensions @xcite . ultimately, however, different research lines agree on one point. there exists a handful of "diagnostic features" which indicate that an emotion is taking place @xcite : typically, a starting cause is there (e.g., an event happens); it is evaluated by its experiencers; and it sparks in them some concrete effects, like changes in their voice and posture. to organise these observations, appraisal theories study emotions in terms of sets of evaluations @xcite . when a stimulus presents itself to an individual, it is evaluated (i.e., appraised) in relation to the individual's goals, beliefs and desires. for this reason, an appraisal corresponds to specific effects -if i win the competition, i might smile and feel a pleasant sensation because winning supports my wellbeing; my opponent likely does not have the same reaction. such effects involve various subsystems, all of which are engaged in an emotion process together with the cognitive appraisal. they consist of a neurophysiological component (i.e., bodily symptoms, like heart beating faster), a motor component (i.e., facial and vocal expressions), a motivational component (i.e., action dispositions), and a subjective feeling component (e.g., winning the competition feels good) @xcite . from psychological research, we retain the idea that an emotion episode involves at least three aspects that can mirror in language: emotion stimuli (i.e., what happens), evaluations (how that is assessed in the light, e.g., of who initiates or is affected by the stimulus), and reactions (e.g., bodily manifestations of emotions). ## frame semantics framenet. the theory of frame semantics fundamentally assumes that utterances are understood via frames @xcite . a frame represents a situation fragment that serves to match a word (or a group thereof) to the bundle of knowledge it presupposes @xcite . for instance, the term "abandon" evokes a conceptual category instantiated by different events (e.g., leaving a membership group, or metaphorically, quitting a bad habit) which comprise a series of participants (e.g., the group being left, the person dropping out of it). the corresponding frame, abandonment, binds together these bits of knowledge. for english, the berkeley framenet project @xcite has been curating the lexical resource framenet. it provides an inventory of predicates (lexical units), roles (arguments), and frames. its latest release (framenet 1.7) counts over 13k lexical units and 1.2k frames, which connect to one another via specific frame-to-frame (f2f) relations such as inheritance, subframe, or using @xcite . an example for the frame abandonment from the database foot_1 is in ## methods our goal is to @xcite to what extent (i.e., quantitatively) the emotionality of texts is mirrored in the frames that the texts evoke; (b) if there is a qualitative difference between the emotionality that frames carry; and whether (c) these aspect can help in starting a discussion of emotions in framenet. framenet contains a narrow emotion nucleus, but for most of the frames their 'emotionality status' (whether or not the situation is emotional) is not specified. this constitutes the core of our investigation. accessing data with the two types of information that we need is not straightforward. no resource for emotion analysis is labelled with frame semantics information, except for the dataset by @xcite , which is limited in size and only includes emotionbearing texts. likewise, corpora for frame-semantic parsing do not contain emotion annotations -at least, not for the vast majority of frames. as a solution, we devise a method that combines the use of neural technologies and prior knowledge about language as contained in framenet: we correlate the categorical variable of emotionality (obtained through an emotion classifier) with that of frame membership (grasped by a frame identification tool). we use this correlation to find categories of frames (inherently emotional, inherently nonemotional, and others) and to explain their belonging to one category or another in quantitative terms. focussing on the emotional frames, we conduct a qualitative discussion based on @xcite , which explains emotions as processes involving the subsystems of an organism (cognitive, motivational, motor, etc.), and has a theoretical counterpart in linguistics. 4 ## emotionality-frame associations the processing steps described in section 4.1 result in two independent layers of annotation for the same texts, for which all other frames fall within the second and third quartiles of the distribution: the fact that the pmi values in figure 3 are approximately normally distributed around 0 does not indicate the absence of a correlation between emotions and frames; it rather tells us that a large group of items are neither strongly associated with nonemotionality nor with emotionality. these 378 frames will be referred to as contextually determined for reasons discussed in section 5.3. in the following subsections, we characterise the emotional, nonemotional, and contextuallydetermined frames, validating the findings of the pmi procedure with a detailed qualitative analysis. ## discussion we conducted a pmi-based analysis guided by the research question "are framenet frames associated with emotionality?" as well as two leading hypotheses: first, emotional frames constitute a large part of framenet, and second, it makes sense to talk about "emotional" frames in the sense that the lexical units within the frame behave coherently. both assumptions proved correct. frames that carry emotionality extend beyond the current organization of the database, as many are emotional while having a factual denotation; further, they pass this affective trait on to their lexical units. our manual analysis explains what frames have in common from the perspective of emotions, confirming that there are many levels of an emotion mechanism captured by frame semantics. some frames depict concepts that seem more descriptive than affective, but it is precisely in this manner that they pick up on some important components of emotions. they correspond to some of the factors that elicit, underlie or manifest an emotion, like events, event evaluations, and emotion effects. the effects components, in particular, not only correspond to phenomena that happen in response to emotion-eliciting events (e.g., facial expression). they can be considered events per se, and consequently, they can evoke specific frames. we manually group these characteristics in four clusters, motivated by the original structure of framenet, and the fact that appraisal models and frames are grounded on a notion of event. ruppenhofer (2018) already pointed out that appraisal theories can inform an investigation of the emotion vocabulary in framenet. we bolster that observation by indicating the frames to which it extends, but one could also identify other emotion properties and other links to theories different from appraisals, and organise the emotional frames accordingly. take, for instance, the appraisalbased cluster. in our proposal, it includes both items that contribute to eliciting an emotion (e.g., difficulty), and items that result from it (e.g., fleeing). this is a fruitful distinction that can be made to find more finegrained theoretical coherence in the obtained statistical associations. further, we empirically show that there are frames somewhat transparent to emotions: contextuallydetermined frames reiterate the need to think about emotionality in terms of relations between words, and raise the question of if and how frames influence the emotionality of a text, as well as its automatic classification. to what extent do predicates or arguments contribute to the decisions of an emotion classifier? is compositionality at play? we have previously pointed out that emotionality is a continuum, while our study approaches it through categorical lenses. from a practical standpoint, this categorization has the value of generating clear insights. but this choice introduces limitations, not least of which is a certain degree of arbitrariness in such divisions: frames do not necessarily fit into the three nonemotional, contextually-determined, and emotional "boxes" identified with the help of quartiles, and the line between contextually-determined and emotional frames (in particular the incidentally emotional ones) is blurred. in fact, the compelling case that emotionality is always a matter of context could be made also for many emotional frames, with the emotion stimuli being evident cases (events could stir an emotion or not, depending on who experiences them and how they are rendered in language). our results prove however that a separation holds, at least in coca, between frames for which exhibiting the emotional association is invariably contextual, whereas others maintain a certain level of emotionality -e.g., emotion stimuli have the tendency to denote events with potentially dramatic consequences for their experiencers, see for instance violence or catastrophe. in sum, our analysis reveals that the relationship between the emotionality of a sentence and that of frames is not straightforward. frames that have a strong positive or negative association to emotionality can be found in texts that express the opposite affective content overall. 21 even the frames that framenet explicitly associates to the emotion domain are evoked by nonemotional sentences. emotions by stimulus, as an example, is found by the frame identifier in the nonemotional "i had every right to descend this stair, to walk among the glad company […]", because of the lexical unit "glad". rather than putting the automatic annotation into question, this outcome sheds light on an important fact. namely, sentence-level emotionality classifiers can disregard emotional subtleties. a verbal expression might have a predominant connotation to convey (e.g., a nonemotional one, in the example above), and which might be correctly identified by the automatic system; yet, by considering entities besides the subject, different emotion nuances emerge (e.g., the company is glad). classifiers might fail to account for those, and in such cases the performance of frame identification tools can complement theirs. in line with previous work @xcite , we thus found that approaches based on embeddings and on human-curated resources help one another also in emotion analysis. ## conclusion the phenomenon of "emotions" is psychological in nature but pervades language. there, the presence of overt markers (the adjectives "sad", "happy", for instance) is not necessary for an emotion to be conveyed. these "untold" emotions spurred much attention in the field of computational emotion analysis (balahur and 21 note that there are signs of domain dependence: frames are more emotional in certain domains of coca than in others. for example, run risk has a pmi value of .13 in textual blogs, which raises to .4 in the domain of fiction; and the frame protest turns out considerably more emotional if evoked by fiction-(pmi=.77) than by tv-related texts (.47). consistent with this observation, a wilcoxon signed-rank test reveals a significant difference between the general pmi values of the emotional frames reported in figure 3 and the values of the same frames in the various domains (for all of them, except for tv, @xmath2.05). therefore, emotionality is only partly consistent across genres, and this finding is in line with existing literature on the genre dependence of fine-grained emotions (e.g., @xcite . at the same time, pmi differences are rarely as extreme as to have frames that are emotional in figure 3 turn into nonemotional in a specific domain (that only happens for attitude description, precariousness, and temperature). @xcite , which strives to automatise the ability to infer them. within such a context, we left traditional, lexicalbased approaches of emotion analysis, because interpreting emotions can require a great deal of extralinguistic knowledge. we considered the role that background information plays in emotions understanding, moving our attention to the meeting point between syntax and the u-semantics of fillmore, which presupposes an acknowledgement of the physical and social world, and therefore accounts for the structural components of real-life events that stimulate emotional responses. this way, our work combined methods for computational linguistics with theories from psychology and linguistics, and it showed how these fields can influence (in fact, fertilize) one another. below, we summarise the relevance of our findings in this interdisciplinary perspective, and point out promising next steps to take.