A Lexical Grammatical Implementation of Affect

Mulder, M.P.; Nijholt, Anton; Uyl, Marten den; Terpstra, Peter

doi:10.1007/978-3-540-30120-2_22

Cited by 23 publications

(14 citation statements)

References 9 publications

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“…Secondly, research has only recently shown interest in extracting the sentiments that are expressed towards a certain entity of interest (in our case products) or their attributes (e.g., the size of the Nokia mobile phone) (e.g., Mulder et al 2004;Zhang and Zhang 2006;Kobayashi et al 2007). In the same sentence different opinions might be expressed towards different entities, or towards the same entity, but with varying intensity.…”

Section: Introduction 2 Related Research and Problem Definitionmentioning

confidence: 99%

“…By comparing the semantic similarity (Budanitsky and Hirst 2004), i.e., the length of the path from a word to respectively the words ''good'' and ''bad'', the semantic orientation of the word can be obtained. Techniques other than the bag-of-words approach include Mulder et al (2004), who model the relation between sentiment and object, using certain mechanics that alter the intensity (e.g., intensification and quantification) or orientation (e.g., negation and certain verbs) of the sentiment.…”

Section: Introduction 2 Related Research and Problem Definitionmentioning

confidence: 99%

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A machine learning approach to sentiment analysis in multilingual Web texts

2008

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Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.

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Section: Introduction 2 Related Research and Problem Definitionmentioning

confidence: 99%

Section: Introduction 2 Related Research and Problem Definitionmentioning

confidence: 99%

A machine learning approach to sentiment analysis in multilingual Web texts

2008

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“…(1) word level: Subasic and Huettner (2001); Kamps and Marx (2002); Riloff, Wiebe and Wilson (2003); Turney and Littman (2003); Baroni and Vegnaduzzo (2004); Andreevskaia and Bergler (2006); Strapparava, Valitutti and Stock (2007); (2) synset level: Esuli and Sebastiani (2006); Wiebe and Mihalcea (2006); (3) phrase level: Wilson, Wiebe and Hoffmann (2005); (4) clause or sentence level: Olveres et al (1998); Boucouvalas (2003); Liu, Lieberman and Selker (2003); Yu and Hatzivassiloglou (2003); Mulder et al (2004); Read (2004); Kim and Hovy (2005); Neviarouskaya, Prendinger and Ishizuka (2007c); Moilanen and Pulman (2007); Alm (2008); Aman and Szpakowicz (2008); Choi and Cardie (2008); (5) paragraph or document level: Subasic and Huettner (2001); Turney (2002); Pang, Lee and Vaithyanathan (2002); Mishne (2005); Kim and Hovy (2006); Leshed and Kaye (2006); Mihalcea and Liu (2006); Nadeau et al (2006).…”

Section: Background and Related Workmentioning

confidence: 97%

Affect Analysis Model: novel rule-based approach to affect sensing from text

2010

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In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).

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“…They assumed opinion expressing sentences with distance away from the query phrase less than five sentences were relevant to the query topic. In the NLP field, [15], [16] used NLP techniques to analyze the target of sentimental adjectives. In order to capture opinion term relatedness to the query, S. Gerani et al proposed a proximitybased opinion propagation method to calculate the opinion density at each point in a document [17].…”

Section: Related Workmentioning

confidence: 99%

Measuring Opinion Relevance in Latent Topic Space

Cheng

Sun

et al. 2011

2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing

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Abstract-Opinion retrieval engines aim to retrieve documents containing user opinions towards a given search query. Different from traditional IR engines which rank documents by their topic relevance to the search query, opinion retrieval engines also consider opinion relevance. The result documents should contain user opinions which should be relevant to the search query. In previous opinion retrieval algorithms, opinion relevance scores are usually calculated by using very straightforward approaches, e.g., the distance between search query and opinion-carrying words. These approaches may cause two problems: 1) opinions in the returned result documents are irrelevant to the search query; 2) opinions related to the search query are not well identified. In this paper, we propose a new approach to deal with this topicopinion mismatch problem. We leverage the idea of Probabilistic Latent Semantic Analysis. Both queries and documents are represented in a latent topic space, and then opinion relevance is calculated semantically in this topic space. Experiments on the TREC blog datasets indicate that our approach is effective in measuring opinion relevance and the opinion retrieval system based on our algorithm yields significant improvements compared with most state-of-the-art methods.

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A Lexical Grammatical Implementation of Affect

Cited by 23 publications

References 9 publications

A machine learning approach to sentiment analysis in multilingual Web texts

A machine learning approach to sentiment analysis in multilingual Web texts

Affect Analysis Model: novel rule-based approach to affect sensing from text

Measuring Opinion Relevance in Latent Topic Space

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