Language-Independent Twitter Classification Using Character-Based Convolutional Networks

Zhang, Shiwei; Zhang, Xiuzhen; Chan, Jeffrey

doi:10.1007/978-3-319-69179-4_29

Cited by 2 publications

(2 citation statements)

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“…While word-based approaches are widely employed for handling MSA, numerous studies have introduced character-based approaches as an alternative. Most character-based methods leverage convolutional neural networks to construct efficient architectures that can extract valuable insights from character features ( Becker et al, 2017 ; Zhang, Zhang & Chan, 2017a ; Wehrmann et al, 2017 ; Wehrmann, Becker & Barros, 2018 ). In Becker et al (2017) , a translation-free deep neural approach for MSA was introduced, where three different neural architectures were compared using tweets from four distinct languages.…”

Section: Related Workmentioning

confidence: 99%

“…The obtained results demonstrated that the suggested architecture was competitive with the baseline models, offering a notable improvement by decreasing the number of parameters and memory usage. Similarly, Zhang, Zhang & Chan (2017a) proposed a language-agnostic model, namely unicode character convolutional neural network (UniCNN), designed for sentiment classification. UniCNN was assessed on six Twitter datasets from distinct languages, and the outcomes demonstrated its superior performance over state-of-the-art models.…”

Section: Related Workmentioning

confidence: 99%

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Heterogeneous text graph for comprehensive multilingual sentiment analysis: capturing short- and long-distance semantics

Mercha,

Benbrahim,

Erradi

2024

PeerJ Computer Science

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Multilingual sentiment analysis (MSA) involves the task of comprehending people’s opinions, sentiments, and emotions in multilingual written texts. This task has garnered considerable attention due to its importance in extracting insights for decision-making across diverse fields such as marketing, finance, and politics. Several studies have explored MSA using deep learning methods. Nonetheless, a majority of these studies depend on sequential-based approaches, which focus on capturing short-distance semantics within adjacent word sequences, but they overlook long-distance semantics, which can provide more profound insights for analysis. In this work, we propose an approach for multilingual sentiment analysis, namely MSA-GCN, leveraging a graph convolutional network to effectively capture both short- and long-distance semantics. MSA-GCN involves the comprehensive modeling of the multilingual sentiment analysis corpus through a unified heterogeneous text graph. Subsequently, a slightly deep graph convolutional network is employed to acquire predictive representations for all nodes by encouraging the transfer learning across languages. Extensive experiments are carried out on various language combinations using different benchmark datasets to assess the efficiency of the proposed approach. These datasets include Multilingual Amazon Reviews Corpus (MARC), Internet Movie Database (IMDB), Allociné, and Muchocine. The achieved results reveal that MSA-GCN significantly outperformed all baseline models in almost all datasets with a p-value < 0.05 based on student t-test. In addition, such approach shows prominent results in a variety of language combinations, revealing the robustness of the approach against language variation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%