Songbo Tan

1.7k total citations · 1 hit paper
48 papers, 969 citations indexed

About

Songbo Tan is a scholar working on Artificial Intelligence, Information Systems and Statistical and Nonlinear Physics. According to data from OpenAlex, Songbo Tan has authored 48 papers receiving a total of 969 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Artificial Intelligence, 20 papers in Information Systems and 4 papers in Statistical and Nonlinear Physics. Recurrent topics in Songbo Tan's work include Text and Document Classification Technologies (24 papers), Sentiment Analysis and Opinion Mining (23 papers) and Topic Modeling (19 papers). Songbo Tan is often cited by papers focused on Text and Document Classification Technologies (24 papers), Sentiment Analysis and Opinion Mining (23 papers) and Topic Modeling (19 papers). Songbo Tan collaborates with scholars based in China, United Kingdom and Egypt. Songbo Tan's co-authors include Xueqi Cheng, Huifeng Tang, Qiong Wu, Yuefen Wang, Gaowei Wu, Hongbo Xu, Moustafa Ghanem, Bin Wang, Xiaochun Yun and Zheng Lin and has published in prestigious journals such as PLoS ONE, Expert Systems with Applications and Polymer Composites.

In The Last Decade

Songbo Tan

47 papers receiving 879 citations

Hit Papers

A survey on sentiment detection of reviews 2009 2026 2014 2020 2009 100 200 300

Peers

Songbo Tan
Songbo Tan
Citations per year, relative to Songbo Tan Songbo Tan (= 1×) peers Mauro Dragoni

Countries citing papers authored by Songbo Tan

Since Specialization
Citations

This map shows the geographic impact of Songbo Tan'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 Songbo Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Songbo Tan more than expected).

Fields of papers citing papers by Songbo Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Songbo Tan. 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 Songbo Tan. The network helps show where Songbo Tan may publish in the future.

Co-authorship network of co-authors of Songbo Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Songbo Tan. A scholar is included among the top collaborators of Songbo Tan 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 Songbo Tan. Songbo Tan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
3.
Lin, Zheng, et al.. (2014). Make It Possible: Multilingual Sentiment Analysis without Much Prior Knowledge. 79–86. 17 indexed citations
4.
Lin, Zheng, et al.. (2013). Cross-Language Opinion Lexicon Extraction Using Mutual-Reinforcement Label Propagation. PLoS ONE. 8(11). e79294–e79294. 9 indexed citations
5.
Lin, Zheng, Songbo Tan, & Xueqi Cheng. (2012). A Fast and Accurate Method for Bilingual Opinion Lexicon Extraction. 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. 50–57. 1 indexed citations
6.
Lin, Zheng, et al.. (2012). Effective and efficient?. 1542–1546. 2 indexed citations
7.
Tan, Songbo, et al.. (2012). Towards jointly extracting aspects and aspect-specific sentiment knowledge. 1895–1899. 22 indexed citations
8.
Tan, Songbo & Yuefen Wang. (2011). Weighted SCL model for adaptation of sentiment classification. Expert Systems with Applications. 38(8). 10524–10531. 9 indexed citations
9.
Wu, Qiong, et al.. (2010). MIEA: a Mutual Iterative Enhancement Approach for Cross-Domain Sentiment Classification. International Conference on Computational Linguistics. 1327–1335. 2 indexed citations
10.
Tan, Songbo, et al.. (2010). Extended Domain Model Based Named Attribute Extraction. 47(9). 1567. 1 indexed citations
11.
Tan, Songbo, et al.. (2009). A New Method to Compute Semantic Orientation. 46(10). 1713. 7 indexed citations
12.
Tang, Huifeng, Songbo Tan, & Xueqi Cheng. (2009). A survey on sentiment detection of reviews. Expert Systems with Applications. 36(7). 10760–10773. 315 indexed citations breakdown →
13.
Tan, Songbo, Yuefen Wang, & Xueqi Cheng. (2008). Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. 743–744. 60 indexed citations
14.
Cao, Donglin, et al.. (2007). Experiments in Trec 2007 Blog Opinion Task at CAS-ICT. Text REtrieval Conference. 2 indexed citations
15.
Tan, Songbo, Gaowei Wu, Huifeng Tang, & Xueqi Cheng. (2007). A novel scheme for domain-transfer problem in the context of sentiment analysis. 979–982. 65 indexed citations
16.
Tan, Songbo & Yuefen Wang. (2007). Combining error-correcting output codes and model-refinement for text categorization. 699–700. 2 indexed citations
17.
Shen, Huawei, et al.. (2006). Social Network Structure behind the Mailing Lists: ICT-IIIS at TREC 2006 Expert Finding Track. Text REtrieval Conference. 21 indexed citations
18.
Cao, Donglin, et al.. (2006). Combining Language Model with Sentiment Analysis for Opinion Retrieval of Blog-Post.. Text REtrieval Conference. 9 indexed citations
19.
Tan, Songbo, et al.. (2005). Efficient algorithms for attributes reduction problem. International journal of innovative computing, information & control. 1(4). 767–777. 5 indexed citations
20.
Tan, Songbo. (2005). Binary k‐nearest neighbor for text categorization. Online Information Review. 29(4). 391–399. 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.

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