M. Ghiassi

2.3k citations
30 papers · 1.7k · 1 hit paper · h-index 18

Impact in

Papers in

M. Ghiassi

27 papers receiving 1.6k citations

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

M. Ghiassi
Comparison fields: 5 of 143
  • Management Science and Operations Research 375
  • Artificial Intelligence 694
  • Ocean Engineering 173
  • Environmental Engineering 148
  • Information Systems 196
Replace Alistair Smith with:
Alistair Smith United Kingdom
Ming Yang China
Feng Shan China
Xiaohong Zhang China
Freerk A. Lootsma Netherlands
Francesco Archetti Italy
Pekka Malo Finland
Van‐Nam Huynh Japan
Mariano Luque Spain
A. Kaufmann
M. Ghiassi relative to Alistair Smith United Kingdom Alistair Smith's profile →
Citations per field
00.5×3.3×
Alistair Smith · 1×
Citations per year

Countries citing papers authored by M. Ghiassi

Since Specialization
Citations

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

Fields of papers citing papers by M. Ghiassi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 20 scholars most cited alongside M. Ghiassi, 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 M. Ghiassi Line = papers co-authored together M. Ghiassi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#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 2005134
5 2018110
6 201298
7 200482
8 201477
9 200376
10 201260
11 201659
12 201654
13 198638
14 198432
15 200932
16 201628
17 200923
18 198619
19 202216
20 199411

About M. Ghiassi

M. Ghiassi is a scholar working on Artificial Intelligence, Management Science and Operations Research, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment and Information Systems, having authored 30 papers that have together received 1.7k indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (5 papers), Neural Networks and Applications (5 papers), Text and Document Classification Technologies (5 papers), Energy Load and Power Forecasting (5 papers), Photovoltaic System Optimization Techniques (4 papers), Forecasting Techniques and Applications (4 papers), Stock Market Forecasting Methods (4 papers) and Advanced Text Analysis Techniques (3 papers). The work is most often cited by research in Management Science and Operations Research (375 citations), Artificial Intelligence (694 citations), Ocean Engineering (173 citations), Environmental Engineering (148 citations) and Information Systems (196 citations). M. Ghiassi has collaborated with scholars based in United States, Australia and Iran. Frequent co-authors include David Zimbra, H. Saidane, Sean Lee, Mohamed I. Dessouky, Brian Moon, Sean Bong Lee, Nitin Mantri, Hongfei Lü, Jiang Wu and Wayne J. Davis. Their work appears in journals such as Expert Systems with Applications, Computers & Industrial Engineering, International Journal of Forecasting, Neurocomputing and Journal of Water Resources Planning and Management.

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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