Measuring Similarity for Short Texts on Social Media

Duong, Phuc H.; Nguyen, Hien; Huynh, Ngoc-Tu

doi:10.1007/978-3-319-42345-6_22

Cited by 4 publications

(2 citation statements)

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“…M. A. Sultan, S. Bethard, and T. Sumner in [28] measured the similarities of text by combining a vector similarity feature derived from the word embedding with alignment based similarity. The other model for the semantic measurement between short texts was proposed by [32] using combinations of two different features: (1) distributed word representation, (2) corpus and knowledge-based metrics. Later, the presented method was tested and evaluated by using datasets of Microsoft Research Paraphrase Corpus and SemEval2015.…”

Section: Related Workmentioning

confidence: 99%

A computational analysis of short sentences based on ensemble similarity model

Alhadi¹,

Deraman²,

Jalil³

et al. 2019

IJECE

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<p>The rapid development of Internet along with the wide use of social media applications produce huge volume of unstructured data in short text form such as tweets, text snippets and instant messages. This form of data rarely contains repeated word. It presents challenge in sentences similarity analysis as the standard text similarity models merely rely on the number of word occurrence, often resulting unreliable similarity value. Besides, the use of abbreviation, acronyms, slang, smiley, jargon, symbol or non-standard short form also contributes to the difficulty in similarity analysis. Thus, an extended ensemble similarity model approach is proposed. An experimental study has been conducted using datasets of English short sentences. The findings are very encouraging in improving the similarity value for short sentences.</p>

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Section: Related Workmentioning

confidence: 99%

A computational analysis of short sentences based on ensemble similarity model

Alhadi¹,

Deraman²,

Jalil³

et al. 2019

IJECE

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“…However, they tend to consume high computational resources. Moreover, it has been observed that a robust preprocessing and feature extractor function that is able to normalize and extract Twitter specific text features may significantly improve the performance of STSS measures in the context of social media data [42], [43], [11].…”

Section: Stss Challenges In Twittermentioning

confidence: 99%

An Empirical Performance Evaluation of Semantic-Based Similarity Measures in Microblogging Social Media

Alnajran

Crockett

McLean

et al. 2018

2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT)

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Measuring textual semantic similarity has been a subject of intense discussion in NLP and AI for many years. A new area of research has emerged that applies semantic similarity measures within Twitter. However, the development of these measures for the semantic analysis of tweets imposes fundamental challenges. The sparsity, ambiguity, and informality present in social media are hampering the performance of traditional textual similarity measures as "tweets", have special syntactic and semantic characteristics. This paper reviews and evaluates the performance of topological, statistical, and hybrid similarity measures, in the context of Twitter analysis. Furthermore, the performance of each measure is compared against a naïve keyword-based similarity computation method to assess the significance of semantic computation in capturing the meaning in tweets. An experiment is designed and conducted to evaluate the different measures through examining various metrics, including correlation, error rates, and statistical tests on a benchmark dataset. The potential weaknesses of semantic similarity measures in relation to Twitter applications of textual similarity assessment and the research contributions are discussed. This research highlights challenges and potential improvement areas for the semantic similarity of tweets, a resource for researchers and practitioners.

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