See How Your Research Aligns
Create a free account to analyze your research alignment,
identify potential research gaps, and generate personalized
application documents for this professor.
Create Free Account
Arjun Mukherjee
Natural Language Processing (NLP)
Deceptive Opinion Spam Detection
Sentiment Analysis
Computational Social Science
Large Language Models
About
Dr. Mukherjee received his Ph.D. in 2014. His research spans data and web mining, natural language processing, large language models, computational social science, sentiment analysis, and opinion spam detection. His work has been published in leading venues such as ACL, EMNLP, KDD, WWW, ICWSM, and ICDM, and he presented a tutorial on deceptive opinion spam at ACL 2015. He has served on research grant panels and as a program committee member for numerous journals and conferences. His research has been supported by the U.S. National Science Foundation (NSF) and the U.S. Department of Defense (DoD).
Research Performance Summary
1998-2026
Active Research Span
First Recorded Paper
The Origins of The Elements From The Basis of Chemical Reactions In Massive Stars In Deep Space
Year:
1998
Citations:
0
Venue:
Monthly Notices of the Royal Astronomical Society 293
Latest Recorded Paper
Say Anything but This: When Tokenizer Betrays Reasoning in LLMs
Year:
2026
Citations:
0
Venue:
arXiv preprint arXiv:2601.14658
Last 10 Years Publication Activity
This timeline shows the professor's yearly publication activity.
Publication Venues and Collaboration
Journal, Conference, and Book Publication Breakdown
Top Coauthors
Mukherjee
Liu
Dragut
Research Impact by Period
Papers
7
Citations
8
Avg. Citations / Paper
1.1
H-Index
1
Papers
31
Citations
356
Avg. Citations / Paper
11.5
H-Index
10
Papers
34
Citations
1134
Avg. Citations / Paper
33.4
H-Index
15
Papers
27
Citations
6329
Avg. Citations / Paper
234.4
H-Index
22
Papers
1
Citations
0
Avg. Citations / Paper
0
H-Index
0
Contact and Professional Links
Detected Research Keywords
Demystifying Dimensions Source
Dimensions Source Code
Source Code Embeddings
What Yelp Fake
Yelp Fake Review
Fake Review Filter
Review Filter Might
Filter Might Doing
Sentiment Stock Prediction
Positive Unlabeled Learning