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International Conference on Intelligence of Things

ICIT 2022: Intelligence of Things: Technologies and Applications pp 134–145Cite as

Aspect-Based Sentiment Analysis with Deep Learning: A Multidomain and Multitask Approach

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 148))

Abstract

Sentiment analysis aids in obtaining the opinion of the users towards a particular product, service or policy. Focusing on classifying the sentiment that corresponds to each aspect of the entity in the document will help to identify the sentiment more clearly. This is also the mission of aspect-based sentiment analysis (ABSA). The vast majority of prior studies in ABSA have implemented single-task execution models on single-domain datasets. This is inconvenient when it is necessary to perform the full range of tasks in ABSA and on domain-independent datasets. In this paper, we offer to operate the advanced arrangement of deep learning techniques for multidomain and multitask approach in ABSA. The main tasks in ABSA: aspect extraction, category identification, sentiment classification and domain classification are all finished by an integration framework of Convolutional Neural Network (CNN), Bidirectional Independent Long Short Term Memory (BiIndyLSTM) and Attention mechanism. In addition, we use a POS tag layer combined with GloVe in word embedding layer to get the morphological attributes of each token word from review sentences. Through the experimenting process in the Laptop_Restaurant_Hotel multidomain dataset, we found that our proposed model has achieved high precision in multitasking ABSA. With this approach, we hope our proposed model will lay the foundation for ensuring flexibility and multiutility compared to previous opinion analysis models.

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Notes

  1. 1.

    http://alt.qcri.org/semeval2016/task5/.

  2. 2.

    http://alt.qcri.org/semeval2016/task5/.

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Author information

Authors and Affiliations

  1. Vietnam-Korea University of Information and Communication Technology, The Danang University, Danang, Vietnam

    Trang Uyen Tran

  2. University of Economics, The Danang University, Danang, Vietnam

    Ha Thanh Thi Hoang

  3. University of Science and Technology, The Danang University, Danang, Vietnam

    Phuong Hoai Dang

  4. Université Côte d’Azur, CNRS, Inria, Nice, France

    Michel Riveill

Authors
  1. Trang Uyen Tran
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  2. Ha Thanh Thi Hoang
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  3. Phuong Hoai Dang
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  4. Michel Riveill
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Corresponding author

Correspondence to Trang Uyen Tran .

Editor information

Editors and Affiliations

  1. Wroclaw University of Science and Technology, Wrocław, Poland

    Ngoc-Thanh Nguyen

  2. Sejong University, Seoul, Korea (Republic of)

    Nhu-Ngoc Dao

  3. Vietnam National University of Agriculture, Hanoi, Vietnam

    Quang-Dung Pham

  4. Hanoi University of Mining and Geology, Hanoi, Vietnam

    Hong Anh Le

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Tran, T.U., Hoang, H.T.T., Dang, P.H., Riveill, M. (2022). Aspect-Based Sentiment Analysis with Deep Learning: A Multidomain and Multitask Approach. In: Nguyen, NT., Dao, NN., Pham, QD., Le, H.A. (eds) Intelligence of Things: Technologies and Applications . ICIT 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 148. Springer, Cham. https://doi.org/10.1007/978-3-031-15063-0_12

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