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Dang, Edward Kai Fung; Luk, Robert W. P.; Allan, James; Ho, Kei Shiu; Chan, Siu Lai; Chung, Kwok Fai; Lee, Dik Lun

doi:10.1002/asi.v61:12

Cited by 217 publications

(27 citation statements)

References 485 publications

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“…Other times, ready-made lexical dictionaries like General Inquiry (Stone, Dunphy, Smith, & Ogilvie, 1966) or SentiWordNet (SWN) (Esuli & Sebastiani, 2006) are used, although some studies report that these are less effective than lexicons extracted from the test data collection itself (Andreevskaia & Bergler, 2006;. While many approaches use individual words for processing semantic information, some approaches use natural language processing techniques that consider not only the word but the entire sentence (Agarwal et al, 2011;Castro-Espinoza, Gelbukh, & González-Mendoza, 2013;Liu et al, 2016;Tang, Tan, & Cheng, 2009;Thelwall, Buckley, & Paltoglou, 2012;Thelwall, Buckley, Paltoglou, Cai, & Kappas, 2010). These approaches are typically based on the presence or absence of a subjective term in a document, and do not incorporate the frequency of the subjective term in question.…”

Section: Related Workmentioning

confidence: 99%

Using language models to improve opinion detection

Belbachir

2018

Information Processing & Management

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A B S T R A C T :Opinion mining is one of the most important research tasks in the information retrieval research community. With the huge volume of opinionated data available on the Web, approaches must be developed to differentiate opinion from fact. In this paper, we present a lexicon-based approach for opinion retrieval. Generally, opinion retrieval consists of two stages: relevance to the query and opinion detection. In our work, we focus on the second state which itself focusses on de-tecting opinionated documents . We compare the document to be analyzed with opinionated sources that contain subjective information. We hypothesize that a document with a strong si-milarity to opinionated sources is more likely to be opinionated itself. Typical lexicon-based approaches treat and choose their opinion sources according to their test collection, then cal-culate the opinion score based on the frequency of subjective terms in the document. In our work, we use different open opinion collections without any specific treatment and consider them as a reference collection. We then use language models to determine opinion scores. The analysis document and reference collection are represented by different language models (i.e., Dirichlet, Jelinek-Mercer and two-stage models). These language models are generally used in information retrieval to represent the relationship between documents and queries. However, in our study, we modify these language models to represent opinionated documents. We carry out several ex-periments using Text REtrieval Conference (TREC) Blogs 06 as our analysis collection and Internet Movie Data Bases (IMDB), Multi-Perspective Question Answering (MPQA) and CHESLY as our reference collection. To improve opinion detection, we study the impact of using different language models to represent the document and reference collection alongside different com-binations of opinion and retrieval scores. We then use this data to deduce the best opinion de-tection models. Using the best models, our approach improves on the best baseline of TREC Blog (baseline4) by 30%.

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Section: Related Workmentioning

confidence: 99%

Using language models to improve opinion detection

Belbachir

2018

Information Processing & Management

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“…Our final analysis explores a different feature of the contexts in which the expression "fake news" appear in our dataset: their polarities, that is, whether the expressed opinion in the texts is mostly positive, negative or neutral. Here, we performed sentiment analysis [23] in each one of the contexts in our dataset using SentiStrength 6 [25], a tool able to estimate the strength of positive and negative sentiment in short texts. Given a piece of text, this tool returns a score that varies from -4 (negative sentiment) to +4 (positive sentiment).…”

Section: Polaritymentioning

confidence: 99%

Fake News as We Feel It: Perception and Conceptualization of the Term “Fake News” in the Media

Cunha

Magno

Caetano

et al. 2018

Lecture Notes in Computer Science

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In this article, we quantitatively analyze how the term "fake news" is being shaped in news media in recent years. We study the perception and the conceptualization of this term in the traditional media using eight years of data collected from news outlets based in 20 countries. Our results not only corroborate previous indications of a high increase in the usage of the expression "fake news", but also show contextual changes around this expression after the United States presidential election of 2016. Among other results, we found changes in the related vocabulary, in the mentioned entities, in the surrounding topics and in the contextual polarity around the term "fake news", suggesting that this expression underwent a change in perception and conceptualization after 2016. These outcomes expand the understandings on the usage of the term "fake news", helping to comprehend and more accurately characterize this relevant social phenomenon linked to misinformation and manipulation.

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“…Thelwall et al (2010) present a sentiment analysis system, available online 1 . Following a recent trend there is a wide variety applied to Twitter as well.…”

Section: Sentiment Analysismentioning

confidence: 99%