EST e R : Combining Word Co-occurrences and Word Associations for Unsupervised Emotion Detection (2020)

Abstract: Accurate detection of emotions in user- generated text was shown to have several applications for e-commerce, public well-being, and disaster management. Currently, the state-of-the-art performance for emotion detection in text is obtained using complex, deep learning models trained on domain-specific, labeled data. In this paper, we propose ESTeR , an unsupervised model for identifying emotions using a novel similarity function based on random walks on graphs. Our model combines large-scale word co-occurrence information with word-associations from lexicons avoiding not only the dependence on labeled datasets, but also an explicit mapping of words to latent spaces used in emotion-enriched word embeddings. Our similarity function can also be computed efficiently. We study a range of datasets including recent tweets related to COVID-19 to illustrate the superior performance of our model and report insights on public emotions during the on-going pandemic.

## introduction human beings are known to perceive and feel various, highly-nuanced emotions, expressed both in spoken and written texts. modeling emotions in user-generated content has been shown to benefit domains such as commerce, public health, and disaster management @xcite @xcite . e.g., emotion cues from social media posts were used to identify depression and ptsd @xcite and for personalizing chatbots to improve user satisfaction @xcite . recent studies list as many as 154 human emotions @xcite . however, most researchers in psychology have largely agreed on a set of basic emotions such as anger, fear, disgust, sadness, surprise, and happiness @xcite and showed that complex emotions can be expressed using this basic set @xcite . for example, plutchik uses combinations, intensity, and opposites of basic emotions for capturing the higherorder emotions. that is, annoyance and rage can be viewed as the less or more intense forms of anger, and anticipation is the opposite of surprise. thus, most recent studies on automatic emotion detection use ekman's or plutchik's sets of 6 or 8 emotions, respectively @xcite . existing models for automatic emotion identification in user-generated texts typically use supervised learning techniques. the state-of-the-art emotion detection performance on tweets, news articles, blogs, reviews, and tv-show transcripts is obtained using complex, deep learning architectures that combine a range of features including terms, embeddings, and domain-specific aspects such as emojis, as well as human-generated lexicons of emotion-word associations @xcite @xcite @xcite . much manual effort is involved in collecting annotated data for a given domain and fine-tuning domain-specific models. other auxiliary works enabling emotion detection can be placed under two complementary directions. the first one is lexicon development for emotions via manual vocabulary labeling or automatic generation, for example, based on similarity to a set of seed words @xcite . the second direction uses a latent space of embeddings to compare sentences with emotion lexicons @xcite . compiling a lexicon of a high quality and coverage is a labor-intensive task, and even when automation and crowdsourcing is involved, a close manual control is required. as for latent space representations, the embedding model must include sufficient information about the underlying emotions, obtained, e.g., from the lexicons or labeled datasets @xcite @xcite . both embeddings and lexicons enable basic techniques for unsupervised emotion predictionfor example, by using word embeddings similarities @xcite or overlap between lexicon words and input text @xcite . considering the abundance of user-generated texts on the current-day web with its ever-changing topics (for example, "covid-19 lockdown"), we argue that it is desirable to develop advanced unsupervised models that detect emotions accurately across domains, offer a probabilistic explanation for the predicted emotions, while not depending on large quantities of labeled data. these desirables comprise our precise objectives in this paper. we present emotion-sensitive textrank (es-ter ) and its variants as our similarity functions that use word graphs for scoring input texts with reference to a given set of emotions. ester is designed based on the following two observations: (1) for a given language, words expressing emotions are fairly stable across domains @xcite . for example, the same words ("this is absurd...") may be used to express anger (emotion) regarding a product on an e-commerce website as well as in a tweet related to a goverment policy. (2) word-occurrence graphs are known to capture contextual and latent language information and were successfully used in various nlp tasks @xcite @xcite . we make the following contributions: in the next section (section 2), we present our scoring framework for emotion detection along with derivations on how to compute our solution efficiently. in section 3, we summarize datasets and experiments illustrating the performance of our proposed model. in section 4, we demonstrate anecdotally, the effectiveness of model on covid-19 tweets. finally, we present closely-related work in section 5 and conclusions in section 6. ## study of covid-19 tweets we present an analysis of tweets related to the ongoing covid-19 pandemic using ester to high- .09 .09 .09 .10 .08 .10 .08 .06 .10 .11 .01 .07 .10 .11 .06 .06 .03 .09 .09 .14 .04 .13 .13 .11 .06 .10 .08 .11 .12 .12 .04 .16 .09 .03 .04 .18 .07 .15 .03 .06 .14 .13 .10 .13 .11 .14 .13 .12 .15 .08 .06 .07 .08 .09 .09 .05 .15 .13 .15 .14 .07 .14 .14 .18 .16 .09 .09 .04 .04 .02 .12 .07 .15 .12 .04 .05 .07 .18 .07 .10 .11 .12 .13 .12 .16 .17 .12 .14 .16 .08 .00 .20 .14 .19 .15 .01 .17 .13 .08 .24 .04 .05 .16 .11 .08 .19 .15 .08 .08 .22 .06 .0 .13 .06 .20 .27 .21 .11 .13 .10 .26 .25 .23 .25 .21 .18 .19 .19 .24 .26 .70 .16 .32 .16 .18 .27 .20 .18 .33 .20 .85 .27 .28 .35 .23 .25 .19 .41 .24 .22 .31 .60 .27 .34 .22 .15 .14 .22 .13 .10 .23 .23 .28 .23 .29 .21 .22 .25 .31 .20 .09 .28 .09 .21 .20 .75 .26 .26 .18 .15 .22 .21 .19 .19 .25 .17 .31 .11 .13 .37 .24 .08 .24 .13 .25 .22 .37 .32 .30 .18 .59 .61 .59 .60 .59 .61 .64 .65 .56 .62 .90 .68 .44 .55 .62 .43 .62 .67 .59 .59 .51 .61 .60 .62 .56 .63 .53 .66 .71 .51 .72 .64 .63 .69 .66 .55 .37 .45 .61 .87 .03 .04 .03 .03 .01 .04 .05 .03 .03 .05 .00 .03 .51 .02 .01 .05 .06 .03 .12 .03 .01 .09 .03 .01 .15 .04 .05 .04 .03 .0 .0 .0 .03 .06 .04 .01 .0 .03 .01 .02 .51 .49 .50 .49 .50 .48 .50 .52 .41 .56 .2 .45 .27 .56 .59 .33 .42 .46 .42 .43 .21 .48 .41 .40 .48 .46 .54 .5 .62 .44 .48 .42 .43 .54 .46 .55 .75 .47 .61 .55 light its usefulness in uncovering macro-level emotion trends despite zero labeled data. we study a random sample of 17k english tweets from the last week of march collected by panacea labs 8 . this subset of tweets was analyzed using ester method with combined matrix and emolex lexicon. to obtain emotion labeling, we assign each tweet to 2 categories with the highest score. in the interest of space, we provide a summary of our findings in this section and provide further details in appendix b for the interested reader. in figure 2 we show top frequent hashtags. 9 each number in the matrix is the emotion frequency for the hashtag, i.e., the number of times the emotion is assigned to a tweet with the hashtag, divided by the total number of tweets with this hashtag in the dataset. we observe that, uniformly, sadness and trust are dominant emotions. general covid-19 hashtags are mostly related to sadness, then to trust (due to the large volume of mentions of authorities and facts) and fear. social tweets, which call to stay at home and embrace social distancing, also often have reassuring, comforting, and uplifting content, and thus are relatively often marked as joy. as expected, the majority of tweets with #pandemic tag get assigned to sadness and fear. notably, the tag #trump gets most often assigned to surprise and infrequently to trust (probably reflective of public opinion due to his changing stance on covid-19). tags #quarantine and #yemen once again expectedly show a high assignment rate to fear. #nhs stands for united kingdom national health service and is dominantly assigned to trust, sadness, and joy highlighting public emotion towards the struggles of the healthcare workers, as well as gratitude from the society. ## related work as part of affective computing, various research communities are studying emotion identification models via gestures and facial expressions @xcite , voice @xcite as well as user-generated text @xcite . in particular, text-based emotion detection and mood analysis has attracted significant research focus through task challenges @xcite due to the abundance of user-generated content on social media and microblogging platforms that captures public mood on various events in social, political, and economic spheres @xcite @xcite . emotion detection was studied in various settings including social media content @xcite , literature @xcite , tvshow transcripts (zahiri and choi, 2018) and conversations @xcite using supervised learning approaches. the best performing models are based on deep learning with labeled data and other knowledge resources such as lexicons and word embeddings @xcite @xcite . transfer learning and multi-task learning techniques were also studied for reducing labeled data requirements for supervision @xcite @xcite . previous studies include those on automatic and crowd-sourced building of lexicons @xcite @xcite as well as learning emotion-enhanced word embeddings @xcite @xcite . ## concluding remarks we proposed a random walk based model for unsupervised emotion detection in text using word associations from emotion lexicons and word cooccurrences from a general corpus. our solution efficiently computes emotion scores at a dataset level as well as provides a probabilistic interpretation of scores. we showed superior performance of our model over existing unsupervised baselines on several recent, real-world datasets. in future, we would like to study other graph-based scoring functions to further improve performance @xcite . in particular, we are interested in minimallysupervised representations that can apply to a range of related tasks that involve emotions such as sarcasm, stress and insult detection, abusive language classification, and personality recognition @xcite .