DOCSLIB.ORG

ECNU at Semeval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
ECNU at Semeval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models 1 1,2 1,2 Huimin Xu , Man Lan ∗ ,Yuanbin Wu 1Department of Computer Science and Technology, East China Normal University, Shanghai, P.R.China 2Shanghai Key Laboratory of Multidimensional Information Processing [email protected], mlan, ybwu @cs.ecnu.edu.cn { } Abstract Semeval 2018 Task 1 subtask 5 takes basic human emotion proposed by Ekman (Ekman, 1999) into In this paper we describe our systems submit- ted to Semeval 2018 Task 1 “Affect in Tweet” consideration, including Anger, Anticipation, Dis- (Mohammad et al., 2018). We participated in gust, Fear, Joy, Sadness, Surprise, and Trust. all subtasks of English tweets, including emo- The difference between these subtasks lies in tion intensity classification and quantification, the emotion granularity and classification or quan- valence intensity classification and quantifica- tification, so in our work, the similar method is tion. In our systems, we extracted four type- adopted for five subtasks. We extracted a rich set s of features, including linguistic, sentiment of elaborately designed features. In addition to lin- lexicon, emotion lexicon and domain-specific features, then fed them to different regressors, guistic features, sentiment lexicon features and e- finally combined the models to create an en- motion lexicon features, we also extracted some semble for the better performance. Officially domain specific features. Also, we conducted a released results showed that our system can be series of experiments on different machine learn- further extended. ing algorithms and ensemble methods to obtain the better performing for each subtask. For subask 5, 1 Introduction we adopted multiple binary classification and con- The Semeval 2018 Task 1 aims to automatically structed a model for each emotion. determine the intensity of emotions of the tweeters from their tweets, including five subtasks. That is, 2 System Description given a tweet and one of the four emotions (anger, fear, joy, sadness), the subtask 1 and 2 are to deter- We first performed data preprocessing, then ex- mine the intensity and classify the tweet into one tracted several types of features from tweets and of the four ordinal classes of intensity of the e- constructed supervised models for this task. motion respectively. Similarly, the subtask 3 and 4 determine the intensity and classify the tweet 2.1 Data Preprocessing into one of seven ordinal classes of intensity of Firstly, all words are converted to lower case, valance. Subtask 5 is a multi-label emotion clas- URLs are replaced by “url”, abbreviations, s- sification task which classifies the tweets as neu- langs and elongated words are transformed to tral or no emotion or as one, or more, of eleven their normal format. Then, emojis are replaced given emotions (anger, anticipation, disgust, fear, by corresponding emojis names by “Emoji Li- joy, love, optimism, pessimism, sadness, surprise, brary”1. Finally, we use Stanford CoreNLP tools trust) that best represent the mental state of the (Manning et al., 2014) for tokenization, POS tag- tweeter. For each task, training and test dataset- ging, named entity recognizing (NER) and pars- s are divided into English, Arabic, and Spanish ing. tweets. We participated in all subtasks of English tweets. 2.2 Feature Engineering Traditional sentiment classification is a coarse- We extracted a set of features to construct super- grained task in sentiment analysis which focuses vised models for five subtasks, that is linguistic on sentiment polarity classification of the whole sentence (i.e., positive, negative, neutral, mixed). 1https://github.com/fvancesco/emoji/ 231 Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 231–235 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics features, sentiment lexicon features, emotion lexi- Sentiment Lexicon9, and NRC Sentiment140 Lexi- con features and domain-specific features. con10. There is not a unified form among the eight- 2.2.1 Linguistic Features s lexicons. For example, Bing Liu lexicon use t- Lemma unigram Considering there is sim- wo values for each word to represent its sentiment • ilar emotion intensity expressed by “anger” scores which one for positive sentiment and the and “angers”, we choose word lemma uni- other for negative sentiment. In order to unify the gram features from tweets rather than word form, we transformed the two scores into a one- unigram features. dimensional value by subtracting negative emo- tion scores from positive emotion scores. Given Negation Negation in a sentence often affect- a tweet, we calculated the following six scores: • s its sentiment orientation, and conveys it- s intensity of the sentiment. For example, a – the ratio of positive words to all words. sentence with several negation words is more inclined to negative sentiment polarity. Fol- – the ratio of negative words to all words. lowing previous work (Zhang et al., 2015), – the maximum sentiment scores. we manually collected 29 negations2 and de- signed two binary features. One is to indicate – the minimum sentiment scores. whether there is any negation in the tweet and the other is to record whether this tweet con- – the sum of sentiment scores. tains more than one negation. – the sentiment score of the last word in tweet. NER Given a tweet “@JackHoward the • 2.2.3 Emotion Lexicon Features Christmas episode genuinely had me in tears Considering subtask 1, 2, 5 are related to e- of laughter”, it has useful information like motion intensity prediction, subtask 3, 4 are person name and festival which may con- related valence intensity prediction, three e- vey tweeter’s happiness. So we extracted 12 motion lexicons and one valence lexion are types of named entities (DURATION, SET, adopted. That is NRC Hashtag Sentimen- NUMBER, LOCATION, PERSON, ORGA- t Lexicon (Mohammad and Kiritchenko, 2015), NIZATION, PERCENT, MISC, ORDINAL, NRC Affect Intensity Lexicon (Mohammad, TIME, DATE, MONEY) from the sentence 2017), NRC Word-Emotion Association Lexicon and represented each type of named entity as (Bravo-Marquez et al., 2017) and ANEW-1999 a binary feature to check whether it appears Lexicon (Bradley and Lang, 1999). Given a tweet, in the sentence. we calculate three scores for each lexicon to con- struct emotion lexicon features: the maximum s- 2.2.2 Sentiment Lexicon Features cores, the sum of scores, the number of words ex- Many tasks related to sentiment or emotion anal- ist in lexicons. ysis depend upon affect, opinion, sentiment, sense and emotion lexicons. So we employ eight sen- 2.2.4 Domain-specific Features timent lexicons to capture the sentiment informa- Punctuation People often use exclamation • tion of the given sentence. The eight sentiment mark(!) and question mark(?) to express sur- lexicons are as follows: Bing Liu lexicon3, Gener- prise or emphasis. Therefore, we extract the al Inquirer lexicon4, IMDB5, MPQA6, NRC Emo- following 6 features: tion Sentiment Lexicon7, AFINN8, NRC Hashtag – whether the tweet contains an exclama- 2https://github.com/haierlord/resource tion mark. 3 http://www.cs.uic.edu/liub/FBS/sentiment- – whether the tweet contains more than analysis.html#lexicon 4http://www.wjh.harvard.edu/inquirer/homecat.htm one exclamation mark. 5http://www.aclweb.org/anthology/S13-2067 – whether the tweet has a question mark. 6http://mpqa.cs.pitt.edu/ 7http://www.saifmohammad.com/WebPages/lexicons.html 9http://www.umiacs.umd.edu/saif/WebDocs/NRC- 8http://www2.imm.dtu.dk/pubdb/views/publication Hashtag-Sentiment-Lexicon-v0.1.zip details.php?id=6010 10http://help.sentiment140.com/for-students/ 232 – whether the tweet contains more than Gradient Boosting Regressor (GBR) implement- one question mark. ed in scikit-learn tools13 and XGBoost Regressor 14 – whether the tweet contains both excla- (XGB) . All these algorithms are used with de- mation marks and question marks. fault parameters. – whether the last token of this tweet is an 3 Experiments exclamation or question mark. 3.1 Dataset Bag-of-Hashtags Hashtags reflect emotion • The statistics of the English datasets provided by orientation of tweets directly, so we con- Semeval 2018 Task 1 are shown in Table 1 and structed a vocabulary of hashtags appearing 2. How the English data created is described in in the training set and development set, then (Mohammad and Kiritchenko, 2018). adopted the bag-of-hashtags method for each tweet. Datasets anger fear joy sadness train 1,701 2,252 1,616 1,533 Emoticon We collected 67 emoticons from dev 388 689 290 397 • 11 subtask 1 17,939 17,923 18,042 17,912 Internet , including 34 positive emoticons test subtask 2 1,002 986 1,105 975 and 33 negative emoticons, then designed the following 4 binary features: Table 1: The statistics of data sets for subtask 1 and 2. – to record whether the positive and nega- tive emoticons are present in the tweet, Subtask train dev test respectively (1 for yes, 0 for no). 3 1,181 449 17,874 – to record whether the last token is a pos- 4 1,181 449 937 itive or a negative emoticon. 5 6,838 886 3,259 Intensity Words Some words appeared more Table 2: The statistics of data sets for subtask 3, 4, 5. • frequently in tweets with higher intensity, some words has higher score in emotion lex- 3.2 Evaluation Metric icons, these words may contain information that express strong emotion intensity. So we To evaluate the performance of different system- extracted this type words in two ways: s, the official evaluation measure Pearson Corre- lation Coefficient with the Gold ratings/labels is – Pick up words whose emotion score is adopted for the first four subtasks. The correlation greater than threshold from emotion lex- scores across all four emotions will be averaged icons. (macro-average) to determine the final system per- – Calculate the probability of each word formance.
Load more

Recommended publications
  • Bad Is Stronger Than Good
    Review of General Psychology Copyright 2001 by the Educational Publishing Foundation 2001. Vol. 5. No. 4. 323-370 1089-2680/O1/S5.O0 DOI: 10.1037//1089-2680.5.4.323 Bad Is Stronger Than Good Roy F. Baumeister and Ellen Bratslavsky Catrin Finkenauer Case Western Reserve University Free University of Amsterdam Kathleen D. Vohs Case Western Reserve University The greater power of bad events over good ones is found in everyday events, major life events (e.g., trauma), close relationship outcomes, social network patterns, interper- sonal interactions, and learning processes. Bad emotions, bad parents, and bad feedback have more impact than good ones, and bad information is processed more thoroughly than good. The self is more motivated to avoid bad self-definitions than to pursue good ones. Bad impressions and bad stereotypes are quicker to form and more resistant to disconfirmation than good ones. Various explanations such as diagnosticity and sa- lience help explain some findings, but the greater power of bad events is still found when such variables are controlled. Hardly any exceptions (indicating greater power of good) can be found. Taken together, these findings suggest that bad is stronger than good, as a general principle across a broad range of psychological phenomena. Centuries of literary efforts and religious pothesis that bad is stronger than good (see also thought have depicted human life in terms of a Rozin & Royzman, in press). That is, events struggle between good and bad forces. At the that are negatively valenced (e.g., losing metaphysical level, evil gods or devils are the money, being abandoned by friends, and receiv- opponents of the divine forces of creation and ing criticism) will have a greater impact on the harmony.
    [Show full text]
  • The Effects of an Emotion Strengthening Training Program On
    BALCI ÇELİK / Duyguları Güçlendirme Eğitimi Programı’nın Hemşirelerin ... • 793 Th e Eff ects of an Emotion Strengthening Training Program on the Optimism Level of Nurses Seher BALCI ÇELİK* Abstract Th e aim of this study is to investigate the eff ects of emotion strengthening as a training programme for optimistic for nurses. Th e experimental and control group of this rese- arch has totally composed of 20 nurses. A pre-test post-test research model with control group was been used in this research. Nurses’ optimistic levels have been measured by ‘Optimistic Scale’ which was developed by Balcı and Yılmaz (2002). In order to test that, meaningful diff erences between the scores of pre-test and post-test within both control and experimental group Mann Whitey U and Wilcoxon Signed Rank test were applied for statisical analysis. It was found that this emotional strengthening training had positive eff ects on levels of optimistic for nurses. Th e findings have been discussed in the light of literature, and some suggestions have been made. Key Words Emotion Strengthening Training Program, Nurse, Optimistic, Optimistic Scale. * Correspondence: Ondokuz Mayıs University, Faculty of Education, Department of Education Sciences, Kurupelit 55139 Samsun-Turkey / E-mail: [email protected] Kuram ve Uygulamada Eğitim Bilimleri / Educational Sciences: Th eory & Practice 8 (3) • September 2008 • 793-804 © 2008 Eğitim Danışmanlığı ve Araştırmaları İletişim Hizmetleri Tic. Ltd. Şti. 794 • EDUCATIONAL SCIENCES: THEORY & PRACTICE As internal mechanisms emotions are psychological signs of how an individual feels. Th ey develop in situations which call for an individual’s life style or behavioral pattern.
    [Show full text]
  • Using an Ambiguous Cue Paradigm to Assess Cognitive Bias in Gorillas (Gorilla Gorilla Gorilla) During a Forage Manipulation
    ABC 2017, 4(1):70-83 Animal Behavior and Cognition DOI: 10.12966/abc.06.02.2017 ©Attribution 3.0 Unported (CC BY 3.0) Using an Ambiguous Cue Paradigm to Assess Cognitive Bias in Gorillas (Gorilla gorilla gorilla) during a Forage Manipulation Molly C. McGuire1, Jennifer Vonk1*, Grace Fuller2, & Stephanie Allard2 1Oakland University, Department of Psychology, Rochester, MI, USA 2Detroit Zoological Society, Royal Oak, MI USA *Corresponding author (Email: [email protected]) Citation – McGuire, M. C., Vonk, J., Fuller, G., & Allard, S. (2017). Using an ambiguous cue paradigm to assess cognitive bias in gorillas (Gorilla gorilla gorilla) during a forage manipulation. Animal Behavior and Cognition, 4(1), 70–83. doi: 10.12966/abc.06.02.2017 Abstract - In nonhumans, ‘optimism’ is often defined as responding to an ambiguous item in the same manner as to items previously associated with reward (or lack of punishment), and “pessimism” is defined as responding to an ambiguous item in the same manner as to items previously associated with a lack of reward (or with punishment). We measured the degree of “optimism” and “pessimism” in three captive male western lowland gorillas (Gorilla gorilla gorilla) during four consecutive two-week periods in which the amount of available forage material (mulberry, Moraceae or alfalfa, Medicago sativa) was manipulated. We assessed cognitive bias using an ambiguous cue paradigm for the first time. Pairs of two-dimensional shapes were presented on a touch-screen computer in a forced choice task in which one shape was always reinforced (P), one was never reinforced (N), and one was reinforced half the time, making it ambiguous (A).
    [Show full text]
  • Research-Based Practice with Women Who Have Had Miscarriages
    CMcal Scholarship Research- based Practice with Women Who Have Had Miscarriages Kristen M. Swanson Purpose: To summarize a research-based description of what it is like to miscarry and to recommend an empirically tested theory of caring for women who have experienced miscarriage. Design: The research program included three phases: interpretive theory generation, descriptive survey and instrument development, and experimental testing of a theory-based intervention. Methods: Research methods included interpretive phenomenologE factor analysis, and ANCOVA. Findings: A theory of caring and a model of what it is like to miscarry were generated, refined, and tested. A case study shows one woman’s response to miscarrying and illustrates clinical application of the caring theory. Conclusions: The Miscarriage Model is a useful framework for anticipating the variety of responses women have to miscarrying. The caring theory is an effective and sensitive guide to clinical practice with women who miscarry. IMAGE:JOURNAL OF NURSINGSCHOLARSHIP, 1999; 31 :4,339-345.01999 SIGMA THETATAU INTERNATIONAL. [Key words: caring, miscarriage, theory construction, counseling] t least one in five pregnancies ends in miscarriage-the program of inquiry about miscarriage and its aftermath. An unplanned, unexpected ending of pregnancy before the important contribution of the pilot study was the conclusion that time of expected fetal viability (Hall, Beresford, & a woman’s feelings about miscarriage could be understood only Quinones, 1987). Women’s responses range from relief in the context of what being pregnant and having a miscarriage to devastation with much variability in the time required meant to her. For example, if being pregnant was perceived as a Ato achieve resolution.
    [Show full text]
  • The Influence of Physical Activity, Anxiety, Resilience And
    International Journal of Environmental Research and Public Health Article The Influence of Physical Activity, Anxiety, Resilience and Engagement on the Optimism of Older Adults Alfonso Martínez-Moreno * , Ricardo José Ibáñez-Pérez , Francisco Cavas-García F and Francisco Cano-Noguera Department of Physical Activity and Sports, University of Murcia, 30720 Santiago de la Ribera-San Javier, Spain; [email protected] (R.J.I.-P.); [email protected] (F.C.-G.F.); [email protected] (F.C.-N.) * Correspondence: [email protected] Received: 14 October 2020; Accepted: 5 November 2020; Published: 9 November 2020 Abstract: The purpose of this study was to learn how physical activity, anxiety, resilience and engagement can influence optimism in older adults. An observational, quantitative, descriptive and transversal design was used with non-probabilistic sampling. A descriptive statistical analysis of the sample, Cronbach’s alpha test of internal consistency and linear correlation using Pearson’s correlation coefficient (r) were performed. In addition, a t-Student test, analysis of variance (ANOVA), Kolmogorov–Smirnov test of normality and Levene test of homogeneity, as well as a multivariate linear regression model, were conducted. Participants who had not engaged in physical activity showed an increased total anxiety and significantly greater decrease in concentration compared to those who had engaged in physical activity. The Revised Life Orientation Test (LOT-R), Utrecht Work Engagement Scale (UWES) and resilience of participants who had not engaged in physical activity were significantly lower than those of the participants who had engaged in physical activity. Those with a partner showed significantly lower decreases in concentration compared to single women.
    [Show full text]
  • Optimism As a Mediating Factor in the Relationship Between Anxiety and News Media Exposure in College Students
    Optimism as a Mediating Factor in the Relationship between Anxiety and News Media Exposure in College Students BY Danielle Hoyt ADVISOR • Joseph Trunzo _________________________________________________________________________________________ Submitted in partial fulfillment of the requirements for graduation with honors in the Bryant University Honors Program December 2016 Table of Contents Abstract .................................................................................................................................1 Introduction ...........................................................................................................................2 Violent Imagery and Childhood Aggression.......................................................................2 Violent Imagery and Negative Psychological Outcomes ....................................................3 Terrorist Attacks of September 11th, 2001 ..........................................................................4 Effect of News as a Medium ..............................................................................................5 Supporting Factors and Mitigating Factors .........................................................................5 Population Considerations .................................................................................................8 Optimism as a Protecting Trait/Mediating Factor ............................................................. 10 Conclusion .....................................................................................................................
    [Show full text]
  • Ambiguous Loss: a Phenomenological
    CORE Metadata, citation and similar papers at core.ac.uk Provided by SHAREOK repository AMBIGUOUS LOSS: A PHENOMENOLOGICAL EXPLORATION OF WOMEN SEEKING SUPPORT FOLLOWING MISCARRIAGE By KATHLEEN MCGEE Bachelor of Science in Human Development and Family Science Oklahoma State University Stillwater, Oklahoma 2011 Submitted to the Faculty of the Graduate College of the Oklahoma State University in partial fulfillment of the requirements for the Degree of MASTER OF SCIENCE May, 2014 AMBIGIOUS LOSS: A PHENOMENOLOGICAL EXPLORATION OF WOMEN SEEKING SUPPORT FOLLOWING MISCARRIAGE Thesis Approved: Dr. Kami Gallus Dr. Amanda Harrist Dr. Karina Shreffler ii Name: KATHLEEN MCGEE Date of Degree: MAY, 2014 Title of Study: AMBIGUOUS LOSS: A PHENOMENOLOGICAL EXPLORATION OF WOMEN SEEKING SUPPORT FOLLOWING MISCARRIAGE Major Field: HUMAN DEVELOPMENT AND FAMILY SCIENCE Abstract: Miscarriage is a fairly common experience that is overlooked by today’s society. Miscarriage as simply a female medical issue does not embody the full emotional toll of the experience. Research is lacking on miscarriage and the couple relationship. Even further, a framework for understanding miscarriage is nonexistent. This study aims to explore the phenomenon of miscarriage as well as provide a framework for understanding miscarriage. The current study will look at miscarriage through the lenses of ambiguous loss theory and trauma theory. The sample consisted of 10 females, five who interviewed as individuals and 5 who interviewed with their partners as a couple. Semi-structured, open-ended interviews were conducted with each individual participant, and the five couples were interviewed as a couple as well. From the data, six themes and four subthemes emerged for the female experience: Emotional toll, Stolen dreams, No one understands, He loves me in a different way, Why? I don’t understand, and In the end, I have my faith.
    [Show full text]
  • The Tyranny of Optimism
    The Tyranny of Optimism C. W. Von Bergen Southeastern Oklahoma State University [email protected] Diane Bandow Troy University [email protected] ABSTRACT America culture emphasizes upbeat thinking, cheerfulness, optimism, and other manifestations of positive affect in its aphorisms, songs, religion, books and magazines, medicine, business, and psychology. This has led to the often unchallenged idea that positive thinking is always a good thing. This belief has caused the power that negative thinking and affect hold, in terms of realistic appraisals of the self and the world, to be not only underestimated, but often shunned as well. It is dangerous, however, to lose sight of unpleasant realities. The downfall of losing sight of realities is illustrated with examples. Costs and benefits are reviewed as they relate to positive and negative thinking and positive and negative affect. This paper presents a curvilinear, inverted U-shaped model that suggests that an optimal range of affect is most adaptive and that extremes in either positive or negative affect are less beneficial. “Almost everyone is overconfident—except the people who are depressed, and they tend to be realists.” [32, p. 149] The American way of life is replete with appeals to be encouraging, cheerful, and upbeat. We are told that looking on the bright side is good for us because positive thoughts realize themselves in the form of good health, prosperity, and success [27]. The common view that people should always feel positive about themselves and to think positive has created the perception that negative thinking should be shunned by society. This rejection is dangerous since negative thinking promotes truthful and realistic appraisals: “Truth matters to people, even if it is at the expense of feelings of well-being, self-satisfaction and social adjustment” [69, p.
    [Show full text]
  • Loneliness and Expressive Suppression; the Role of Pessimism About Expressivity Pooya Razavi, Seung Hee Yoo San Francisco State University
    Loneliness and Expressive Suppression; The Role of Pessimism about Expressivity Pooya Razavi, Seung Hee Yoo San Francisco State University Overview Study 2 Study 3 Loneliness triggers a state of hypervigilance to social threats (Hawkley & In this study, the findings of the first study were replicated and extended in In this study, we tested the model with an experimental design. In a between- Cacioppo, 2010). As a result, lonely individuals tend to form pessimistic views three ways: subjects design, the participants were randomly assigned to think and write about a of the social world, and, accordingly, choose prevention-focused interaction (a) Vignettes were used to assess the participants’ suppression of emotions time when they felt (a) isolated, (b) socially connected, or (c) sad. Afterwards, strategies in order to minimize the possibility of negative social evaluations and attitudes towards expressivity in a specific scenario. similar to Study 2’s procedure, they imagined an interaction and reported their (Lucas et al., 2010). Ironically, these cautious strategies may harm lonely expressive suppression, and their attitudes towards emotional expressivity based on individuals’ social interactions (Pilkonis, 1977) and lead to a vicious circle of (b) In addition to negative attitudes towards expressivity (prevention the imagined interaction. loneliness. focused), positive attitudes (promotion focused) were measured. In the present research, we extended this theory to examine the relationship (c) The model was tested while controlling for two personality traits, Participants: Recruited through M-Turk; N = 220; 57.3% female; Mage = 36.70 between loneliness and expressive suppression of emotions. Specifically, we extraversion and neuroticism, which are often associated with suppression predicted that: of emotions.
    [Show full text]
  • Cosmic Pessimism
    pharmakon V Eugene Thacker COSMIC PESSIMISM Original Artwork by Keith Tilford Eugene Thacker Cosmic Pessimism by Eugene Thacker First Edition Minneapolis © 2015, Univocal Publishing Published by Univocal 123 North 3rd Street, #202 Minneapolis, MN 55401 Artwork: acrylic on duralar. COSMIC PESSIMISM All images copyright Keith Tilford. Designed & Printed by Jason Wagner Distributed by the University of Minnesota Press ISBN 9781937561864 Library of Congress Control Number 2015940595 Original Artwork by Keith Tilford ~ * ~ There is no philosophy of pessimism, only the reverse. ~ * ~ Cosmic Pessimism Cosmic Pessimism e’re Doomed. Pessimism is the night- side of thought, a melodrama of the futility of the brain, a poetry written in the graveyard of philosophy. WPessimism is a lyrical failure of philosophical think- ing, each attempt at clear and coherent thought, sullen and submerged in the hidden joy of its own futility. The closest pessimism comes to philo- sophical argument is the droll and laconic “We’ll never make it,” or simply: “We’re doomed.” Every effort doomed to failure, every project doomed to incompletion, every life doomed to be unlived, every thought doomed to be unthought. Pessimism is the lowest form of philosophy, frequently disparaged and dismissed, merely the symptom of a bad attitude. No one ever needs pessimism, in the way that one needs optimism to inspire one to great heights and to pick oneself up, in the way one needs constructive criticism, 3 advice and feedback, inspirational books or a pat on the back. No one needs pessimism (though I like to imagine the idea of pessimist self-help). No one needs pessimism, and yet everyone — without exception — has, at some point in their lives, had to confront pessimism, if not as a philosophy then as a grievance — against one’s self or others, against one’s surroundings or one’s life, against the state of things or the world in general.
    [Show full text]
  • Trends, Sentiments and Emotions
    Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions Xiaoya Li♣, Mingxin Zhou♣, Jiawei Wu♣, Arianna Yuan, Fei Wu♠ and Jiwei Li♣ ♠ Department of Computer Science and Technology, Zhejiang University Computer Science Department, Stanford University ♣ Shannon.AI {xiaoya_li, mingxin_zhou, jiawei_wu, jiwei_li}@shannonai.com [email protected], [email protected] Abstract—At the time of writing, the ongoing pandemic of coron- People constantly post about the pandemic on social media avirus disease (COVID-19) has caused severe impacts on society, such as Twitter, Weibo and Facebook. They express their atti- economy and people’s daily lives. People constantly express their tudes and feelings regarding various aspects of the pandemic, opinions on various aspects of the pandemic on social media, making user-generated content an important source for understanding public such as the medical treatments, public policy, their worry, etc. emotions and concerns. Therefore, user-generated content on social media provides an important source for understanding public emotions and In this paper, we perform a comprehensive analysis on the affective trajectories of the American people and the Chinese people based on concerns. Twitter and Weibo posts between January 20th, 2020 and May 11th In this paper, we provide a comprehensive analysis on the 2020. Specifically, by identifying people’s sentiments, emotions (i.e., anger, disgust, fear, happiness, sadness, surprise) and the emotional affective trajectories of American people and Chinese people triggers (e.g., what a user is angry/sad about) we are able to depict the based on Twitter and Weibo posts between January 20th, dynamics of public affect in the time of COVID-19.
    [Show full text]
  • The Experience of Men After Miscarriage Stephanie Dianne Rose Purdue University
    Purdue University Purdue e-Pubs Open Access Dissertations Theses and Dissertations January 2015 The Experience of Men After Miscarriage Stephanie Dianne Rose Purdue University Follow this and additional works at: https://docs.lib.purdue.edu/open_access_dissertations Recommended Citation Rose, Stephanie Dianne, "The Experience of Men After Miscarriage" (2015). Open Access Dissertations. 1426. https://docs.lib.purdue.edu/open_access_dissertations/1426 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. THE EXPERIENCE OF MEN AFTER MISCARRIAGE A Dissertation Submitted to the Faculty of Purdue University by Stephanie Dianne Rose In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy December 2015 Purdue University West Lafayette, Indiana ii To my curious, sweet, spunky, intelligent, and fun-loving daughter Amira, and to my unborn baby (lost to miscarriage February 2010), whom I never had the privilege of meeting. I am extremely happy and fulfilled being your mother. Thank you for your motivation and inspiration. iii ACKNOWLEDGEMENTS I am grateful to everyone who contributed to my study. Specifically, I am indebted to my sisters Sara Okello and Stacia Firebaugh for their helpful revisions, and to my parents Scott and Susan Firebaugh for their emotional and financial support along the way. I am thankful to those who provided childcare during this project, including my family and friends. My wonderful family and friends have blessed me with much support and encouragement throughout this project. I am also very grateful to my advisor Dr. Heather Servaty-Seib for her tireless support and investment in this project.
    [Show full text]