An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs

The effect of amplifiers, downtoners, and negations has been studied in general and particularly in the context of sentiment analysis. However, there is only limited work which aims at transferring the results and methods to discrete classes of emotions, e. g., joy, anger, fear, sadness, surprise, and disgust. For instance, it is not straight-forward to interpret which emotion the phrase "not happy" expresses. With this paper, we aim at obtaining a better understanding of such modifiers in the context of emotion-bearing words and their impact on document-level emotion classification, namely, microposts on Twitter. We select an appropriate scope detection method for modifiers of emotion words, incorporate it in a document-level emotion classification model as additional bag of words and show that this approach improves the performance of emotion classification. In addition, we build a term weighting approach based on the different modifiers into a lexical model for the analysis of the semantics of modifiers and their impact on emotion meaning. We show that amplifiers separate emotions expressed with an emotion- bearing word more clearly from other secondary connotations. Downtoners have the opposite effect. In addition, we discuss the meaning of negations of emotion-bearing words. For instance we show empirically that "not happy" is closer to sadness than to anger and that fear-expressing words in the scope of downtoners often express surprise.Comment: Accepted for publication at The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA), https://dsaa2018.isi.it

A Knowledge-Based Model for Polarity Shifters

[EN] Polarity shifting can be considered one of the most challenging problems in the context of Sentiment Analysis. Polarity shifters, also known as contextual valence shifters (Polanyi and Zaenen 2004), are treated as linguistic contextual items that can increase, reduce or neutralise the prior polarity of a word called focus included in an opinion. The automatic detection of such items enhances the performance and accuracy of computational systems for opinion mining, but this challenge remains open, mainly for languages other than English. From a symbolic approach, we aim to advance in the automatic processing of the polarity shifters that affect the opinions expressed on tweets, both in English and Spanish. To this end, we describe a novel knowledge-based model to deal with three dimensions of contextual shifters: negation, quantification, and modality (or irrealis).This work is part of the project grant PID2020-112827GB-I00, funded by MCIN/AEI/10.13039/501100011033, and the SMARTLAGOON project [101017861], funded by Horizon 2020 - European Union Framework Programme for Research and Innovation.Blázquez-López, Y. (2022). A Knowledge-Based Model for Polarity Shifters. Journal of Computer-Assisted Linguistic Research. 6:87-107. https://doi.org/10.4995/jclr.2022.1880787107

Fokozás szkizofréniában

Dolgozatunkban szkizofrén betegek nyelvhasználati sajátságait vizsgáljuk a nyelvi fokozásra fókuszálva. Mivel a különböző mentális betegségek hatással vannak az egyén kommunikációjára, a nyelvi produktumaik (akár írott, akár beszélt formájú) elemzése fontos közvetett eszköze lehet a betegségek vizsgálatának és esetleges diagnosztizálásának. A jelen kutatásban a nyelvi sajátságok közül a fokozást tettük a fókuszba. Azt elemezzük, hogy a fokozás milyen kvantitatív és kvalitatív sajátságokkal jelenik meg a vizsgált betegek kommunikációjában. Korábbi érzelemkutatások alapján a szkizofréniával élőknél nehézség mutatkozik az érzelmi események megérzése, előrejelzése, az érzelmi benyomások és a kontextus integrálása, valamint az érzelmi élmények gazdagsága és fenntartása terén (Kring és mtsai, 2013). A háttérben álló agyi aktivitások a kognitív kontrollért felelős hálózatok működésében mutattak deficitet az érzelmek és a kogníció integrálásának elégtelenségét jelezve (Kring és mtsai, 2013). A mentális betegségek és az érzelemszabályozás ezen összefüggései okán, a fokozó elemek csoportján belül külön figyelmet fordítunk az ún. negatív emotív fokozókra, amelyek prior szemantikai tartalma valamely negatív érzelemhez kapcsolódik, azonban fokozó elemekként funkcionálhatnak a nyelvben. A kutatás végső kérdése, hogy mutatkozik-e olyan eltérés a fokozók használatában a vizsgált betegek és a kontrollcsoport között, amely prediktív sajátságként felhasználható. Vizsgálatunkhoz beszélt nyelvi szövegeket használunk, amelyek három különböző narratív feladatban keletkeztek. Eredményeink megmutatják, hogy bár a nyelvi fokozás tekintetében a két csoport hasonlóan viselkedik, a negatív emotív fokozók használata jelentősen eltérő: a betegcsoport alig használ negatív emotív fokozókat a kontrollcsoport tagjaihoz képest, illetve rendre negatív kontextusban

Nagyot mondó képviselők? : fokozás a politikai kommunikációban

A politikai kommunikációban megjelenő érzelemkifejezés kutatása az utóbbi évtizedekben egyre nagyobb hangsúlyt kap. Dolgozatunkban azt vizsgáljuk, hogy a nyelvi fokozás milyen kvantitatív és kvalitatív sajátságokkal jelenik meg a politikai kommunikációban. A fokozó elemek csoportján belül külön figyelmet fordítunk az ún. negatív emotív fokozókra, amelyek prior szemantikai tartalma valamely negatív érzelemhez kapcsolódik, azonban fokozó elemekként funkcionálhatnak. Ezzel összefüggésben a szövegek szentimentjét is elemezzük: azt vizsgáljuk, hogy mely szentimentértékű szavak jelentését intenzifikálják a politikusok a fokozó szavak segítségével. Mindezek segítségével megmutatjuk, hogy milyen kvalitatív és kvantitatív sajátságok jellemzik a nyelvi fokozás tekintetében a kormánypárti és az ellenzéki kommunikációt, valamint, hogy hogyan befolyásolja mindezt a Covid19-járvány

Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach

Background: Patient activation is defined as a patient’s confidence and perceived ability to manage their own health. Patient activation has been a consistent predictor of long-term health and care costs, particularly for people with multiple long-term health conditions. However, there is currently no means of measuring patient activation from what is said in health care consultations. This may be particularly important for psychological therapy because most current methods for evaluating therapy content cannot be used routinely due to time and cost restraints. Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. Objective: This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. Methods: With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. Results: The k-nearest neighbors classifier obtained 73% accuracy (82% precision and 80% recall) in a test data set. The bagged trees classifier obtained 81% accuracy for test data (87% precision and 75% recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. Conclusions: Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions

Discursive Features of Nigerian Online Ponzi Schemes’ Narratives

Although Ponzi schemes have existed since the 1800s, contemporary financial challenges have rejuvenated them while the Internet has enhanced their proliferation, particularly in developing countries. The present study analyses select discursive features for digital deception in Nigerian online Ponzi schemes. We identify the use of stance and linguistic engagement, formulaic expressions and politeness strategies, narrativity, naming, and lexical range as techniques used by scheme creators. These linguistic and discursive choices are wielded as tools to attract customers and, ultimately, to deceive. The overt propagation of financial gains has underlying ideological implications, as it projects a sense of communality and encourages financial leverage which are in turn exploited to con unsuspecting – often greedy – subscribers. We conclude that language use in Ponzi schemes is intentionally crafted to appeal to diverse individual sentiments, particularly within developing economies where poverty is widespread and people seek to make money through any means in order to survive

Discursive Features of Nigerian Online Ponzi Schemes’ Narratives

Although Ponzi schemes have existed since the 1800s, contemporary financial challenges have rejuvenated them while the Internet has enhanced their proliferation, particularly in developing countries. The present study analyses select discursive features for digital deception in Nigerian online Ponzi schemes. We identify the use of stance and linguistic engagement, formulaic expressions and politeness strategies, narrativity, naming, and lexical range as techniques used by scheme creators. These linguistic and discursive choices are wielded as tools to attract customers and, ultimately, to deceive. The overt propagation of financial gains has underlying ideological implications, as it projects a sense of communality and encourages financial leverage which are in turn exploited to con unsuspecting – often greedy – subscribers. We conclude that language use in Ponzi schemes is intentionally crafted to appeal to diverse individual sentiments, particularly within developing economies where poverty is widespread and people seek to make money through any means in order to survive

Compositional language processing for multilingual sentiment analysis

Programa Oficial de Doutoramento en Computación. 5009V01[Abstract] This dissertation presents new approaches in the field of sentiment analysis and polarity classification, oriented towards obtaining the sentiment of a phrase, sentence or document from a natural language processing point of view. It makes a special emphasis on methods to handle semantic composionality, i. e. the ability to compound the sentiment of multiword phrases, where the global sentiment might be different or even opposite to the one coming from each of their their individual components; and the application of these methods to multilingual scenarios. On the one hand, we introduce knowledge-based approaches to calculate the semantic orientation at the sentence level, that can handle different phenomena for the purpose at hand (e. g. negation, intensification or adversative subordinate clauses). On the other hand, we describe how to build machine learning models to perform polarity classification from a different perspective, combining linguistic (lexical, syntactic and semantic) knowledge, with an emphasis in noisy and micro-texts. Experiments on standard corpora and international evaluation campaigns show the competitiveness of the methods here proposed, in monolingual, multilingual and code-switching scenarios. The contributions presented in the thesis have potential applications in the era of the Web 2.0 and social media, such as being able to determine what is the view of society about products, celebrities or events, identify their strengths and weaknesses or monitor how these opinions evolve over time. We also show how some of the proposed models can be useful for other data analysis tasks.[Resumen] Esta tesis presenta nuevas técnicas en el ámbito del análisis del sentimiento y la clasificación de polaridad, centradas en obtener el sentimiento de una frase, oración o documento siguiendo enfoques basados en procesamiento del lenguaje natural. En concreto, nos centramos en desarrollar métodos capaces de manejar la semántica composicional, es decir, con la capacidad de componer el sentimiento de oraciones donde la polaridad global puede ser distinta, o incluso opuesta, de la que se obtendría individualmente para cada uno de sus términos; y cómo dichos métodos pueden ser aplicados en entornos multilingües. En la primera parte de este trabajo, introducimos aproximaciones basadas en conocimiento para calcular la orientación semántica a nivel de oración, teniendo en cuenta construcciones lingüísticas relevantes en el ámbito que nos ocupa (por ejemplo, la negación, intensificación, o las oraciones subordinadas adversativas). En la segunda parte, describimos cómo construir clasificadores de polaridad basados en aprendizaje automático que combinan información léxica, sintáctica y semántica; centrándonos en su aplicación sobre textos cortos y de pobre calidad gramatical. Los experimentos realizados sobre colecciones estándar y competiciones de evaluación internacionales muestran la efectividad de los métodos aquí propuestos en entornos monolingües, multilingües y de code-switching. Las contribuciones presentadas en esta tesis tienen diversas aplicaciones en la era de la Web 2.0 y las redes sociales, como determinar la opinión que la sociedad tiene sobre un producto, celebridad o evento; identificar sus puntos fuertes y débiles o monitorizar cómo estas opiniones evolucionan a lo largo del tiempo. Por último, también mostramos cómo algunos de los modelos propuestos pueden ser útiles para otras tareas de análisis de datos.[Resumo] Esta tese presenta novas técnicas no ámbito da análise do sentimento e da clasificación da polaridade, orientadas a obter o sentimento dunha frase, oración ou documento seguindo aproximacións baseadas no procesamento da linguaxe natural. En particular, centrámosnos en métodos capaces de manexar a semántica composicional: métodos coa habilidade para compor o sentimento de oracións onde o sentimento global pode ser distinto, ou incluso oposto, do que se obtería individualmente para cada un dos seus términos; e como ditos métodos poden ser aplicados en entornos multilingües. Na primeira parte da tese, introducimos aproximacións baseadas en coñecemento; para calcular a orientación semántica a nivel de oración, tendo en conta construccións lingüísticas importantes no ámbito que nos ocupa (por exemplo, a negación, a intensificación ou as oracións subordinadas adversativas). Na segunda parte, describimos como podemos construir clasificadores de polaridade baseados en aprendizaxe automática e que combinan información léxica, sintáctica e semántica, centrándonos en textos curtos e de pobre calidade gramatical. Os experimentos levados a cabo sobre coleccións estándar e competicións de avaliación internacionais mostran a efectividade dos métodos aquí propostos, en entornos monolingües, multilingües e de code-switching. As contribucións presentadas nesta tese teñen diversas aplicacións na era da Web 2.0 e das redes sociais, como determinar a opinión que a sociedade ten sobre un produto, celebridade ou evento; identificar os seus puntos fortes e febles ou monitorizar como esas opinións evolucionan o largo do tempo. Como punto final, tamén amosamos como algúns dos modelos aquí propostos poden ser útiles para outras tarefas de análise de datos