Roy Bar-Haim

1.1k citations
27 papers · 601 · h-index 12

Impact in

Papers in

    • Topic Modeling 18
    • Natural Language Processing Techniques 14
    • Sentiment Analysis and Opinion Mining 8
    • Advanced Text Analysis Techniques 7
    • Semantic Web and Ontologies 6

Roy Bar-Haim

26 papers receiving 546 citations

Peers

Roy Bar-Haim
Comparison fields: 5 of 42
  • Artificial Intelligence 533
  • Management Science and Operations Research 69
  • Information Systems 102
  • Finance 27
  • General Social Sciences 7
Replace Hen‐Hsen Huang with:
Hen‐Hsen Huang Taiwan
Amir Feder Israel
Jingjing Xu China
Chun-Neng Huang United States
Benjamin Rosenfeld Israel
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Citations per field
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Citations per year

Countries citing papers authored by Roy Bar-Haim

Since Specialization
Citations

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

Fields of papers citing papers by Roy Bar-Haim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roy Bar-Haim. 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 Roy Bar-Haim. The network helps show where Roy Bar-Haim may publish in the future.

Co-authors

The 25 scholars most cited alongside Roy Bar-Haim, 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 Roy Bar-Haim Line = papers co-authored together Roy Bar-Haim links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201791
2
Identifying and Following Expert Investors in Stock Microblogs
201182
3 201181
4
Semantic inference at the lexical-syntactic level
200749
5 200734
6 200534
7
Contextual Preferences
200833
8 200531
9 200726
10
Efficient Semantic Deduction and Approximate Matching over Compact Parse Forests.
200824
11 201719
12 200915
13
Addressing Discourse and Document Structure in the RTE Search Task.
200911
14 201410
15 201910
16 20088
17 20167
18
LEARNING SENTIMENT COMPOSITION FROM SENTIMENT LEXICONS
20187
19 20186
20
Claims on demand -- an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora
20145

About Roy Bar-Haim

Roy Bar-Haim is a scholar working on Artificial Intelligence, Information Systems, Communication, Computer Networks and Communications and Signal Processing, having authored 27 papers that have together received 601 indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (14 papers), Sentiment Analysis and Opinion Mining (8 papers), Advanced Text Analysis Techniques (7 papers), Semantic Web and Ontologies (6 papers), Wikis in Education and Collaboration (2 papers), Network Security and Intrusion Detection (2 papers) and Stock Market Forecasting Methods (2 papers). The work is most often cited by research in Artificial Intelligence (533 citations), Management Science and Operations Research (69 citations), Information Systems (102 citations), Finance (27 citations) and General Social Sciences (7 citations). Roy Bar-Haim has collaborated with scholars based in Israel, United States and India. Frequent co-authors include Ido Dagan, Ronen Feldman, Moshe Fresko, Idan Szpektor, Noam Slonim, Yoad Winter, Khalil Sima’an, Benjamin Rosenfeld, Eyal Shnarch and Amrita Saha. Their work appears in journals such as Theory and applications of categories, Natural Language Engineering, Journal of Artificial Intelligence Research, Language Resources and Evaluation and Lecture notes in computer science.

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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