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OBOME - Ontology based opinion mining in UBIPOL
Ontologies have a special role in the UBIPOL system, they help to structure the policy related context, provide conceptualization for policy domain and use in the opinion mining process. In this work we presented a system called Ontology Based Opinion Mining Engine (OBOME) for analyzing a domain-specific opinion corpus by first assisting the user with the creation of a domain ontology from the corpus. We determined the polarity of opinion on the various domain aspects. In the former step, the policy domain aspect has are identified (namely which policy category is represented by the concept). This identification is supported by the policy modelling ontology, which describe the most important policy â related classes and structure. Then the most informative documents from the corpus are extracted and asked the user to create a set of aspects and related keywords using these documents. In the latter step, we used the corpus specific ontology to model the domain and extracted aspect-polarity associations using grammatical dependencies between words. Later, summarized results are shown to the user to analyze and store. Finally, in an offline process policy modeling ontology is updated
Unleashing the Potential of Argument Mining for IS Research: A Systematic Review and Research Agenda
Argument mining (AM) represents the unique use of natural language processing (NLP) techniques to extract arguments from unstructured data automatically. Despite expanding on commonly used NLP techniques, such as sentiment analysis, AM has hardly been applied in information systems (IS) research yet. Consequentially, knowledge about the potentials for the usage of AM on IS use cases appears to be still limited. First, we introduce AM and its current usage in fields beyond IS. To address this research gap, we conducted a systematic literature review on IS literature to identify IS use cases that can potentially be extended with AM. We develop eleven text-based IS research topics that provide structure and context to the use cases and their AM potentials. Finally, we formulate a novel research agenda to guide both researchers and practitioners to design, compare and evaluate the use of AM for text-based applications and research streams in IS
TRANSFORMING GOVERNMENT AGENCIESâ APPROACH TO EPARTICIPATION THROUGH EFFICIENT EXPLOITATION OF SOCIAL MEDIA
Government agencies are making considerable investments for exploiting the capabilities offered by ICT, and especially the Internet, to increase citizensâ engagement in their decision and policy making processes. However, this first generation of e-participation has been characterised by limited usage of the âofficialâ e-consultation spaces of government agencies by the citizens. The emergence of Web 2.0 social media offers big opportunities for overcoming this problem, and proceeding to a second generation of broader, deeper and more advanced e-participation. This paper presents a methodology for the efficient exploitation of Web 2.0 social media by government agencies in order to broaden and enhance e-participation. It is based on a central platform which enables posting content and deploying micro web applications (âPolicy Gadgetsâ-Padgets) to multiple popular Web 2.0 social media, and also collecting usersâ interactions with them (e.g. views, comments, ratings) in an efficient manner using their application programming interfaces (API). These interactionsâ data undergo various levels of processing, such as calculation of useful analytics, opinion mining and simulation modelling, in order to provide effective support to public decision and policy makers. The proposed methodology allows government agencies to adopt advanced and highly effective âhybridâ e-participation approaches
Recolha, extração e classificação de opiniĂ”es sobre aplicaçÔes lĂșdicas para saĂșde e bem-estar
Nowadays, mobile apps are part of the life of anyone who owns a smartphone.
With technological evolution, new apps come with new features, which brings a
greater demand from users when using an application. Moreover, at a time when
health and well-being are a priority, more and more apps provide a better user
experience, not only in terms of health monitoring but also a pleasant experience
in terms of entertainment and well-being. However, there are still some limitations
regarding user experience and usability. What can best translate user satisfaction
and experience are application reviews. Therefore, to have a perception of the most
relevant aspects of the current applications, a collection of reviews and respective
classifications was performed.
This thesis aims to develop a system that allows the presentation of the most relevant
aspects of a given health and wellness application after collecting the reviews
and later extracting the aspects and classifying them. In the reviews collection task,
two Python libraries, one for the Google Play Store and one for the App Store, provide
methods for extracting data about an application. For the extraction and
classification of aspects, the LCF-ATEPC model was chosen given its performance
in aspects-based sentiment analysis studies.Atualmente, as aplicaçÔes móveis fazem parte da vida de qualquer pessoa que possua
um smartphone. Com a evolução tecnológica, novas aplicaçÔes surgem com
novas funcionalidades, o que traz uma maior exigĂȘncia por parte dos utilizadores
quando usam uma aplicação. Numa altura em que a saĂșde e bem-estar sĂŁo uma
prioridade, existem cada vez mais aplicaçÔes com o intuito de providenciar uma
melhor experiĂȘncia ao utilizador, nĂŁo sĂł a nĂvel de monitorização de saĂșde, mas
tambĂ©m de uma experiĂȘncia agradĂĄvel em termos de entertenimento e bem estar.
Contudo, existem ainda algumas limitaçÔes no que toca Ă experiĂȘncia e usabilidade
do utilizador. O que melhor pode traduzir a satisfação e experiĂȘncia do utilizador
são as reviews das aplicaçÔes. Assim sendo, para ter uma perceção dos aspetos
mais relevantes das atuais aplicaçÔes, foi feita uma recolha das reviews e respetivas
classificaçÔes.
O objetivo desta tese consiste no desenvolvimento de um sistema que permita
apresentar os aspetos mais relevantes de uma determinada aplicação de saĂșde e
bem estar, após a recolha das reviews e posterior extração dos aspetos e classificação
dos mesmos. No processo de recolha de reviews, foram usadas duas
bibliotecas em Python, uma relativa Ă Google Play Store e outra Ă App Store, que
providenciam métodos para extrair dados relativamente a uma aplicação. Para a
extração e classificação dos aspetos, o modelo LCF-ATEPC foi o escolhido dada a
sua performance em estudos de anĂĄlise de sentimento baseada em aspectos.Mestrado em Engenharia de Computadores e TelemĂĄtic
Mapping AI Arguments in Journalism Studies
This study investigates and suggests typologies for examining Artificial
Intelligence (AI) within the domains of journalism and mass communication
research. We aim to elucidate the seven distinct subfields of AI, which
encompass machine learning, natural language processing (NLP), speech
recognition, expert systems, planning, scheduling, optimization, robotics, and
computer vision, through the provision of concrete examples and practical
applications. The primary objective is to devise a structured framework that
can help AI researchers in the field of journalism. By comprehending the
operational principles of each subfield, scholars can enhance their ability to
focus on a specific facet when analyzing a particular research topic
Heuristics-Driven Link-of-Analogy Prompting: Enhancing Large Language Models for Document-Level Event Argument Extraction
In this study, we investigate in-context learning (ICL) in document-level
event argument extraction (EAE). The paper identifies key challenges in this
problem, including example selection, context length limitation, abundance of
event types, and the limitation of Chain-of-Thought (CoT) prompting in
non-reasoning tasks. To address these challenges, we introduce the
Heuristic-Driven Link-of-Analogy (HD-LoA) prompting method. Specifically, we
hypothesize and validate that LLMs learn task-specific heuristics from
demonstrations via ICL. Building upon this hypothesis, we introduce an explicit
heuristic-driven demonstration construction approach, which transforms the
haphazard example selection process into a methodical method that emphasizes
task heuristics. Additionally, inspired by the analogical reasoning of human,
we propose the link-of-analogy prompting, which enables LLMs to process new
situations by drawing analogies to known situations, enhancing their
adaptability. Extensive experiments show that our method outperforms the
existing prompting methods and few-shot supervised learning methods, exhibiting
F1 score improvements of 4.53% and 9.38% on the document-level EAE dataset.
Furthermore, when applied to sentiment analysis and natural language inference
tasks, the HD-LoA prompting achieves accuracy gains of 2.87% and 2.63%,
indicating its effectiveness across different tasks
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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 chatbotâs 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
Central Bank Communication in Ghana: Insights from a Text Mining Analysis
Effective central bank communication is useful for anchoring market expectations and enhancing macroeconomic stability. In this paper, the communication strategy of the Bank of Ghana (BOG) is analysed using BOGâs monetary policy committee press releases for the period 2018-2019. Specifically, we apply text mining techniques to investigate the readability, sentiments and hidden topics of the policy documents. Our results provide evidence of increased central bank communication during the sample period, implying improved monetary policy transparency. Also, the computed Coleman and Liau (1975) readability index shows that the word and sentence structures of the press releases have become less complex, indicating increased readability. Furthermore, we find an average monetary policy net sentiment score of 3.9 per cent. This means that the monetary policy committee expressed positive sentiments regarding policy and macroeconomic outlooks during the period. Finally, the estimated topic model reveals that the topic proportion for âmonetary policy and inflationâ was prominent in the year 2018 while concerns regarding exchange rate were strong in 2019. The paper recommends that in order to enhance monetary policy communication, the Bank of Ghana should continue to improve on the readability of the monetary policy press releases
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