Should I Care about Your Opinion? : Detection of Opinion Interestingness and Dynamics in Social Media

In this paper, we describe a set of reusable text processing components for extracting opinionated information from social media, rating it for interestingness, and for detecting opinion events. We have developed applications in GATE to extract named entities, terms and events and to detect opinions about them, which are then used as the starting point for opinion event detection. The opinions are then aggregated over larger sections of text, to give some overall sentiment about topics and documents, and also some degree of information about interestingness based on opinion diversity. We go beyond traditional opinion mining techniques in a number of ways: by focusing on specific opinion-target extraction related to key terms and events, by examining and dealing with a number of specific linguistic phenomena, by analysing and visualising opinion dynamics over time, and by aggregating the opinions in different ways for a more flexible view of the information contained in the documents.EU/27023

Semantic and influence aware k-representative queries over social streams

Ministry of Education, Singapore under its Academic Research Funding Tier

Mineração e uso de padrões linguísticos para desambiguação de palavras e análise do discurso

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2020.A extração de informação contida em textos na web tem o potencial de alavancar uma série de aplicações, mas muitas delas requerem a captura automática da semântica exata de elementos textuais relevantes. O Twitter, por exemplo, gera diariamente centenas de milhões de pequenos textos (tweets), muitos dos quais com rica informação sobre usuários, fatos, produtos, serviços, desejos, opiniões, etc. A anotação semântica de palavras relevantes em tweets é um grande desafio, pois eles impõem dificuldades adicionais (e.g., pouca informação de contexto, agramaticalidade) para métodos automáticos realizarem uma desambiguação de qualidade, o que leva a resultados com baixa precisão e cobertura. Inclusive, porque a língua é um sistema simbólico polissêmico, que não tem uma semântica pronta, o que se manifesta acentuadamente em linguagem coloquial e particularmente em mídias sociais. As soluções atuais de anotação geralmente não conseguem encontrar o sentido correto de palavras em construções envolvendo a semântica implícita que, às vezes, é colocada intencionalmente, por exemplo, para fazer humor, ironia, jogo de palavras ou trocadilhos. Este trabalho propõe o desenvolvimento de uma abordagem para minerar padrões léxico-semânticos, com a finalidade de captar a semântica em texto para utilizar em tarefas que processam a linguagem. Estes padrões foram denominados de padrões MSC+, pois são definidos por sequências de Componentes Morfo-semânticos (MSC). Um algoritmo não-supervisionado foi desenvolvido para minerar tais padrões, que suportam a identificação de um novo tipo de característica semântica em documentos, assim como métodos para desambiguar o sentido de palavras. Os resultados de experimentos com a tarefa de Word Sense Disambiguation (WSD), em texto de mídia social, mostraram que instâncias de alguns padrões MSC+ aparecem em vários tweets, mas às vezes usando palavras diferentes para transmitir o sentido. Os testes realizados nos resultados do experimento em WSD demonstraram que a exploração dos padrões MSC+ permite mecanismos eficazes na desambiguação do sentido de palavras, levando a melhorias no estado da arte, segundo medidas de precisão, cobertura e medida-F. Os padrões MSC+ também foram explorados em experimentos com Análise do Discurso (AD) do conteúdo de diferentes obras do escritor Machado de Assis. Os experimentos revelaram a incidência de padrões morfo-semânticos que evidenciam características de obras literárias e que podem auxiliar na classificação de discurso das obras analisadas, tais como a preponderância de verbos específicos nos contos, de substantivos femininos nos romances e adjetivos nos poemas.Abstract: Information extraction from social media texts has the potential to boost a number of applications, but many of them require the automatic capture of accurate semantics of relevant textual elements. Twitter, for example, generates hundreds of millions of short texts (tweets) daily, many of which containing rich information about users, facts, products, services, desires, opinions, etc. The semantic annotation of relevant words in tweets is a challenge because social media impose additional difficulties (e.g., little context information, poor grammatical rules conformity) for automatic methods to carry out quality disambiguation. It leads to results with low accuracy and coverage. In addition, a language is a polysemic symbolic system without ready semantics for some constructs. Sometimes words have implicit semantics (e.g., to make humor, irony, wordplay). It is common in colloquial language, and particularly in social media. In this work, we propose the development of an approach to mine lexical-semantic patterns and capture the semantics of texts for use in language processing tasks. We learn these patterns, that we call MSC+ patterns, from text data defined by Morpho-semantic Components (MSC). An unsupervised algorithm was developed to mine such patterns, which support the identification of a new kind of semantic feature in documents, as well as methods for disambiguating the meaning of words. Experimental results on Word Sense Disambiguation (WSD) task, from tweets, show that instances of some MSC+ patterns arise in many tweets, but sometimes using different words to convey the sense of the respective MSC in some tweets where pattern instances appear. The exploitation of MSC+ patterns when they induce semantics on target words enables effective word sense disambiguation mechanisms leading to improvements in the state of the art (e.g., metrics such as accuracy, coverage, and F-measure). We also explored the MSC+ patterns on the Discourse Analysis (DA) with literary content. Experimental results on selected works of a Brazilian writer submitted to our algorithm reveal the incidence of distinct morpho-semantic patterns in different types of works, such as the preponderance of specific verbs in tales, feminine nouns in romances, and adjectives in poems

Macro-micro approach for mining public sociopolitical opinion from social media

During the past decade, we have witnessed the emergence of social media, which has prominence as a means for the general public to exchange opinions towards a broad range of topics. Furthermore, its social and temporal dimensions make it a rich resource for policy makers and organisations to understand public opinion. In this thesis, we present our research in understanding public opinion on Twitter along three dimensions: sentiment, topics and summary. In the first line of our work, we study how to classify public sentiment on Twitter. We focus on the task of multi-target-specific sentiment recognition on Twitter, and propose an approach which utilises the syntactic information from parse-tree in conjunction with the left-right context of the target. We show the state-of-the-art performance on two datasets including a multi-target Twitter corpus on UK elections which we make public available for the research community. Additionally we also conduct two preliminary studies including cross-domain emotion classification on discourse around arts and cultural experiences, and social spam detection to improve the signal-to-noise ratio of our sentiment corpus. Our second line of work focuses on automatic topical clustering of tweets. Our aim is to group tweets into a number of clusters, with each cluster representing a meaningful topic, story, event or a reason behind a particular choice of sentiment. We explore various ways of tackling this challenge and propose a two-stage hierarchical topic modelling system that is efficient and effective in achieving our goal. Lastly, for our third line of work, we study the task of summarising tweets on common topics, with the goal to provide informative summaries for real-world events/stories or explanation underlying the sentiment expressed towards an issue/entity. As most existing tweet summarisation approaches rely on extractive methods, we propose to apply state-of-the-art neural abstractive summarisation model for tweets. We also tackle the challenge of cross-medium supervised summarisation with no target-medium training resources. To the best of our knowledge, there is no existing work on studying neural abstractive summarisation on tweets. In addition, we present a system for providing interactive visualisation of topic-entity sentiments and the corresponding summaries in chronological order. Throughout our work presented in this thesis, we conduct experiments to evaluate and verify the effectiveness of our proposed models, comparing to relevant baseline methods. Most of our evaluations are quantitative, however, we do perform qualitative analyses where it is appropriate. This thesis provides insights and findings that can be used for better understanding public opinion in social media

Personalized Time-Aware Tweets Summarization

We focus on the problem of selecting meaningful tweets given a user's interests; the dynamic nature of user interests, the sheer volume, and the sparseness of individual messages make this an challenging problem. Specifically, we consider the task of time-aware tweets summarization, based on a user's history and collaborative social influences from "social circles." We propose a time-aware user behavior model, the Tweet Propagation Model (TPM), in which we infer dynamic probabilistic distributions over interests and topics. We then explicitly consider novelty, coverage, and diversity to arrive at an iterative optimization algorithm for selecting tweets. Experimental results validate the effectiveness of our personalized time-aware tweets summarization method based on TPM