Aspect-Based Sentiment Classification with Attentive Neural Turing Machines

Mao, Qianren; Li, Jianxin; Wang, Senzhang; Zhang, Yuanning; Peng, Hao; He, Min; Wang, Lihong

doi:10.24963/ijcai.2019/714

Cited by 24 publications

(8 citation statements)

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“…Recently, deep learning has shown its powerful capability to handle various NLP tasks [51], [52]. For example, recurrent convolutional neural networks use different window sizes for one-dimensional convolution of word vectors for all words in a sentence to capture text semantic features [53].…”

Section: B Text Feature Extractingmentioning

confidence: 99%

Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification

Gong

Teng

et al. 2020

IEEE Access

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Traditional methods of multi-label text classification, particularly deep learning, have achieved remarkable results. However, most of these methods use word2vec technology to represent sequential text information, while ignoring the logic and internal hierarchy of the text itself. Although these approaches can learn the hypothetical hierarchy and logic of the text, it is unexplained. In addition, the traditional approach treats labels as independent individuals and ignores the relationships between them, which not only does not reflect reality but also causes significant loss of semantic information. In this paper, we propose a novel Hierarchical Graph Transformer based deep learning model for large-scale multi-label text classification. We first model the text into a graph structure that can embody the different semantics of the text and the connections between them. We then use a multi-layer transformer structure with a multi-head attention mechanism at the word, sentence, and graph levels to fully capture the features of the text and observe the importance of the separate parts. Finally, we use the hierarchical relationship of the labels to generate the representation of the labels, and design a weighted loss function based on the semantic distances of the labels. Extensive experiments conducted on three benchmark datasets demonstrated that the proposed model can realistically capture the hierarchy and logic of text and improve performance compared with the state-of-the-art methods. INDEX TERMS Multi-label text classification, graph modeling, graph transformer, deep learning.

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Section: B Text Feature Extractingmentioning

confidence: 99%

Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification

Gong

Teng

et al. 2020

IEEE Access

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“…The datasets that correspond to these domains came from different sources. While most of work conducted so far in restaurants and technology reviews came from the readily SemEval series of evaluations of computational semantic analysis systems, with readily labelled datasets, authors of other papers also used datasets from Sentihood [4,7]; Twitter [9,13,19,20,27,28]; Amazon [18]; Yelp [18]; Coursera [22], online market [21], to name a few. Manual data collection was performed in [11] using Web Crawler and APIs to collect restaurant and hotel reviews (2000 and 4000 respectively).…”

Section: Datasets and Application Domainsmentioning

confidence: 99%

Aspect-Based Sentiment Analysis in Education Domain

Hajrizi,

Nuçi

2020

Preprint

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Analysis of a large amount of data has always brought value to institutions and organizations. Lately, people's opinions expressed through text have become a very important aspect of this analysis. In response to this challenge, a natural language processing technique known as Aspect-Based Sentiment Analysis (ABSA) has emerged. Having the ability to extract the polarity for each aspect of opinions separately, ABSA has found itself useful in a wide range of domains. Education is one of the domains in which ABSA can be successfully utilized. Being able to understand and find out what students like and don't like most about a course, professor, or teaching methodology can be of great importance for the respective institutions. While this task represents a unique NLP challenge, many studies have proposed different approaches to tackle the problem. In this work, we present a comprehensive review of the existing work in ABSA with a focus in the education domain. A wide range of methodologies are discussed and conclusions are drawn. KeywordsAspect-based sentiment analysis • aspect category extraction • polarity evaluation • text mining • education • course reviews • supervised learning • unsupervised learning • data pre-processing • machine learning models

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“…The most widely used type of recommendation algorithm is based on collaborative filtering. Such algorithms can automatically learn the hidden features of users and items, which can help identify potential interests of users [19] [20]. However, collaborative filtering algorithms present data sparseness and cold start problems.…”

Section: Introductionmentioning

confidence: 99%

Score Prediction Algorithm Combining Deep Learning and Matrix Factorization in Sensor Cloud Systems

Gong

et al. 2021

IEEE Access

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In this era of exponential growth in the scale of data, information overload has become an urgent problem, and the use of increasingly flexible sensor cloud systems (SCS) for data collection has become a mainstream trend. Recommendation algorithms can search massive data sets to uncover information that meets the needs of users based on their interests. To improve the accuracy of recommendation scoring, this paper proposes a score prediction algorithm that combines deep learning and matrix factorization. To address the problem of sparse scoring data, our study employs a sensor cloud system to collect data information, preprocesses the collected information, and then uses a deep learning model combined with explicit and implicit feedback to generate recommendations. The proposed algorithm, MF-NeuRec, combines fusion matrix decomposition and the NeuRec model score prediction algorithm. The algorithm employs user-based and item-based NeuRec algorithms to extract the feature vectors of users and items under implicit feedback data. The obtained user and item feature vectors are integrated in a certain ratio through the use of matrix decomposition under the display feedback data. The user and item feature vectors obtained by the algorithm are merged and analyzed to predict how users will rate items. Experiments demonstrate that the algorithm can improve the accuracy of recommendations.

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Aspect-Based Sentiment Classification with Attentive Neural Turing Machines

Cited by 24 publications

References 0 publications

Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification

Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification

Aspect-Based Sentiment Analysis in Education Domain

Score Prediction Algorithm Combining Deep Learning and Matrix Factorization in Sensor Cloud Systems

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