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.
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