David Zimbra

1.9k citations
18 papers · 1.4k · 1 hit paper · h-index 12

Impact in

Papers in

David Zimbra

18 papers receiving 1.3k citations

David Zimbra's Hit Papers

Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network 2013 · 344 citations
3440+4+8Years since publication100200300

Peers

David Zimbra
Comparison fields: 5 of 99
  • Management Science and Operations Research 307
  • Artificial Intelligence 698
  • Communication 72
  • Information Systems 226
  • Ocean Engineering 146
Replace M. Ghiassi with:
M. Ghiassi United States
Jarosław Jankowski Poland
Stefan Dietze Germany
Qiang Wei China
Ru‐xin Nie China
Jin‐Xing Hao China
Bo K. Wong Hong Kong
Qing Zhu China
Shuo Xu China
David Zimbra relative to M. Ghiassi United States M. Ghiassi's profile →
Citations per field
00.5×1.5×1.9×
M. Ghiassi · 1×
Citations per year

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-authors

The 20 scholars most cited alongside David Zimbra, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with David Zimbra Line = papers co-authored together David Zimbra links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1
Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network
Hit paper breakdown →
2013344
2 2008203
3 2004189
4 2018153
5 2010135
6 2005134
7 201659
8 201654
9 201438
10 201026
11
User-Generated Content on Social Media: Predicting Market Success with Online Word-of-Mouth
201021
12 201712
13 201410
14
ASSESSING PUBLIC OPINIONS THROUGH WEB 2.0: A CASE STUDY ON WAL-MART
20099
15 20128
16 20158
17 20113
18
Stakeholder and sentiment analysis in web forums
20121

About David Zimbra

David Zimbra is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics, Sociology and Political Science, Information Systems and Management Science and Operations Research, having authored 18 papers that have together received 1.4k indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (9 papers), Advanced Text Analysis Techniques (8 papers), Complex Network Analysis Techniques (6 papers), Digital Marketing and Social Media (4 papers), Digital Games and Media (3 papers), Topic Modeling (3 papers), Spam and Phishing Detection (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Management Science and Operations Research (307 citations), Artificial Intelligence (698 citations), Communication (72 citations), Information Systems (226 citations) and Ocean Engineering (146 citations). David Zimbra has collaborated with scholars based in United States and China. Frequent co-authors include M. Ghiassi, H. Saidane, Hsinchun Chen, Hsinchun Chen, Ahmed Abbasi, Daniel Zeng, Sean Lee, Hsinchun Chen, Shan Jiang and Jay F. Nunamaker. Their work appears in journals such as Decision Support Systems, ACM Transactions on Management Information Systems, Journal of the Association for Information Systems, International Journal of Forecasting and Journal of the Association for Information Science and Technology.

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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