Linguistically independent sentiment analysis using convolutional-recurrent neural networks model | IEEE Conference Publication | IEEE Xplore
Scheduled Maintenance: On Monday, 27 January, the IEEE Xplore Author Profile management portal will undergo scheduled maintenance from 9:00-11:00 AM ET (1400-1600 UTC). During this time, access to the portal will be unavailable. We apologize for any inconvenience.

Linguistically independent sentiment analysis using convolutional-recurrent neural networks model


Abstract:

Text classification is a process which analyses text and assigns one or more classes to it based on its content. This paper introduces a linguistically independent text c...Show More

Abstract:

Text classification is a process which analyses text and assigns one or more classes to it based on its content. This paper introduces a linguistically independent text classifier based on convolutional-recurrent neural networks. The classifier works at character level instead of some higher structures such as words, sentences, etc. To evaluate the accuracy of the proposed methodology, the Yelp data set and other multilingual data set obtained from film review databases containing Czech, German and Spanish languages were used. The resulting accuracy on the Yelp data set is 93,64%. We also proved that the proposed model can work for various languages.
Date of Conference: 01-03 July 2019
Date Added to IEEE Xplore: 25 July 2019
ISBN Information:
Conference Location: Budapest, Hungary

References

References is not available for this document.