Using deep neural networks for extracting sentiment targets in arabic tweets

By

El-Kilany A.

Azzam A.

El-Beltagy S.R.

Artificial Intelligence

In this paper, we investigate the problem of recognizing entities which are targeted by text sentiment in Arabic tweets. To do so, we train a bidirectional LSTM deep neural network with conditional random fields as a classification layer on top of the network to discover the features of this specific set of entities and extract them from Arabic tweets. We’ve evaluated the network performance against a baseline method which makes use of a regular named entity recognizer and a sentiment analyzer. The deep neural network has shown a noticeable advantage in extracting sentiment target entities from Arabic tweets. © 2018, Springer International Publishing AG.

Using deep neural networks for extracting sentiment targets in arabic

Using deep neural networks for extracting sentiment targets in arabic tweets

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Using deep neural networks for extracting sentiment targets in arabic

Using deep neural networks for extracting sentiment targets in arabic tweets

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