An automatic corpus based method for a building Multiple Fuzzy Word Dataset

Chandran, David; Crockett, Keeley; McLean, David; Crispin, Alan

doi:10.1109/fuzz-ieee.2015.7337877

Cited by 6 publications

(7 citation statements)

References 24 publications

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“…In particular, a fuzzy fingerprint text based approach has been proposed in [36] for classification of companies, which outperformed other popularly used nonfuzzy approaches. Another fuzzy approach was proposed in [37] towards automatically building a corpus that can be used for comparison of text similarity. The experimental results showed that the fuzzy metrics had a higher correlation with human ratings in comparison with other traditional metrics.…”

Section: Background Of Fuzzy Text Classificationmentioning

confidence: 99%

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

Liu

Burnap

Alorainy

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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Section: Background Of Fuzzy Text Classificationmentioning

confidence: 99%

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

Liu

Burnap

Alorainy

et al. 2019

IEEE Trans. Comput. Soc. Syst.

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“…A fuzzy fingerprint text based classification of companies has been developed [23], which outperformed other commonly used non-fuzzy techniques. A fuzzy approach was used in [24] to automatically build a corpus to be used for text similarity comparison; their results showed that the fuzzy metrics had a higher correlation with human ratings when compared with traditional metrics. An unsupervised fuzzy approach [25] was used to classify Twitter users according to gender.…”

Section: Related Studiesmentioning

confidence: 99%

Fuzzy approach for sentiment analysis

Jefferson

Liu

Cocea

2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Sentiment analysis aims to identify the polarity of a document through natural language processing, text analysis and computational linguistics. Over the last decade, there has been much focus on sentiment analysis as the data available on-line has grown exponentially to include many sentiment based documents (reviews, feedback, articles). Many approaches consider machine learning techniques or statistical analysis, but there has been little use of the fuzzy classifiers in this field especially considering the ambiguity of language and the suitability of fuzzy approaches to deal with this ambiguity. This paper proposes a fuzzy rule based system for sentiment analysis, which can offer more refined outputs through the use of fuzzy membership degrees. We compare the performance of our proposed approach with commonly used sentiment classifiers (e.g. Decision Trees, Naïve Bayes) which are known to perform well in this task. The experimental results indicate that our fuzzybased approach performs marginally better than the other algorithms. In addition, the fuzzy approach allows the definition of different degrees of sentiment without the need to use a larger number of classes.

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“…FAST identified fuzzy words within a short text and calculated the effect such words would have on the overall similarity. Experimental results showed that FAST gave an improved correlation between the similarity measure and human ratings [18,24] compared with traditional measures.…”

Section: IImentioning

confidence: 99%

“…5) Apply sentence similarity measure using word similarities from different word pair combinations from 3) and 4). Developed from an established traditional sentence similarity measure known as STASIS [24], FAST also incorporates an empirically determined semantic threshold, α, which was used to filter out word pairs with very low similarity scores Li et al [24] justified the use of a semantic threshold, particularly when short texts were very short in length. The work also determined that function words (words that express grammatical relationships with other words within a short text [24] i.e.…”

Section: IImentioning

confidence: 99%

Application of fuzzy semantic similarity measures to event detection within tweets

Crockett

Adel

O’Shea

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper examines the suitability of applying fuzzy semantic similarity measures (FSSM) to the task of detecting potential future events through the use of a group of prototypical event tweets. FSSM are ideal measures to be used to analyse the semantic textual content of tweets due to the ability to deal equally with not only nouns, verbs, adjectives and adverbs, but also perception based fuzzy words. The proposed methodology first creates a set of prototypical event related tweets and a control group of tweets from a data source, then calculates the semantic similarity against an event dataset compiled from tweets issued during the 2011 London riots. The dataset of tweets contained a proportion of tweets that the Guardian Newspaper publically released that were attributed to 200 influential Twitter users during the actual riot. The effects of changing the semantic similarity threshold are investigated in order to evaluate if Twitter tweets can be used in conjunction with fuzzy short text similarity measures and prototypical event related tweets to determine if an event is more likely to occur. By looking at the increase in frequency of tweets in the dataset, over a certain similarity threshold when matched with prototypical event tweets about riots, the results have shown that a potential future event can be detected.

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An automatic corpus based method for a building Multiple Fuzzy Word Dataset

Cited by 6 publications

References 24 publications

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

A Fuzzy Approach to Text Classification With Two-Stage Training for Ambiguous Instances

Fuzzy approach for sentiment analysis

Application of fuzzy semantic similarity measures to event detection within tweets

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