Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets

Moghaddam, Hoora Rezaei; Ramanna, Sheela

doi:10.1016/j.patter.2020.100053

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…•"( ) However, there are certain applications such as document classification [16] and natural language processing [61,34] where overlapping classes are necessary with a tolerance form of rough sets [64]. A tolerance model uses a tolerance relation instead of equivalence where the transitivity property is relaxed which enables overlapping classes (in other words, objects can belong to more than one class).…”

Section: Uppermentioning

confidence: 99%

Short Text Classification with Tolerance Near Sets

Patel¹

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Text classification is a classical machine learning application in Natural Language Processing, which aims to assign labels to textual units such as documents, sentences, paragraphs, and queries. Applications of text classification include sentiment classification and news categorization. Sentiment classification identifies the polarity of text such as positive, negative or neutral based on textual features. In this thesis, we implemented a modified form of a tolerance-based algorithm (TSC) to classify sentiment polarities of tweets as well as news categories from text. The TSC algorithm is a supervised algorithm that was designed to perform short text classification with tolerance near sets (TNS). The proposed TSC algorithm uses pre-trained SBERT algorithm vectors for creating tolerance classes. The effectiveness of the TSC algorithm has been demonstrated by testing it on ten well-researched data sets. One of the datasets (Covid-Sentiment) was hand-crafted with tweets from Twitter of opinions related to COVID. Experiments demonstrate that TSC outperforms five classical ML algorithms with one dataset, and is comparable with all other datasets using a weighted F1-score measure.

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Section: Uppermentioning

confidence: 99%