Siamese Network-Based Supervised Topic Modeling

Huang, Minghui; Rao, Yanghui; Liu, Yuwei; Xie, Haoran; Wang, Fu Lee

doi:10.18653/v1/d18-1494

Cited by 16 publications

(6 citation statements)

References 30 publications

(25 reference statements)

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“…[36] extracts topics using LDA [2], and the emotion of each microblog is determined using a supervised emotion classifier. The topic-emotion models in [9,16,26,27,35] learn a supervised topicaware emotion classifier. These models rely on labeled training data and hence their ability to generalize to new topics is limited.…”

Section: Related Workmentioning

confidence: 99%

Emotion-Aware Event Summarization in Microblogs

Panchendrarajan

Hsu

Lee

2021

Companion Proceedings of the Web Conference 2021

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Microblogs have become the preferred means of communication for people to share information and feelings, especially for fast evolving events. Understanding the emotional reactions of people allows decision makers to formulate policies that are likely to be more well-received by the public and hence better accepted especially during policy implementation. However, uncovering the topics and emotions related to an event over time is a challenge due to the short and noisy nature of microblogs. This work proposes a weakly supervised learning approach to learn coherent topics and the corresponding emotional reactions as an event unfolds. We summarize the event by giving the representative microblogs and the emotion distributions associated with the topics over time. Experiments on multiple real-world event datasets demonstrate the effectiveness of the proposed approach over existing solutions.

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Section: Related Workmentioning

confidence: 99%

Emotion-Aware Event Summarization in Microblogs

Panchendrarajan

Hsu

Lee

2021

Companion Proceedings of the Web Conference 2021

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“…Most of them are built upon Variational Autoencode (VAE) (Kingma and Welling, 2014) which constructs a neural network to approximate the topic-word distribution in probabilistic topic models (Srivastava and Sutton, 2017;Sønderby et al, 2016;Bouchacourt et al, 2018). Intuitively, training the VAE-based supervised neural topic models with class labels (Chaidaroon and Fang, 2017;Huang et al, 2018;Gui et al, 2020) can introduce sentiment information into topic modelling, which may generate better features for sentiment classification.…”

Section: Related Workmentioning

confidence: 99%

Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

Runcong¹,

Gui²,

Pergola³

et al. 2021

Preprint

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In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brandassociated polarity-bearing topics from product reviews. Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as 'positive', 'negative' and 'neural', BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., 'shaver' or 'cream') while its associated sentiment gradually varies from negative to positive. BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews. Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and uniqueness, and extracting better-separated polaritybearing topics.

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“…Reference [29] attempted to leverage external linguistic knowledge for sentiment classification. Some works based on supervised topic model [30], semantically rich hybrid model [31], noisy label aggregation [32] and other methods are also proposed for text classification. Figure 3 is the structure of the SRCLA model, which contains four processes: 1 , 2 , 3 , 4 .…”

Section: Other Neural Networkmentioning

confidence: 99%

Stacked Residual Recurrent Neural Networks With Cross-Layer Attention for Text Classification

et al. 2020

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Text classification is a fundamental task in natural language processing and is essential for many tasks like sentiment analysis and question classification etc. As we all know, different NLP tasks require different linguistic features. Tasks such as text classification requires more semantic features than other tasks such as dependency parsing requiring more syntactic features. Most existing methods focus on improving performance by mixing and calibrating features, without distinguishing the types of features and corresponding effects. In this paper, we propose a stacked residual recurrent neural networks with crosslayer attention model to filter more semantic features for text classification, which named SRCLA. Firstly, we build a stacked network structure to filter different types of linguistic features, and then propose a novel cross-layer attention mechanism that exploits higher-level features to supervise the lower-level features to refine the filtering process. Based on this, more semantic features can be selected for text classification. We conduct experiments on eight text classification tasks, including sentiment analysis, question classification and subjectivity classification and compare with a broad range of baselines. Experimental results show that the proposed approaches achieve the state-of-the-art results on 5 out of 8 tasks. INDEX TERMS Cross-layer attention, stacked residual neural network, bidirectional long short-term memory, feature filtering, text classification.

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Siamese Network-Based Supervised Topic Modeling

Cited by 16 publications

References 30 publications

Emotion-Aware Event Summarization in Microblogs

Emotion-Aware Event Summarization in Microblogs

Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

Stacked Residual Recurrent Neural Networks With Cross-Layer Attention for Text Classification

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