A bilingual approach for conducting Chinese and English social media sentiment analysis

Yan, Gongjun; He, Wu; Shen, Jiancheng; Tang, Chuanyi

doi:10.1016/j.comnet.2014.08.021

Cited by 42 publications

(12 citation statements)

References 21 publications

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“…The semantic component of our analysis is based on words and not on phrases, leading to an accuracy for automatic translation that may be higher than in processing phrases. We would also explore other approaches, from recent publications, such as: He & Zha, 2014;Kalampokis, Tambouris, & Tarabanis, 2013;Yan, He, Shen, & Tang, 2014;Yan, He, Shi, & Rawat, 2015).…”

Section: Limitations and Future Developmentsmentioning

confidence: 98%

Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams

Lipizzi

Iandoli

Marquez

2015

International Journal of Information Management

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Section: Limitations and Future Developmentsmentioning

confidence: 98%

Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams

Lipizzi

Iandoli

Marquez

2015

International Journal of Information Management

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“…Regarding the few multilingual polarity classification systems described in the literature, they are based on a supervised setting. In this respect, Yan et al (Yan et al, 2014) describe a supervised multilingual system for SA working on previously tokenized Chinese and English texts. Vilares et al (2015c) present a multilingual SA system trained on a multilingual dataset that is able to outperform monolingual systems on some monolingual datasets and that can work successfully on code-switching texts, i.e., texts that contain terms written in two or more different languages (Vilares et al, 2016a).…”

Section: Multilingual Samentioning

confidence: 99%

Universal, unsupervised (rule-based), uncovered sentiment analysis

Vilares

Gómez-Rodríguez

Alonso

2017

Knowledge-Based Systems

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We present a novel unsupervised approach for multilingual sentiment analysis driven by compositional syntax-based rules. On the one hand, we exploit some of the main advantages of unsupervised algorithms: (1) the interpretability of their output, in contrast with most supervised models, which behave as a black box and (2) their robustness across different corpora and domains. On the other hand, by introducing the concept of compositional operations and exploiting syntactic information in the form of universal dependencies, we tackle one of their main drawbacks: their rigidity on data that are structured differently depending on the language concerned. Experiments show an improvement both over existing unsupervised methods, and over state-of-the-art supervised models when evaluating outside their corpus of origin. Experiments also show how the same compositional operations can be shared across languages. The system is available at http://www.grupolys. org/software/UUUSA/.

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“…Two different languages in one application is presented in [20]. The Chinese and English tweets are studied in this approach.…”

Section: Related Workmentioning

confidence: 99%

Feature-based Sentiment Analysis for Slang Arabic Text

Abdallah¹,

Abo-Suaileek²

2019

IJACSA

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The increased number of Arab users on microblogging services who use Arabic language to write and read has triggered several researchers to study the posted data and discover the user's opinion and feelings to support decision making. In this paper, a sentiment analysis framework is presented for slang Arabic text. A new dataset with Jordanian dialect is presented. Numerous specific Arabic features are shown with their impact on slang Arabic Tweets. The new set of features consists of lexicon, writing style, grammatical and emotional features. Several experiments are conducted to test the performance of the proposed scheme. The new proposed scheme produces better results in comparison with others. The experiments show that the system performs well without translating the tweets to English or standard Arabic.

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A bilingual approach for conducting Chinese and English social media sentiment analysis

Cited by 42 publications

References 21 publications

Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams

Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers’ reactions to the launch of new products using Twitter streams

Universal, unsupervised (rule-based), uncovered sentiment analysis

Feature-based Sentiment Analysis for Slang Arabic Text

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