NILC_USP: aspect extraction using semantic labels

This paper details the system NILC USP that participated in the Semeval 2014: Aspect Based Sentiment Analysis task. This system uses a Conditional Random Field (CRF) algorithm for extracting the aspects mentioned in the text. Our work added semantic labels into a basic feature set for measuring the efficiency of those for aspect extraction. We used the semantic roles and the highest verb frame as features for the machine learning. Overall, our results demonstrated that the system could not improve with the use of this semantic information, but its precision was increased.FAPES

BuscaOpinioes: searching for opinions over the internet

This paper describes the BuscaOpinioes website, a tool for searching for opinions over the internet. Our system uses Google search engine to retrieve reviews from the internet and a lexicon-based sentimento analysis approach to identify opinions in these reviews. A web interface is available to visualize the results as well as some statistics.FAPES

NILC_USP: an improved hybrid system for sentiment analysis in Twitter messages.

This paper describes the NILC USP system that participated in SemEval-2014 Task 9: Sentiment Analysis in Twitter, a re-run of the SemEval 2013 task under the same name. Our system is an improved version of the system that participated in the 2013 task. This system adopts a hybrid classification process that uses three classification approaches: rule-based, lexiconbased and machine learning. We suggest a pipeline architecture that extracts the best characteristics from each classifier. In this work, we want to verify how\ud this hybrid approach would improve with better classifiers. The improved system achieved an F-score of 65.39% in the Twitter message-level subtask for 2013 dataset (+ 9.08% of improvement) and 63.94% for 2014 dataset.FAPESPSAMSUN

A large corpus of product reviews in Portuguese: tackling out-of-vocabulary words

Web 2.0 has allowed a never imagined communication boom. With the widespread use of computational and mobile devices, anyone, in practically any language, may post comments in the web. As such, formal language is not necessarily used. In fact, in these communicative situations, language is marked by the absence of more complex syntactic structures and the presence of internet slang, with missing diacritics, repetitions of vowels, and the use of chat-speak style abbreviations, emoticons and colloquial expressions. Such language use poses severe new challenges for Natural Language Processing (NLP) tools and applications, which, so far, have focused on well-written texts. In this work, we report the construction of a large web corpus of product reviews in Brazilian Portuguese and the analysis of its lexical phenomena, which support the development of a lexical normalization tool for, in future work, subsidizing the use of standard NLP products for web opinion mining and summarization purposes.University of São PauloSamsung Eletrônica da Amazônia LtdaFAPESPCNP

A qualitative analysis of a corpus of opinion summaries based on aspects

Aspect-based opinion summarization is the task of automatically generating a summary\ud for some aspects of a specific topic from a set of opinions. In most cases, to evaluate the quality of the automatic summaries, it is necessary to have a reference corpus of human\ud summaries to analyze how similar they are. The scarcity of corpora in that task has been a limiting factor for many research works. In this paper, we introduce OpiSums-PT, a corpus of extractive and abstractive summaries of opinions written in Brazilian Portuguese. We use this corpus to analyze how similar human summaries are and how people take into account the issues of aspect coverage and sentimento orientation to generate manual summaries. The results of these analyses show that human summaries are diversified and people generate summaries only for some aspects, keeping the overall sentiment orientation with little variation.Samsung Eletrônica da Amazônia Ltda

Extração de aspectos em análise de sentimentos para língua portuguesa

Aspect-based sentiment analysis is the field of study which extracts and interpret the sentiment, usually classified as positive or negative, towards some target or aspect in an opinionated text. This doctoral dissertation details an empirical study of techniques and methods for aspect extraction in aspect-based sentiment analysis with the focus on Portuguese. Three different approaches were explored: frequency-based, relation-based and machine learning. In each one, this work shows a comparative study between a Portuguese and an English corpora and the differences found in applying the approaches. In addition, richer linguistic knowledge is also explored by using syntatic dependencies and semantic roles, leading to better results. This work lead to the establishment of new benchmarks for the aspect extraction in Portuguese.A análise do sentimento orientada a aspectos é o campo de estudo que extrai e interpreta o sentimento, geralmente classificado como positivo ou negativo, em direção a algum alvo ou aspecto em um texto de opinião. Esta tese de doutorado detalha um estudo empírico de técnicas e métodos para extração de aspectos em análises de sentimentos baseadas em aspectos com foco na língua Portuguesa. Foram exploradas três diferentes abordagens: métodos baseados na frequências, métodos baseados na relação e métodos de aprendizagem de máquina. Em cada abordagem, este trabalho mostra um estudo comparativo entre um córpus para o Português e outro para o Inglês e as diferenças encontradas na aplicação destas abordagens. Além disso, o conhecimento linguístico mais rico também é explorado pelo uso de dependências sintáticas e papéis semânticos, levando a melhores resultados. Este trabalho resultou no estabelecimento de novos padrões de avaliação para a extração de aspectos em Português

Extração de aspectos em análise de sentimentos para língua portuguesa

Aspect-based sentiment analysis is the field of study which extracts and interpret the sentiment, usually classified as positive or negative, towards some target or aspect in an opinionated text. This doctoral dissertation details an empirical study of techniques and methods for aspect extraction in aspect-based sentiment analysis with the focus on Portuguese. Three different approaches were explored: frequency-based, relation-based and machine learning. In each one, this work shows a comparative study between a Portuguese and an English corpora and the differences found in applying the approaches. In addition, richer linguistic knowledge is also explored by using syntatic dependencies and semantic roles, leading to better results. This work lead to the establishment of new benchmarks for the aspect extraction in Portuguese.A análise do sentimento orientada a aspectos é o campo de estudo que extrai e interpreta o sentimento, geralmente classificado como positivo ou negativo, em direção a algum alvo ou aspecto em um texto de opinião. Esta tese de doutorado detalha um estudo empírico de técnicas e métodos para extração de aspectos em análises de sentimentos baseadas em aspectos com foco na língua Portuguesa. Foram exploradas três diferentes abordagens: métodos baseados na frequências, métodos baseados na relação e métodos de aprendizagem de máquina. Em cada abordagem, este trabalho mostra um estudo comparativo entre um córpus para o Português e outro para o Inglês e as diferenças encontradas na aplicação destas abordagens. Além disso, o conhecimento linguístico mais rico também é explorado pelo uso de dependências sintáticas e papéis semânticos, levando a melhores resultados. Este trabalho resultou no estabelecimento de novos padrões de avaliação para a extração de aspectos em Português

Corpus annotation of textual aspects in multi-document summaries

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