Sentiment Analysis with a Multilingual Pipeline

Bal, Daniella; Bal, Malissa; Bunningen, Arthur van; Hogenboom, Alexander; Hogenboom, Frederik; Frăsincar, Flavius

doi:10.1007/978-3-642-24434-6_10

Cited by 14 publications

(16 citation statements)

References 8 publications

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“…The negative words in the top-level satellite trigger a negative classification of this segment, which is a potentially irrelevant segThis analysis can yield a competitive advantage for businesses, as onefifth of all tweets and one-third of all blog posts discuss products or brands. (2) with T d representing all top-level RST nodes in the RST trees for document d.…”

Section: Sentiment Analysis and Rhetorical Relationsmentioning

confidence: 99%

Using rhetorical structure in sentiment analysis

et al. 2015

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Section: Sentiment Analysis and Rhetorical Relationsmentioning

confidence: 99%

Using rhetorical structure in sentiment analysis

et al. 2015

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“…In the last years, researchers have been working with sentiment analysis in many aspects. There are many cases where scientists do study the multilingual sentiment analysis in more details (BADER et al, 2011, BAL et al, 2011, GÎNSCA et al, 2011, BALAHUR AND TURCHI, 2012, BOYD-GRABER AND RESNIK, 2010. Lately, a majority of the research has been focused on the sentiment analysis on the web.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Free/Open Source Developers: Preliminary Findings From a Case Study

Rousinopoulos

Robles

González-Barahona

2014

RESI

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O desenvolvimento de software é uma atividade intensive em esforço humano. Assim, a forma como os desenvolvedores encaram suas tarefas é de suam importância. Em um ambiente como o usual em projetos de FOSS (free/open source software) em que profissionais (desenvolvedores pagos) compartilham os esforços de desenvolvimento com voluntários, a moral da comunidade de desenvolvedores e usuários é fundamental. Neste artigo, apresentamos uma análise preliminary utilizando técnicas de análise de sentimentos realizada em um projeto de FOSS. Para isso, executamos a mineração da lista de endereços eletrônicos de um projeto e aplicamos as técnicas propostas aos participantes mais relevantes. Embora a aplicação seja limitada, no momento atual, experamos que essa experiência possa ser benéfica no future para determiner situações que possam afetar os desenvolvedores ou o projeto, tais como baixa produtividade, abandono do projeto ou bifurcação do projeto, entre outras.

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“…It is expected that some researchers decided to test the straightforward approach which consists in, first, translating the messages to English, and, then, use a high performing English sentiment classifier (for instance, see [3] and [4]) instead of creating a sentiment classifier optimized for a given language. However, the advantages of a properly tuned sentiment classifier have been studied for different languages (for instance, see [18,25,1,2]).…”

Section: Introductionmentioning

confidence: 99%

A simple approach to multilingual polarity classification in Twitter

Téllez¹,

Miranda-Jiménez²,

Graff³

et al. 2017

Pattern Recognition Letters

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Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations and errors introduced by the people generating content. In this contribution, our aim is to provide a simple to implement and easy to use multilingual framework, that can serve as a baseline for sentiment analysis contests, and as starting point to build new sentiment analysis systems. We compare our approach in eight different languages, three of them have important international contests, namely, SemEval (English), TASS (Spanish), and SENTIPOLC (Italian). Within the competitions our approach reaches from medium to high positions in the rankings; whereas in the remaining languages our approach outperforms the reported results.

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Sentiment Analysis with a Multilingual Pipeline

Cited by 14 publications

References 8 publications

Using rhetorical structure in sentiment analysis

Using rhetorical structure in sentiment analysis

Sentiment Analysis of Free/Open Source Developers: Preliminary Findings From a Case Study

A simple approach to multilingual polarity classification in Twitter

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