Liang-Chih Yu

2.1k total citations
56 papers, 1.4k citations indexed

About

Liang-Chih Yu is a scholar working on Artificial Intelligence, Social Psychology and Molecular Biology. According to data from OpenAlex, Liang-Chih Yu has authored 56 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Artificial Intelligence, 7 papers in Social Psychology and 6 papers in Molecular Biology. Recurrent topics in Liang-Chih Yu's work include Sentiment Analysis and Opinion Mining (30 papers), Topic Modeling (29 papers) and Advanced Text Analysis Techniques (29 papers). Liang-Chih Yu is often cited by papers focused on Sentiment Analysis and Opinion Mining (30 papers), Topic Modeling (29 papers) and Advanced Text Analysis Techniques (29 papers). Liang-Chih Yu collaborates with scholars based in Taiwan, China and Hong Kong. Liang-Chih Yu's co-authors include K. Robert Lai, Jin Wang, Xuejie Zhang, Xuejie Zhang, Jheng-Long Wu, Pei‐Chann Chang, Chien‐Lung Chan, Chung‐Hsien Wu, Fong-Lin Jang and Lung‐Hao Lee and has published in prestigious journals such as IEEE Access, International Journal of Production Economics and Artificial Intelligence.

In The Last Decade

Liang-Chih Yu

54 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Liang-Chih Yu Taiwan	20	924	166	142	122	121	56	1.4k
Isidoros Perikos Greece	16	522 0.6×	184 1.1×	187 1.3×	69 0.6×	77 0.6×	89	1.1k
Ioannis Hatzilygeroudis Greece	21	872 0.9×	343 2.1×	250 1.8×	79 0.6×	78 0.6×	142	1.6k
Thanassis Tiropanis United Kingdom	16	273 0.3×	248 1.5×	117 0.8×	35 0.3×	103 0.9×	91	846
Jimmy H. M. Lee Hong Kong	14	349 0.4×	118 0.7×	142 1.0×	33 0.3×	70 0.6×	95	1.1k
Barry G. Silverman United States	20	428 0.5×	161 1.0×	52 0.4×	61 0.5×	220 1.8×	98	1.3k
Derwin Suhartono Indonesia	19	706 0.8×	352 2.1×	33 0.2×	90 0.7×	113 0.9×	170	1.4k
Anbang Xu United States	14	544 0.6×	165 1.0×	186 1.3×	62 0.5×	285 2.4×	45	1.2k
Mihaela Cocea United Kingdom	20	671 0.7×	325 2.0×	201 1.4×	32 0.3×	56 0.5×	75	1.2k
Inioluwa Deborah Raji United States	12	661 0.7×	203 1.2×	109 0.8×	36 0.3×	183 1.5×	16	1.5k
Etsuko Ishii Hong Kong	5	916 1.0×	185 1.1×	71 0.5×	26 0.2×	110 0.9×	11	1.6k

Countries citing papers authored by Liang-Chih Yu

Since Specialization

Citations

This map shows the geographic impact of Liang-Chih Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Liang-Chih Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang-Chih Yu more than expected).

Fields of papers citing papers by Liang-Chih Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liang-Chih Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Liang-Chih Yu. The network helps show where Liang-Chih Yu may publish in the future.

Co-authorship network of co-authors of Liang-Chih Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Liang-Chih Yu. A scholar is included among the top collaborators of Liang-Chih Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Liang-Chih Yu. Liang-Chih Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wang, Jin, et al.. (2024). Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis. 4777–4788. 7 indexed citations

Lee, Lung‐Hao, Liang-Chih Yu, Suge Wang, & Jian Liao. (2024). Overview of the SIGHAN 2024 shared task for Chinese dimensional aspect-based sentiment analysis. 165–174. 4 indexed citations

Zhang, You, Jin Wang, Liang-Chih Yu, Dan Xu, & Xuejie Zhang. (2023). Domain Generalization via Switch Knowledge Distillation for Robust Review Representation. 12812–12826. 2 indexed citations

Wang, Jin, et al.. (2023). Decoupled variational autoencoder with interactive attention for affective text generation. Engineering Applications of Artificial Intelligence. 123. 106447–106447. 6 indexed citations

Chen, Ruijun, et al.. (2023). Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 12653–12661. 10 indexed citations

Wang, Jin, et al.. (2022). Dual-Encoder Transformers with Cross-modal Alignment for Multimodal Aspect-based Sentiment Analysis. 414–423. 9 indexed citations

Zhang, You, Jin Wang, Liang-Chih Yu, & Xuejie Zhang. (2021). MA-BERT: Learning Representation by Incorporating Multi-Attribute Knowledge in Transformers. 2338–2343. 13 indexed citations

Wu, Jheng-Long, et al.. (2020). Identifying Emotion Labels From Psychiatric Social Texts Using a Bi-Directional LSTM-CNN Model. IEEE Access. 8. 66638–66646. 35 indexed citations

Yu, Liang-Chih, et al.. (2020). Sentiment Analysis for Investment Atmosphere Scoring. International Conference on Computational Linguistics. 275–289. 1 indexed citations

10.

Wu, Jheng-Long, et al.. (2019). Using an analogical reasoning framework to infer language patterns for negative life events. BMC Medical Informatics and Decision Making. 19(1). 173–173. 1 indexed citations

11.

Yu, Liang-Chih, Jin Wang, K. Robert Lai, & Xuejie Zhang. (2017). Refining Word Embeddings for Sentiment Analysis. 534–539. 100 indexed citations

12.

Yu, Liang-Chih, et al.. (2017). YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model. 238–242. 7 indexed citations

13.

Lee, Lung‐Hao, Bo‐Lin Lin, Liang-Chih Yu, & Yuen‐Hsien Tseng. (2016). The NTNU-YZU System in the AESW Shared Task: Automated Evaluation of Scientific Writing Using a Convolutional Neural Network. 122–129. 3 indexed citations

14.

Yu, Liang-Chih, et al.. (2016). YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network. 251–255. 2 indexed citations

15.

Yu, Liang-Chih, et al.. (2016). Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning. 11(1). 8–8. 100 indexed citations

16.

Yu, Liang-Chih, et al.. (2014). Identifying Emotion Labels from Psychiatric Social Texts Using Independent Component Analysis. International Conference on Computational Linguistics. 837–847. 8 indexed citations

17.

Wu, Jheng-Long, Liang-Chih Yu, & Pei‐Chann Chang. (2012). Detecting causality from online psychiatric texts using inter-sentential language patterns. BMC Medical Informatics and Decision Making. 12(1). 72–72. 13 indexed citations

18.

Yu, Liang-Chih, Chien‐Lung Chan, Chao-Cheng Lin, & I-Chun Lin. (2011). Mining association language patterns using a distributional semantic model for negative life event classification. Journal of Biomedical Informatics. 44(4). 509–518. 15 indexed citations

19.

Yu, Liang-Chih, Chung‐Hsien Wu, & Fong-Lin Jang. (2009). Psychiatric document retrieval using a discourse-aware model. Artificial Intelligence. 173(7-8). 817–829. 23 indexed citations

20.

Yu, Liang-Chih, et al.. (2007). Topic Analysis for Psychiatric Document Retrieval. Meeting of the Association for Computational Linguistics. 1024–1031. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact