Sentiue: Target and Aspect based Sentiment Analysis in SemEval-2015 Task 12

This paper describes our participation in SemEval-2015 Task 12, and the opinion mining system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a supervised machine learning classifier, for each label, followed by a selection based on the probability of the entity/attribute pair, on that domain. The target expression detection, for slot 2, is achieved by using a catalog of known targets for each entity type, complemented with named entity recognition. In the opinion sentiment slot, we used a 3 class polarity classifier, having BoW, lemmas, bigrams after verbs, presence of polarized terms, and punctuation based features. Working in unconstrained mode, our results for slot 1 were assessed with precision between 57% and 63%, and recall varying between 42% and 47%. In sentiment polarity, sentiue’s result accuracy was approximately 79%, reaching the best score in 2 of the 3 domains

Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9

This document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This sys- tem maintains a supervised machine learn- ing approach to classify the tweet overall sentiment, but with a change in the used features and the algorithm. We use a re- stricted set of 47 features in subtask B and 31 features in subtask A. In the constrained mode, and for the five data sources, senti.ue achieved a score between 78,72 and 84,05 in subtask A, and a score between 55,31 and 71,39 in sub- task B. For the unconstrained mode, our score was slightly below, except for one case in subtask A

In search of reputation assessment: experiences with polarity classification in RepLab 2013

The diue system uses a supervised Machine Learning approach for the polarity classification subtask of RepLab. We used the Python NLTK for preprocessing, including file parsing, text analysis and feature extraction. Our best solution is a mixed strategy, combining bag-of-words with a limited set of features based on sentiment lexicons and superficial text analysis. This system begins by applying tokenization and lemmatization. Then each tweet content is analyzed and 18 features are obtained, related to presence of polarized term, negation before polarized expression and entity reference. For the first run, the learning and classification were performed with the Decision Tree algorithm, from the NLTK framework. In the second run, we used a pipeline of classifiers. The first classifier applies Naive Bayes in a bag-of-words feature model, with the 1500 most frequent words in the training set. The second classifier used the features from the first run plus another feature with the result from the previous classifier. Our system's best result had 0.54694 Accuracy and 0.31506 in F measure

The Senso Question Answering System at QA@CLEF 2008

This article has the Working Notes about the Universidade de Évora's participation in QA@CLEF2008 (http://www.clef-campaign.org/), based on the Senso question answer system and the Portuguese monolingual task

BCLaaS: implementação de uma base de conhecimento linguístico as-a-service

Na área de Processamento de Linguagem Natural existem operações muito frequentes, independentemente do maior ou menor grau de análise linguística praticada. Um caso muito comum é a consulta da lista de sinónimos de um termo. Num ambiente com várias aplicações deste género, como Sistemas de Pergunta-Resposta, Análise de Sentimentos e outros, a manutenção destes recursos de apoio linguístico junto de cada aplicação torna-se pouco eficaz. Para cada ajuste numa coleção de sinónimos, por exemplo, seria necessário gerir o processo de atualização dos recursos individuais instalados junto das aplicações. Este trabalho descreve a conceção de uma base de conhecimento linguístico as-a-service, considerando aspetos de armazenamento, comunicação e gestão de conteúdo, que permitam uma solução evolutiva e eficiente

The Senso Question Answering approach to Portuguese QA@CLEF-2007

This article has the Working Notes about the Universidade de Évora's participation in QA@CLEF2007 (http://www.clef-campaign.org/), based on the Senso question answer system and the Portuguese monolingual task

DI@UE in CLEF2012: question answering approach to the multiple choice QA4MRE challenge

In the 2012 edition of CLEF, the DI@UE team has signed up for Question Answering for Machine Reading Evaluation (QA4MRE) main task. For each question, our system tries to guess which of the five hypotheses is the more plausible response, taking into account the reading test content and the documents from the background collection on the question topic. For each question, the system applies Named Entity Recognition, Question Classification, Document and Passage Retrieval. The criteria used in the first run is to choose the answer with the smallest distance between question and answer key elements. The system applies a specific treatment for certain factual questions, with the categories Quantity, When, Where, What, and Who, whose responses are usually short and likely to be detected in the text. For the second run, the system tries to solve each question according to its category. Textual patterns used for answer validation and Web answer projection are defined according to the question category. The system answered to all 160 questions, having found 50 right candidate answers

ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter

This paper describes the system we have used for participating in Subtasks A (Message Polarity Classification) and B (Topic Based Message Polarity Classification according to a two-point scale) of SemEval2017 Task 4 Sentiment Analysis in Twitter. We used several features with a sentiment lexicon and NLP techniques, Maximum Entropy as a classifier for our system.gLINK project of ”Erasmus Mundus Programme, Action 2 - STRAND 1, Lot 5, Asia (East)

Detecting Persuasion Attempts on Social Networks: Unearthing the Potential of Loss Functions and Text Pre-Processing in Imbalanced Data Settings

The rise of social networks and the increasing amount of time people spend on them have created a perfect place for the dissemination of false narratives, propaganda, and manipulated content. In order to prevent the spread of disinformation, content moderation is needed. However, manual moderation is unfeasible due to the large amount of daily posts. This paper studies the impact of using different loss functions on a multi-label classification problem with an imbalanced dataset, consisting of 20 persuasion techniques and only 950 samples, provided by SemEval’s 2021 Task 6. We used machine learning models, such as Naive Bayes and Decision Trees, and a custom deep learning architecture, based on DistilBERT and Convolutional Layers. Overall, the machine learning models achieved far worse results than the deep learning model, using Binary Cross Entropy, which we considered our baseline deep learning model. To address the class imbalance problem, we trained our model using different loss functions, such as Focal Loss and Asymmetric Loss. The latter providing the best results, particularly for the least represented classes

OLAP em âmbito hospitalar: Transformação de dados de enfermagem para análise multidimensional

Resumo O desenvolvimento a que assistimos nos dias que correm leva-nos a ter que tomar decisões cada vez mais sustentadas e correctas. Tal encaminha-nos para uma procura incessante de mais e melhor informação. O uso de bases de dados e a constante angariação de dados subjacente para salvaguardar e optimizar o funcionamento das organizações de diferentes áreas vieram trazer um grande avanço na perspectiva da colecta de dados que posteriormente se tornam numa grande fonte de conhecimentos. Os Data Warehouses (DW), aliados a sistemas de Online Analytical Processing (OLAP), estão contidos no rol de soluções disponíveis para análise de dados e são uma grande mais valia para os analistas que vêem o seu trabalho bastante facilitado. Este artigo visa descrever estas duas tecnologias e mostrar como elas se complementam, levando ao desenvolvimento de uma ferramenta incluída na temática de Business Intelligence