Citation

Huan JL, Sekh AA, Quek C, Prasad DK. Neural Comput Appl 2022; 34(3): 2341-2351.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s00521-021-06542-1

PMID

unavailable

Abstract

Text classification is one of the widely used phenomena in different natural language processing tasks. State-of-the-art text classifiers use the vector space model for extracting features. Recent progress in deep models, recurrent neural networks those preserve the positional relationship among words achieve a higher accuracy. To push text classification accuracy even higher, multi-dimensional document representation, such as vector sequences or matrices combined with document sentiment, should be explored. In this paper, we show that documents can be represented as a sequence of vectors carrying semantic meaning and classified using a recurrent neural network that recognizes long-range relationships. We show that in this representation, additional sentiment vectors can be easily attached as a fully connected layer to the word vectors to further improve classification accuracy. On the UCI sentiment labelled dataset, using the sequence of vectors alone achieved an accuracy of 85.6%, which is better than 80.7% from ridge regression classifier--the best among the classical technique we tested. Additional sentiment information further increases accuracy to 86.3%. On our suicide notes dataset, the best classical technique--the Naíve Bayes Bernoulli classifier, achieves accuracy of 71.3%, while our classifier, incorporating semantic and sentiment information, exceeds that at 75% accuracy. © 2021, The Author(s).

Language: en

Keywords

Regression analysis; Semantics; State of the art; Classification (of information); Sentiment analysis; Text classification; Long short-term memory; LSTM; Classical techniques; Extracting features; Information retrieval systems; Positional relationship; Recent progress; Text classifiers; Vector space models; Vector spaces; Vectors