A Comprehensive Study on Opinion Mining Features and Their Applications

Noekhah, Shirin; Salim, Naomie; Zakaria, Nor Hidayati

doi:10.1007/978-3-319-59427-9_9

Cited by 3 publications

(1 citation statement)

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“…Text classification is a fundamental problem in the field of natural language processing (NLP), which has numerous applications such as sentiment analysis, spam detection, and extractive question answering [1][2][3][4]. With the widespread use of mobile internet, a large amount of short text information has emerged.…”

Section: Introductionmentioning

confidence: 99%

Concept and Dependencies Enhanced Graph Convolutional Networks for Short Text Classification

Zhang

Ping

2022

Preprint

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Short text classification task is a special kind of text classification task in that the text to be classified is generally short, typically generating a sparse text representation that lacks rich semantic information. Given this shortcoming, scholars worldwide have explored improved short text classification methods based on deep learning. However, existing methods cannot effectively use concept knowledge and long-distance word dependencies. Therefore, based on graph neural networks from the perspective of text composition, we propose concept and dependencies enhanced graph convolutional networks for short text classification. First, the co-occurrence relationship between words, the inclusion relationship between documents and words, long-distance word dependencies, and the association relationship between external concepts and words are defined. Then, a text graph is constructed for an entire text corpus based on the four relationships. Finally, the text graph is input into graph convolutional neural networks, and the category of each document node is predicted after two layers of convolution. Experimental results show that our proposed method overall best on multiple classical English text classification datasets.

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Section: Introductionmentioning

confidence: 99%