Relating the Opinion Holder and the Review Accuracy in Sentiment Analysis of Tourist Reviews

Colhon, Mihaela; Bǎdicǎ, Costin; Şendre, Alexandra

doi:10.1007/978-3-319-12096-6_22

Cited by 17 publications

(15 citation statements)

References 14 publications

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“…They pruned or discarded the aspects that have low frequent and not important for tourism domain. [21] selected five most debated aspects in reviews and built five seed sets of each aspect. In these seed sets each word is belonged to an important aspect.…”

Section: Rule Based Methodsmentioning

confidence: 99%

“…These lexicons have opinions words with the positive and negative score. Colhon et al [21] performed binary classification of reviews using lexicon of positive and negative terms. They applied term-counting method that is based on the positive and negative terms in a review which are related to aspects of the object under discussion.…”

Section: Lexicon Based Methods Lexicon Based Methods Classi-mentioning

confidence: 99%

“…In this paper, we argue that fuzzy based classification methods are very useful and effective in aspect based opinion classification. Five predominant fuzzy based algorithms, namely, Fuzzy Unordered Rule Induction Algorithm (FURIA) [41], Fuzzy Nearest Neighbors [42], Fuzzy Rough Nearest Neighbors [42], Vaguely Quantified Nearest Neighbors [42], and Fuzzy Lattice Reasoning (FLR) [43], have been compared with other similar supervised learning methods [17,18,[20][21][22]. Comparison results indicate noteworthy improvement in aspect based classification.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Aspect Based Opinion Classification System for Mining Tourist Reviews

Afzaal

Usman

Fong

et al. 2016

Advances in Fuzzy Systems

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Due to the large amount of opinions available on the websites, tourists are often overwhelmed with information and find it extremely difficult to use the available information to make a decision about the tourist places to visit. A number of opinion mining methods have been proposed in the past to identify and classify an opinion into positive or negative. Recently, aspect based opinion mining has been introduced which targets the various aspects present in the opinion text. A number of existing aspect based opinion classification methods are available in the literature but very limited research work has targeted the automatic aspect identification and extraction of implicit, infrequent, and coreferential aspects. Aspect based classification suffers from the presence of irrelevant sentences in a typical user review. Such sentences make the data noisy and degrade the classification accuracy of the machine learning algorithms. This paper presents a fuzzy aspect based opinion classification system which efficiently extracts aspects from user opinions and perform near to accurate classification. We conducted experiments on real world datasets to evaluate the effectiveness of our proposed system. Experimental results prove that the proposed system not only is effective in aspect extraction but also improves the classification accuracy.

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Section: Rule Based Methodsmentioning

confidence: 99%

Section: Lexicon Based Methods Lexicon Based Methods Classi-mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Aspect Based Opinion Classification System for Mining Tourist Reviews

Afzaal

Usman

Fong

et al. 2016

Advances in Fuzzy Systems

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“…Paper [17] presents result of sentiment analysis for relating tourist opinion holder with the review content. Paper [18] explores complex network representations of tourist reviews for extracting lexical and quantitative features of the review text.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Other two papers are focused on the analysis of data extracted from AmFostAcolo [17], [18] . Paper [17] presents result of sentiment analysis for relating tourist opinion holder with the review content.…”

Section: Background and Related Workmentioning

confidence: 99%

Complex Network Analysis of a Tourism Content Sharing Network

Becheru

Bǎdicǎ

Antonie

2015

2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

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This paper presents results of the analysis of a tourism information web-site (AmFostAcolo.ro) by using Complex Networks (CN) methods. The work accomplished here complements a previous paper, where we discussed data extraction and modelling into a complex network. Properties of the resulted network, communities and vertices are looked upon, in order to extract useful information and detect social phenomena. Temporal analysis methods are employed for examining the evolution of the web-site. The results obtained prove the natural development of the web-site and the usefulness of CN analysis methods in this scenario.

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