Aspect Extraction from Bangla Reviews using Convolutional Neural Network

Rahman, Md. Atikur; Dey, Emon Kumar

doi:10.1109/iciev.2018.8641050

Cited by 24 publications

(29 citation statements)

References 19 publications

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“…We exploited AEs, contractive AEs, and sparse AEs, trained in the stacked fashion. All the proposed models show better precision, recall, and F1-score, with respect to the state-of-the-art works of Rahman et al [7,8].…”

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confidence: 73%

“…Baseline results exploiting k-NN, SVM, and random forests are provided by the authors for both the datasets [7]. The latter work is followed by the work [8] of the same authors. They classified aspects using a CNN and obtained better performance with respect to their previous article [7] in terms of recall and F1-score.…”

Section: Related Workmentioning

confidence: 99%

“…The authors reported baseline results on the task of aspect extraction, relying on several standard machine learning algorithms, such as k-NN, support vector machine (SVM), and random forest. Then, in another article presented by the same authors, a model to extract aspects based on a convolutional neural network (CNN) is presented [8]. The model shows better performance, in terms of recall and F1-score, with respect to the above mentioned conventional classifiers, considering the same dataset.…”

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confidence: 99%

“…As far as we know, stacked AEs have never been exploited in the task of aspect extraction in the Bangla language. In particular, in [7] the authors relies on standard machine learning algorithms, such as k-NN, SVM and random forest, while in [8] the authors propose a CNN with a single convolutional layer, max pooling, and a final standard classification layer, composed by a fully connected neural network and a softmax.…”

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confidence: 99%

“…The following work makes use of three AEs methods for the task of ABSA in the Bangla language to improve aspect classification performance with respect to [7,8]. As the authors did in such articles, we had to preprocess the sentences to remove some noise in the text (i.e., information not useful in performing the classification task), for instance, numbers, stop words and punctuation.…”

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confidence: 99%

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Aspect Extraction from Bangla Reviews Through Stacked Auto-Encoders

Bodini

2019

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Interactions between online users are growing more and more in recent years, due to the latest developments of the web. People share online comments, opinions, and reviews about many topics. Aspect extraction is the automatic process of understanding the topic (the aspect) of such comments, which has obtained huge interest from commercial and academic points of view. For instance, reviews available in webshops (like eBay, Amazon, Aliexpress, etc.) can help the customers in purchasing products and automatic analysis of reviews would be useful, as sometimes it is almost impossible to read all the available ones. In recent years, aspect extraction in the Bangla language has been regarded more and more as a task of growing importance. In the previous literature, a few methods have been introduced to classify Bangla texts according to the aspect they were focused on. This kind of research is limited mainly due to the lack of publicly available datasets for aspect extraction in the Bangla language. We take into account the only two publicly available datasets, recently published, collected for the task of aspect extraction in the Bangla language. Then, we introduce several classification methods based on stacked auto-encoders, as far as we know never exploited in the task of aspect extraction in Bangla, and we achieve better aspect classification performance with respect to the state-of-the-art: the experiments show an average improvement of 0.17 , 0.31 and 0.30 (across the two datasets), respectively in precision, recall and F1-score, reported in the state-of-the-art works that tackled the problem.

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confidence: 73%