David Zimbra

1.9k total citations · 1 hit paper
18 papers, 1.4k citations indexed

About

David Zimbra is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Sociology and Political Science. According to data from OpenAlex, David Zimbra has authored 18 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 6 papers in Statistical and Nonlinear Physics and 5 papers in Sociology and Political Science. Recurrent topics in David Zimbra's work include Sentiment Analysis and Opinion Mining (9 papers), Advanced Text Analysis Techniques (8 papers) and Complex Network Analysis Techniques (6 papers). David Zimbra is often cited by papers focused on Sentiment Analysis and Opinion Mining (9 papers), Advanced Text Analysis Techniques (8 papers) and Complex Network Analysis Techniques (6 papers). David Zimbra collaborates with scholars based in United States and China. David Zimbra's co-authors include M. Ghiassi, H. Saidane, Hsinchun Chen, Hsinchun Chen, Ahmed Abbasi, Sean Lee, Daniel Zeng, Jay F. Nunamaker, Shan Jiang and Hsinchun Chen and has published in prestigious journals such as Expert Systems with Applications, Journal of Management Information Systems and Decision Support Systems.

In The Last Decade

David Zimbra

18 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network

2013 344 citations M. Ghiassi, David Zimbra et al. Expert Systems with Applications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David Zimbra United States	12	698	307	284	279	226	18	1.4k
M. Ghiassi United States	18	694 1.0×	375 1.2×	182 0.6×	327 1.2×	196 0.9×	30	1.7k
Qing Zhu China	18	213 0.3×	204 0.7×	279 1.0×	268 1.0×	85 0.4×	81	1.2k
Ru‐xin Nie China	19	340 0.5×	811 2.6×	138 0.5×	46 0.2×	76 0.3×	35	1.2k
Jarosław Jankowski Poland	16	132 0.2×	362 1.2×	200 0.7×	37 0.1×	103 0.5×	62	1.1k
Nonita Sharma India	16	307 0.4×	153 0.5×	48 0.2×	126 0.5×	119 0.5×	99	928
Fernando Jiménez Spain	18	462 0.7×	124 0.4×	99 0.3×	96 0.3×	68 0.3×	87	1.2k
Adriano C. M. Pereira Brazil	17	597 0.9×	634 2.1×	95 0.3×	278 1.0×	337 1.5×	116	1.6k
Bo K. Wong Hong Kong	13	283 0.4×	514 1.7×	91 0.3×	78 0.3×	134 0.6×	20	1.3k
Li Cai China	12	181 0.3×	238 0.8×	51 0.2×	108 0.4×	264 1.2×	59	1.2k

Countries citing papers authored by David Zimbra

Since Specialization

Citations

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

Fields of papers citing papers by David Zimbra

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Zimbra

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

All Works

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18 of 18 papers shown

Zimbra, David, Ahmed Abbasi, Daniel Zeng, & Hsinchun Chen. (2018). The State-of-the-Art in Twitter Sentiment Analysis. ACM Transactions on Management Information Systems. 9(2). 1–29. 153 indexed citations

Zimbra, David, Kumar R. Sarangee, & Rupinder P. Jindal. (2017). Movie aspects, tweet metrics, and movie revenues: The influence of iOS vs. Android. Decision Support Systems. 102. 98–109. 12 indexed citations

Zimbra, David, M. Ghiassi, & Sean Lee. (2016). Brand-Related Twitter Sentiment Analysis Using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks. 1930–1938. 54 indexed citations

Ghiassi, M., David Zimbra, & Sean Lee. (2016). Targeted Twitter Sentiment Analysis for Brands Using Supervised Feature Engineering and the Dynamic Architecture for Artificial Neural Networks. Journal of Management Information Systems. 33(4). 1034–1058. 59 indexed citations

Zimbra, David, Hsinchun Chen, & Robert F. Lusch. (2015). Stakeholder Analyses of Firm-Related Web Forums. ACM Transactions on Management Information Systems. 6(1). 1–38. 8 indexed citations

Benjamin, Victor, Hsinchun Chen, & David Zimbra. (2014). Bridging the virtual and real: The relationship between web content, linkage, and geographical proximity of social movements. Journal of the Association for Information Science and Technology. 65(11). 2210–2222. 10 indexed citations

Jiang, Shan, Hsinchun Chen, Jay F. Nunamaker, & David Zimbra. (2014). Analyzing firm-specific social media and market: A stakeholder-based event analysis framework. Decision Support Systems. 67. 30–39. 38 indexed citations

Ghiassi, M., et al.. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications. 40(16). 6266–6282. 344 indexed citations breakdown →

Zimbra, David & Hsinchun Chen. (2012). Scalable sentiment classification across multiple Dark Web Forums. 9. 78–83. 8 indexed citations

10.

Chen, Hsinchun & David Zimbra. (2012). Stakeholder and sentiment analysis in web forums. 1 indexed citations

11.

Chen, Hsinchun, et al.. (2011). The Geopolitical Web: Assessing societal risk in an uncertain world. 60–64. 3 indexed citations

12.

Yong, Liu, Yubo Chen, Robert F. Lusch, et al.. (2010). User-Generated Content on Social Media: Predicting Market Success with Online Word-of-Mouth. SSRN Electronic Journal. 21 indexed citations

13.

Zimbra, David, Ahmed Abbasi, & Hsinchun Chen. (2010). A Cyber-archaeology Approach to Social Movement Research: Framework and Case Study. Journal of Computer-Mediated Communication. 16(1). 48–70. 26 indexed citations

14.

Chen, Hsinchun & David Zimbra. (2010). AI and Opinion Mining. IEEE Intelligent Systems. 25(3). 74–80. 135 indexed citations

15.

Zimbra, David, Tianjun Fu, & Xin Li. (2009). ASSESSING PUBLIC OPINIONS THROUGH WEB 2.0: A CASE STUDY ON WAL-MART. Journal of the Association for Information Systems. 67. 9 indexed citations

16.

Ghiassi, M., David Zimbra, & H. Saidane. (2008). Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model. Journal of Water Resources Planning and Management. 134(2). 138–146. 203 indexed citations

17.

Ghiassi, M., David Zimbra, & H. Saidane. (2005). Medium term system load forecasting with a dynamic artificial neural network model. Electric Power Systems Research. 76(5). 302–316. 134 indexed citations

18.

Ghiassi, M., H. Saidane, & David Zimbra. (2004). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting. 21(2). 341–362. 189 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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