IRS, information services and LIS research - a reminder about affect and the affective paradigm… and a question

PURPOSE – A previous contribution (Fourie, 2013) argues in favour of a balance in emphasis between information communication technology (ICT); information retrieval systems (IRS) such as databases, library catalogues, repositories, Google Scholar, digital libraries, portals, search engines; and, the users of these systems. This contribution pursues the need to consider affect and an affective paradigm more prominently in the design, evaluation, promotion and use of IRS and library and information services (LIS). DESIGN / METHODOLOGY / APPROACH – The contribution is written against the background of research in information behaviour, user studies, systems design, and information literacy. FINDINGS – Although the literature from LIS and other disciplines notes an affective paradigm or even paradigms, it is not strongly positioned compared with the systems and cognitive paradigms. A growing body of research and work practices such as information representation and tagging, and information skills training, is taking a slant towards affect and emotion. The question, however, is whether current work is sufficient to argue for an affective paradigm complimentary to the systems, cognitive and socio-cognitive paradigms, and how an affective paradigm should be introduced in training/education for LIS. ORIGINALITY / VALUE – Although there are a number of publications on affect and emotion, and references to an affective paradigm, this contribution is aimed at stimulating thought on whether we should prominently introduce the affective paradigm into LIS curricula as preparation for adding more value to IRS, library services, and in dealing with emotion-laden jobs, and if so, how.http://www.emeraldinsight.com/journals.htm?issn=0737-8831hb201

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

Differentiating users by language and location estimation in sentiment analisys of informal text during major public events

In recent years there has been intense work on the analysis of social media to support marketing campaigns. A proper methodology for sentiment analysis is a crucial asset in this regard. However, when monitoring major public events the behaviour or social media users may be strongly biased by punctual actions of the participating characters and the sense of group belonging, which is typically linked to specific geographical areas. In this paper, we present a solution combining a location prediction methodology with an unsupervised technique for sentiment analysis to assess automatically the engagement of social network users in different countries during an event with worldwide impact. As far as the authors know, this is the first time such techniques are jointly considered. We demonstrate that the technique is coherent with the intrinsic disposition of individual users to typical actions of the characters participating in the events, as well as with the sense of group belonging.Ministerio de Economía, Industria y Competitividad | Ref. TEC2016-76465-C2-2-RXunta de Galicia | Ref. GRC2014/046Xunta de Galicia | Ref. ED341D R2016/01

Sentiment Analysis: An Overview from Linguistics

Sentiment analysis is a growing field at the intersection of linguistics and computer science, which attempts to automatically determine the sentiment, or positive/negative opinion, contained in text. Sentiment can be characterized as positive or negative evaluation expressed through language. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative towards the item being reviewed. Sentiment analysis is now a common tool in the repertoire of social media analysis carried out by companies, marketers and political analysts. Research on sentiment analysis extracts information from positive and negative words in text, from the context of those words, and the linguistic structure of the text. This brief survey examines in particular the contributions that linguistic knowledge can make to the problem of automatically determining sentiment

Dystemo: Distant Supervision Method for Multi-Category Emotion Recognition in Tweets

Emotion recognition in text has become an important research objective. It involves building classifiers capable of detecting human emotions for a specific application, for example, analyzing reactions to product launches, monitoring emotions at sports events, or discerning opinions in political debates. Most successful approaches rely heavily on costly manual annotation. To alleviate this burden, we propose a distant supervision method-Dystemo-for automatically producing emotion classifiers from tweets labeled using existing or easy-to-produce emotion lexicons. The goal is to obtain emotion classifiers that work more accurately for specific applications than available emotion lexicons. The success of this method depends mainly on a novel classifier-Balanced Weighted Voting (BWV)-designed to overcome the imbalance in emotion distribution in the initial dataset, and on novel heuristics for detecting neutral tweets. We demonstrate how Dystemo works using Twitter data about sports events, a fine-grained 20-category emotion model, and three different initial emotion lexicons. Through a series of carefully designed experiments, we confirm that Dystemo is effective both in extending initial emotion lexicons of small coverage to find correctly more emotional tweets and in correcting emotion lexicons of low accuracy to perform more accurately

Intenzifikace záporu v mluvené britské angličtině

Tato bakalářská práce zkoumá prostředky intenzifikace záporu v britské mluvené angličtině. Jelikož intenzifikace je obecně spojovaná s příslovečným určením, konkrétněji s adverbii míry, je dalším cílem práce prokázat, že existují i jiné než lexikální prostředky intenzifikace. Protože je práce zaměřena na mluvený jazyk, přirozený, neplánovaný, improvizovaný, jsou zahrnuty i konstrukce, které jsou ve standardní angličtině považovány za mluvnicky nesprávné, ale vyskytují se běžně v hovorovém jazyce. Materiál pro analýzu byl proto čerpán z neformálních dialogů obsažených v Britském národním korpusu. Zápor a intenzifikace a jejich vzájemný vztah či interakce jsou popsány podle poznatků Duškové a kol. (1988) a Bibera a kol. (1999). Práce usiluje o návrh vhodné klasifikace prostředků intenzifikace záporu získané korpusovou studií založené na poznatcích Palacios- Martíneze (1996). klíčová slova: intenzifikace, intenzifikátor, zápor, mluvený jazyk, hovorový jazykThe BA thesis examines means of intensification of negation in British spoken English. Since intensification is generally associated with adverbials, more precisely with adverbs of degree, another aim of the study is to prove there are other than lexical means of intensification. For it is focused on the spoken language, which is natural, unplanned, improvised, it includes constructions that are regarded as ungrammatical in Standard English, but occur widely in the material used, namely the demographically sampled sub-corpus of the British National Corpus. Drawing on Dušková et al. (1988) and Biber et al. (1999) the thesis defines negation and intensification, and their mutual interaction and relation. Based on the findings of Palacios- Martínez (1996), the thesis aims at suggesting a suitable classification of the means of negative intensification provided by the corpus-based study. keywords: intensification, intensifier, negation, speech, colloquial languageDepartment of the English Language and ELT MethodologyÚstav anglického jazyka a didaktikyFilozofická fakultaFaculty of Art

深層学習に基づく感情会話分析に関する研究

Owning the capability to express specific emotions by a chatbot during a conversation is one of the key parts of artificial intelligence, which has an intuitive and quantifiable impact on the improvement of chatbot’s usability and user satisfaction. Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. Recently, many studies on neural emotional conversational models have been conducted. However, enabling the chatbot to control what kind of emotion to respond to upon its own characters in conversation is still underexplored. At this stage, people are no longer satisfied with using a dialogue system to solve specific tasks, and are more eager to achieve spiritual communication. In the chat process, if the robot can perceive the user's emotions and can accurately process them, it can greatly enrich the content of the dialogue and make the user empathize. In the process of emotional dialogue, our ultimate goal is to make the machine understand human emotions and give matching responses. Based on these two points, this thesis explores and in-depth emotion recognition in conversation task and emotional dialogue generation task. In the past few years, although considerable progress has been made in emotional research in dialogue, there are still some difficulties and challenges due to the complex nature of human emotions. The key contributions in this thesis are summarized as below: (1) Researchers have paid more attention to enhancing natural language models with knowledge graphs these days, since knowledge graph has gained a lot of systematic knowledge. A large number of studies had shown that the introduction of external commonsense knowledge is very helpful to improve the characteristic information. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. In this work, we employ an external knowledge graph ATOMIC to extract the knowledge sources. We proposed KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. The conversation is a sequence of coherent and orderly discourses. For neural networks, the capture of long-range context information is a weakness. We adopt Transformer a structure composed of self-attention and feed forward neural network, instead of the traditional RNN model, aiming at capturing remote context information. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets. (2) We proposed an emotional dialogue model based on Seq2Seq, which is improved from three aspects: model input, encoder structure, and decoder structure, so that the model can generate responses with rich emotions, diversity, and context. In terms of model input, emotional information and location information are added based on word vectors. In terms of the encoder, the proposed model first encodes the current input and sentence sentiment to generate a semantic vector, and additionally encodes the context and sentence sentiment to generate a context vector, adding contextual information while ensuring the independence of the current input. On the decoder side, attention is used to calculate the weights of the two semantic vectors separately and then decode, to fully integrate the local emotional semantic information and the global emotional semantic information. We used seven objective evaluation indicators to evaluate the model's generation results, context similarity, response diversity, and emotional response. Experimental results show that the model can generate diverse responses with rich sentiment, contextual associations

Genre and Domain Dependencies in Sentiment Analysis

Genre and domain influence an author\''s style of writing and therefore a text\''s characteristics. Natural language processing is prone to such variations in textual characteristics: it is said to be genre and domain dependent. This thesis investigates genre and domain dependencies in sentiment analysis. Its goal is to support the development of robust sentiment analysis approaches that work well and in a predictable manner under different conditions, i.e. for different genres and domains. Initially, we show that a prototypical approach to sentiment analysis -- viz. a supervised machine learning model based on word n-gram features -- performs differently on gold standards that originate from differing genres and domains, but performs similarly on gold standards that originate from resembling genres and domains. We show that these gold standards differ in certain textual characteristics, viz. their domain complexity. We find a strong linear relation between our approach\''s accuracy on a particular gold standard and its domain complexity, which we then use to estimate our approach\''s accuracy. Subsequently, we use certain textual characteristics -- viz. domain complexity, domain similarity, and readability -- in a variety of applications. Domain complexity and domain similarity measures are used to determine parameter settings in two tasks. Domain complexity guides us in model selection for in-domain polarity classification, viz. in decisions regarding word n-gram model order and word n-gram feature selection. Domain complexity and domain similarity guide us in domain adaptation. We propose a novel domain adaptation scheme and apply it to cross-domain polarity classification in semi- and unsupervised domain adaptation scenarios. Readability is used for feature engineering. We propose to adopt readability gradings, readability indicators as well as word and syntax distributions as features for subjectivity classification. Moreover, we generalize a framework for modeling and representing negation in machine learning-based sentiment analysis. This framework is applied to in-domain and cross-domain polarity classification. We investigate the relation between implicit and explicit negation modeling, the influence of negation scope detection methods, and the efficiency of the framework in different domains. Finally, we carry out a case study in which we transfer the core methods of our thesis -- viz. domain complexity-based accuracy estimation, domain complexity-based model selection, and negation modeling -- to a gold standard that originates from a genre and domain hitherto not used in this thesis