Recognizing Euphemisms and Dysphemisms Using Sentiment Analysis (2020)

Abstract: This paper presents the first research aimed at recognizing euphemistic and dysphemistic phrases with natural language processing. Euphemisms soften references to topics that are sensitive, disagreeable, or taboo. Conversely, dysphemisms refer to sensitive topics in a harsh or rude way. For example, “passed away” and “departed” are euphemisms for death, while “croaked” and “six feet under” are dysphemisms for death. Our work explores the use of sentiment analysis to recognize euphemistic and dysphemistic language. First, we identify near-synonym phrases for three topics (firing, lying, and stealing) using a bootstrapping algorithm for semantic lexicon induction. Next, we classify phrases as euphemistic, dysphemistic, or neutral using lexical sentiment cues and contextual sentiment analysis. We introduce a new gold standard data set and present our experimental results for this task.

## introduction euphemisms are expressions used to soften references to topics that are sensitive, disagreeable, or taboo with respect to societal norms. whether as a lubricant for polite discourse, a means to hide disagreeable truths, or a repository for cultural anxieties, veiled by idioms so familiar we no longer think about what they literally mean, euphemisms are an essential part of human linguistic competence. conversely, dysphemisms make references more harsh or rude, often using language that is direct or blunt, less formal or polite, and sometimes offensive. for example, "passed away" and "departed" are common euphemisms for death, while "croaked" and "six feet under" are dysphemisms for death. following terminology from linguistics (e.g., @xcite ), we use the term an area of nlp that relates to x-phemisms is sentiment analysis, although the relationship is complex. a key feature of x-phemisms is that their directionality (euphemism vs. dysphemism) is relative to an underlying topic, which itself often has affective polarity. x-phemisms are usually associated with negative topics that are culturally disagreeable or have a negative connotation, such as death, intoxication, prostitution, old age, mental illness, and defecation. however x-phemisms also occur with topics that are sensitive but not inherently negative, such as pregnancy (e.g., "in a family way" is a euphemism, while "knocked up" is a dysphemism). in general, dysphemistic language increases the degree of sensitivity, intensifying negative polarity or shifting polarity from neutral to negative. conversely, euphemistic language generally decreases sensitivity. but euphemisms for inherently negative topics may still have negative polarity (e.g., vomiting is unpleasant no matter how gently it is referred to). this paper presents the first effort to identify euphemistic and dysphemistic language in text. since affective polarity clearly plays a role in this phenomenon, our research explores whether sentiment analysis can be useful for recognizing x-phemisms. we deconstructed the problem into two subtasks. first, we identify phrases that refer to three sensitive topics: lying, stealing, and firing (job termination). we use a weakly supervised algorithm for semantic lexicon induction @xcite to semi-automatically generate lists of near-synonym phrases for each topic. second, we investigate two methods to classify phrases as euphemistic, dysphemistic, or neutral foot_0 . (1) we use dictionary-based methods to explore the value of several types of information found in sentiment lexicons: affective polarity, connotation, intensity, arousal, and dominance. (2) we use contextual sentiment analysis to classify x-phemism phrases. we collect sentence contexts around instances of each candidate phrase in a large corpus, and assign each phrase to an x-phemism category based on the polarity of its contexts. finally, we introduce a gold standard data set of human x-phemism judgments and evaluate our models for this task. we hope that this new data set will encourage more work on x-phemisms. our experiments show that sentiment connotation and affective polarity can be useful for identifying euphemistic and dysphemistic phrases, although this problem remains challenging. ## related work euphemisms and dysphemisms have been studied in linguistics and related disciplines (e.g., @xcite @xcite @xcite ), but they have received little attention in the nlp community. @xcite recognized code words in "euphemistic hate speech" by measuring cosine distance between word embeddings. but their code words conceal references to hate speech rather than soften them (e.g., the code word "skypes" covertly referred to jews), which is different from the traditional definition of euphemisms that is addressed in our work. the nlp community has explored several linguistic phenomena related to x-phemisms, such as metaphor (e.g., @xcite @xcite ), politeness (e.g., @xcite ), and formality (e.g., @xcite ). @xcite found that people comprehend metaphorical euphemisms or dysphemisms more quickly when they share the same underlying conceptual metaphor. for example, people are likely to use the euphemism "parted ways" to describe ending a relationship in the context of the conceptual metaphor a relationship is a journey, but more likely to use the euphemism "cut their losses" in the context of the metaphor a relationship is an investment. our research focuses on the relationship between x-phemisms and sentiment analysis. we take advantage of several existing sentiment resources, including the nrc emolex, vad, and affective intensity lexicons @xcite and connotation wordnet @xcite . we also re-implementated the nrc-canada sentiment classifier @xcite to use in our work. @xcite examined the connotation of color terms according to how often they appear in dysphemistic, euphemistic, or neutral contexts. for instance, "blue" is often used as a euphemism for "sad", while "yellow" can be dysphemistically used to mean "cowardly". our paper takes the reverse approach, recognizing x-phemisms by means of connotation. rababah (2014) studied how medical professionals use x-phemisms when talking to patients and found that serious conditions tend to inspire more euphemism. rababah argued that translating xphemisms appropriately is important when providing medical care to non-english speakers. it follows that it is important for machine translation systems to preserve euphemistic language across translations in medical applications. more generally, machine translation systems should be concerned not only with preserving the intended semantics but also preserving the intended discourse pragmatics, which includes translating euphemisms into euphemisms and translating dysphemisms into dysphemisms. when a speaker chooses to use a euphemistic or dysphemistic expression, that choice usually reflects a viewpoint or bias that is a significant property of the discourse. consequently, it is important for nlp systems to recognize xphemisms and their polarity, both for applications where views and biases are central (e.g., medicine, argumentation and debate, or stance detection in political discourse) and for comprehensive natural language understanding in general. we tackled this problem by decomposing the task into two steps: (1) identifying near-synonym phrases for a topic, and (2) classifying each phrase as euphemistic, dysphemistic, or neutral. for the first step, we considered using existing thesauri (e.g., wordnet, roget's thesaurus, wiktionary, etc.) but their synonym lists were relatively small. foot_1 roget's thesaurus was among the best resources, but included only a few dozen entries for most topics. furthermore, x-phemisms can stretch meaning to soften or harden a sensitive subject, so we wanted to include near-synonyms that have a similar (but not identical) meaning. for example, laid off, resigned, and downsized are not strictly synonymous with firing, but broadly construed they all refer to job termination. ultimately, we decided to use the basilisk bootstrapping algorithm for weakly supervised semantic lexicon induction @xcite . basilisk begins with a small set of seed terms for a desired category and iteratively learns more terms that consistently occur in the same contexts as the seeds. while there are other methods for nearsynonym generation (e.g., @xcite ), we chose basilisk because it can learn phrases corresponding to syntactic constituents (e.g., nps and vps) and can use lexico-syntactic contextual patterns. for the bootstrapping process, we used the english gigaword corpus because it contains a large and diverse collection of news articles. we focused on three sensitive topics that are common in news and rich in x-phemisms: lying, stealing, and firing (job termination). ## generating near-synonym phrases with semantic lexicon induction the basilisk algorithm learns new phrases for a category using a small list of "seed" terms and a text corpus. in an iterative bootstrapping framework, basilisk extracts contextual patterns surrounding the seed terms, identifies new phrases that consistently occur in the same contexts as the seeds, adds the learned phrases to the seed list, and restarts the process. our categories of interest (lying, steal-ing, firing) are actions, so we wanted to learn verb phrases as well as noun phrases (e.g., event nominals). consequently, we provided basilisk with two seed lists for each topic, one list of verb phrases (vps) and one list of noun phrases (nps). to collect seed terms, we identified common phrases for each topic that had high frequency in the gigaword corpus. the seed lists are shown in ## gold x-phemism data set to create a high-quality gold data set for xphemism classification, we asked three people (not the authors) to label the near-synonym phrases for each topic on a scale from 1 to 5, where 1 is most dysphemistic, 3 is neutral, and 5 is most euphemistic. for each phrase, we computed the average score across the three annotators and assigned each phrase to a "gold" x-phemism category: phrases with @xmath0.5 were labeled dysphemistic, phrases with @xmath1.5 were labeled euphemistic, and the rest were labeled neutral. to assess inter-annotator agreement, we assigned each annotator's score to one of the three x-phemism categories using the same ranges as above, and measured the category agreement for 4 basilisk ran for 200 iterations learning 5 words per cycle. 5 one of the authors did this filtering. our goal was merely to obtain a list of near-synonyms to use for x-phemism classification, and not to evaluate the near-synonym generation per se since that is not the main contribution of our work. ## x-phemism classification with sentiment lexicons euphemisms and dysphemisms capture softer and harsher references to sensitive topics, so one could argue that this phenomenon falls within the realm of sentiment analysis. but x-phemisms are a distinctly different phenomenon. it may be tempting to equate euphemisms with positive sentiment and dysphemisms with negative sentiment, but xphemisms refer to sensitive topics that typically have strong affective polarity (usually negative). for example, vomiting is never a pleasant topic, no matter how it is referred to. consequently, most euphemisms for vomiting still have negative polarity (e.g., "be sick" or "lose your lunch"). however some euphemisms can have neutral polarity, such as scientific or formal terms (e.g., "regurgitation"), and occasionally a euphemism will evoke positive polarity for a negative topic through metaphor (e.g., "pushing up daisies" for death). in this section, we investigate whether sentiment information can be beneficial for recognizing euphemisms and dysphemisms and establish baseline results for this task. we explore five properties associated with sentiment: affective polarity, connotation, intensity, arousal, and dominance. as our first baseline, we assess the effectiveness of using positive/negative affective polarity (valence) information to label x-phemism phrases using two sentiment lexicons: the nrc emolex and vad lexicons @xcite . for the specific emotions, we considered anger, disgust, fear, sadness, and surprise to be negative, and anticipation, joy, and trust to be positive. another sentiment property related to x-phemisms is connotation. euphemisms often include terms with positive connotation to soften a reference, and dysphemisms may include terms with negative connotation to make a reference more harsh. but importantly, connotation and x-phemisms are not the same phenomenon. for one, many terms with a strong connotation are not x-phemisms. also, as with polarity, euphemisms can retain a negative connotation because the underlying topic has negative polarity. but since connotation and x-phemisms are related, we investigate whether connotation polarities from connota-tionwn @xcite can be valuable for labeling x-phemisms. we also explored the effectiveness of using affective intensity, arousal, and dominance information from the nrc affective intensity and vad @xcite for recognizing euphemistic and dysphemistic phrases. dysphemisms are often harsh and can be downright rude, so we hypothesized that terms with high arousal may be dysphemistic. conversely, euphemisms use softer and gentler language, so they may be associated with low arousal. dominant terms correspond to power and control, so it would be logical to expect that high dominance may be associated with euphemisms and low dominance may be associated with dysphemisms (e.g., "frail" and "weak") @xcite . for intensity, we used the nrc affective intensity lexicon (mohammad, 2018b), which associates words with specific emotions. we mapped the intensity scores so that high intensity values for negative emotions ranged from [0-0.5] (representing dysphemistic to neutral) and high intensity values for positive emotions ranged from [0.5-1] (representing neutral to euphemistic). the sentiment resources provide scores between 0 and 1. for polarities and connotation, 0 represents the strongest negative score and 1 represents the strongest positive score. for arousal, and dominance, the range is low (0) to high (1). we expect high arousal to be associated with dysphemism, so to be consistent with the other properties we reverse its range and replace each score s with 1-s. we score multi-word phrases by taking the average score of their words. once a phrase receives a score s, we map s to one of the three x-phemism categories as follows: @xmath2.25 ⇒ dysphemism, 0.25 < @xmath3.75 ⇒ neutral, and @xmath4.75 ⇒ euphemism. we chose these ranges to conservatively divide the space into quadrants, so that scores in the lowest quadrant represent dysphemism, scores in the highest quadrant represent euphemism, and scores in the middle are considered neutral. ## x-phemism classification with contextual sentiment analysis we hypothesized that the contexts around euphemisms and dysphemisms would be different in terms of sentiment. people often use euphemisms when they want to be comforting, supportive, or put a positive spin on a subject. in obituaries, for example, euphemisms for death are often accompanied by references to peace, heaven, flowers, and courage. in contrast, grisly murder mystery novels often use dysphemisms, speaking about death using harsh or graphic language. x-phemisms are also prevalent in political discourse. people frequently use euphemisms to argue for the merits of a particular subject (e.g., "enhanced interrogation" is a euphemism invoked to justify the use of tor-ture). conversely, people use dysphemisms when arguing against something (e.g., "baby killing" to refer to abortion). to investigate this hypothesis, we developed models to classify a phrase with respect to xphemism categories using sentiment analysis of its sentence contexts. we use the gigaword corpus and experiment with both sentiment lexicons and a sentiment classifier to evaluate sentence polarity. however polysemy and metaphor pose a major challenge: many phrases have multiple meanings. to address this problem, we create a subcorpus for each topic by extracting gigaword articles that contain a seed term for that topic (see once we have a set of sentence contexts for an x-phemism phrase, our first contextual model uses sentiment lexicons to determine each sentence's polarity. for each topic, we use the best-performing lexicons reported in section 6.1 (i.e., bestpair for fire and steal, and alldicts for lie). first, each word found in the lexicons is labeled positive for @xmath5.5 or negative for @xmath6.5. 7 we then assign a polarity to each sentence based on majority vote among its labeled words. sentences with an equal number of positive and negative words, or no labeled words, are ignored. x-phemisms are relative to a topic that itself often has strong affective polarity, so given a phrase p , our goal is to determine whether p 's contexts are positive or negative relative to the topic. to assess this, we generate a polarity distribution across all sentences in the topic's subcorpus. we will refer to all sentences in the subcorpus for topic t as sents(t) and the sentences in the subcorpus that mention phrase p as sents(t, p). we define pos(s) as the percent of sentences s labeled positive, and neg(s) as the percent of sentences s labeled negative, and classify each phrase p as follows: if pos(sents(t, p )) > pos(sents(t)) + γ then label p as euphemistic if neg(sents(t, p )) > neg(sents(t)) + γ then label p as dysphemistic else label p as neutral we set γ = 0.10 for our experiments. 8 intuitively, the γ parameter dictates that a phrase is labeled as euphemistic (or dysphemistic) only if its sentence contexts have a positive (or negative) percentage at least 10% higher than the sentence contexts for the topic as a whole. our second contextual model uses a sentiment classifier instead of lexicons to assign polarity to each sentence. we used a reimplementation of the nrc-canada sentiment classifier @xcite , which performed well in the semeval 2013 task 2. given a sentence, the classifier returns probabilities that the sentence is positive, negative, or neutral. we label each sentence with the polarity that has the highest probability. since the classifier provides labels for all three polarities (whereas we only got positive and negative polarities from the lexicons), we use a slightly different procedure to label a phrase. first, we compute the percent of subcorpus sentences containing phrase p that are assigned each polarity (pos, neg, neu), and compute the percent of all subcorpus sentences assigned each polarity. then we compute the difference for each polarity. for example, ∆(pos) = pos(sents(t, p ))-pos(sents(t)). this represents the difference between the percent of positive sentences containing p and the percent of positive sentences in the subcorpus as a whole. finally, we label phrase p based on the polarity that had the largest difference: pos ⇒ euphemistic, neg ⇒ dysphemistic, neu ⇒ neutral. ## conclusions this paper presented the first effort to recognize euphemisms and dysphemisms using natural language processing. our research examined the relationship between x-phemisms and sentiment analysis, exploring whether information about affective polarity, connotation, arousal, intensity, and dominance could be beneficial for this task. we used semantic lexicon induction to generate near-synonyms for three topics, and developed lexicon-based and context-based sentiment analysis methods to classify phrases as euphemistic, dysphemistic, or neutral. we found that affective polarity and connotation information were useful for this task, and that identifying sentiment in sentence contexts around a phrase was generally more effective than labeling the phrases themselves. promising avenues for future work include incorporating methods for recognizing politeness, formality, and metaphor. euphemisms and dysphemisms are an exceedingly rich linguistic phenomenon, and we hope that our research will encourage more work on this interesting yet challenging problem.