H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 90 Citations 28,444 305 World Ranking 263 National Ranking 5

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Natural language processing

His scientific interests lie mostly in Sentiment analysis, Artificial intelligence, Natural language processing, Data science and Natural language. His work deals with themes such as Affect, Affective computing, The Internet, Social media and Semantics, which intersect with Sentiment analysis. In his study, Unstructured data is strongly linked to Machine learning, which falls under the umbrella field of Artificial intelligence.

The concepts of his Natural language processing study are interwoven with issues in Context, Recurrent neural network, Conversation and Commonsense knowledge, Knowledge representation and reasoning. The study incorporates disciplines such as Sentic computing, Human intelligence, Knowledge base and Knowledge-based systems in addition to Data science. His research integrates issues of Predictive analytics, Semantic computing, World Wide Web, Semantic Web and Computational linguistics in his study of Natural language.

His most cited work include:

  • Recent Trends in Deep Learning Based Natural Language Processing (804 citations)
  • New Avenues in Opinion Mining and Sentiment Analysis (712 citations)
  • Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (666 citations)

What are the main themes of his work throughout his whole career to date?

Erik Cambria spends much of his time researching Artificial intelligence, Sentiment analysis, Natural language processing, Data science and Machine learning. His Artificial intelligence study frequently draws connections between adjacent fields such as Context. His Sentiment analysis research integrates issues from Affective computing, Commonsense knowledge, Social media, Semantics and Natural language.

His research in Commonsense knowledge intersects with topics in Commonsense reasoning, Cognitive science and Human–computer interaction. His Natural language processing research incorporates elements of Artificial neural network, Word and Categorization. Data science and Field are two areas of study in which he engages in interdisciplinary research.

He most often published in these fields:

  • Artificial intelligence (57.26%)
  • Sentiment analysis (54.35%)
  • Natural language processing (37.20%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (57.26%)
  • Sentiment analysis (54.35%)
  • Natural language processing (37.20%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Artificial intelligence, Sentiment analysis, Natural language processing, Deep learning and Data science. Erik Cambria combines topics linked to Machine learning with his work on Artificial intelligence. His Sentiment analysis study integrates concerns from other disciplines, such as Context, Arousal, Valence, Categorization and Affective computing.

His Natural language processing research is multidisciplinary, relying on both Commonsense knowledge, SemEval, Time expression and Representation. His Deep learning research includes elements of Artificial neural network, Cluster analysis, Question answering, Personality and Feature extraction. Erik Cambria interconnects Social network analysis, Social network, Social media and Knowledge extraction in the investigation of issues within Data science.

Between 2019 and 2021, his most popular works were:

  • A Survey on Knowledge Graphs: Representation, Acquisition and Applications (115 citations)
  • SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis (67 citations)
  • Deep Learning Based Text Classification: A Comprehensive Review (62 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • The Internet

Erik Cambria mainly investigates Sentiment analysis, Artificial intelligence, Deep learning, Natural language processing and Data science. His Sentiment analysis research is multidisciplinary, relying on both Context, Computational intelligence, Categorization, Anaphora and Machine translation. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Order.

His Deep learning study combines topics in areas such as Classifier, Artificial neural network, Utterance, Set and Convolutional neural network. His work in the fields of Natural language processing, such as Cross lingual, intersects with other areas such as Population, Frequency, Baseline and Binary case. Erik Cambria interconnects Social network analysis, Affective computing and Knowledge extraction in the investigation of issues within Data science.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

New Avenues in Opinion Mining and Sentiment Analysis

E. Cambria;B. Schuller;Yunqing Xia;C. Havasi.
IEEE Intelligent Systems (2013)

1103 Citations

Recent Trends in Deep Learning Based Natural Language Processing

Tom Young;Devamanyu Hazarika;Soujanya Poria;Erik Cambria.
arXiv: Computation and Language (2017)

711 Citations

Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]

Erik Cambria;Bebo White.
IEEE Computational Intelligence Magazine (2014)

706 Citations

Affective Computing and Sentiment Analysis

Erik Cambria.
IEEE Intelligent Systems (2016)

680 Citations

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Tom Young;Devamanyu Hazarika;Soujanya Poria;Erik Cambria.
IEEE Computational Intelligence Magazine (2018)

547 Citations

Aspect extraction for opinion mining with a deep convolutional neural network

Soujanya Poria;Erik Cambria;Alexander Gelbukh.
Knowledge Based Systems (2016)

489 Citations

A review of affective computing

Soujanya Poria;Erik Cambria;Rajiv Bajpai;Amir Hussain.
Information Fusion (2017)

456 Citations

Jumping NLP Curves: A Review of Natural Language Processing Research

Erik Cambria;Bebo White.
(2014)

407 Citations

SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis

Erik Cambria;Daniel Olsher;Dheeraj Rajagopal.
national conference on artificial intelligence (2014)

376 Citations

Fusing audio, visual and textual clues for sentiment analysis from multimodal content

Soujanya Poria;Erik Cambria;Newton Howard;Guang-Bin Huang.
Neurocomputing (2016)

338 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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