Erik Cambria
Impact in
- Artificial Intelligence top 0.01%
- Sentiment Analysis and Opinion Mining
- Topic Modeling
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Natural Language Processing Techniques
- Experimental and Cognitive Psychology top 0.05%
- Emotion and Mood Recognition
Papers in
-
- Sentiment Analysis and Opinion Mining 248
- Topic Modeling 222
- Advanced Text Analysis Techniques 178
- Natural Language Processing Techniques 74
- Text and Document Classification Technologies 51
-
- Emotion and Mood Recognition 60
- Co-authors
- Soujanya Poria (65 shared papers)Amir Hussain (93 shared papers)Devamanyu Hazarika (11 shared papers)Alexander Gelbukh (20 shared papers)Tom Young (8 shared papers)Bebo White (5 shared papers)Catherine Havasi (12 shared papers)Louis‐Philippe Morency (7 shared papers)
- Journals
- Information Fusion (35 papers)Cognitive Computation (34 papers)IEEE Intelligent Systems (22 papers)Knowledge-Based Systems (20 papers)Neurocomputing (17 papers)
- Partner nations
- SingaporeChinaUnited Kingdom
In The Last Decade
Erik Cambria
507 papers receiving 36.5k citations
Erik Cambria's Hit Papers
Peers
Comparison fields: 5 of 214
- Artificial Intelligence 27.9k
- Experimental and Cognitive Psychology 5.5k
- Management Science and Operations Research 2.6k
- Computer Vision and Pattern Recognition 4.3k
- Signal Processing 2.2k
Countries citing papers authored by Erik Cambria
This map shows the geographic impact of Erik Cambria'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 Erik Cambria with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erik Cambria more than expected).
Fields of papers citing papers by Erik Cambria
This network shows the impact of papers produced by Erik Cambria. 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 Erik Cambria. The network helps show where Erik Cambria may publish in the future.
Co-authors
The 25 scholars most cited alongside Erik Cambria, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 535 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Recent Trends in Deep Learning Based Natural Language Processing [Review Article] Hit paper breakdown → | 2018 | 2229 |
| 2 | A Survey on Knowledge Graphs: Representation, Acquisition, and Applications Hit paper breakdown → | 2021 | 1656 |
| 3 | A review of affective computing: From unimodal analysis to multimodal fusion Hit paper breakdown → | 2017 | 1077 |
| 4 | Affective Computing and Sentiment Analysis Hit paper breakdown → | 2016 | 1012 |
| 5 | New Avenues in Opinion Mining and Sentiment Analysis Hit paper breakdown → | 2013 | 956 |
| 6 | Deep Learning--based Text Classification Hit paper breakdown → | 2021 | 940 |
| 7 | Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article] Hit paper breakdown → | 2014 | 768 |
| 8 | Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph Hit paper breakdown → | 2018 | 768 |
| 9 | Aspect extraction for opinion mining with a deep convolutional neural network Hit paper breakdown → | 2016 | 672 |
| 10 | Context-Dependent Sentiment Analysis in User-Generated Videos Hit paper breakdown → | 2017 | 596 |
| 11 | ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis Hit paper breakdown → | 2020 | 571 |
| 12 | Memory Fusion Network for Multi-view Sequential Learning Hit paper breakdown → | 2018 | 562 |
| 13 | DialogueRNN: An Attentive RNN for Emotion Detection in Conversations Hit paper breakdown → | 2019 | 537 |
| 14 | Deep Learning-Based Document Modeling for Personality Detection from Text Hit paper breakdown → | 2017 | 525 |
| 15 | Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM Hit paper breakdown → | 2018 | 477 |
| 16 | Convolutional MKL Based Multimodal Emotion Recognition and Sentiment Analysis Hit paper breakdown → | 2016 | 461 |
| 17 | Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions Hit paper breakdown → | 2022 | 439 |
| 18 | Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks Hit paper breakdown → | 2021 | 398 |
| 19 | Fusing audio, visual and textual clues for sentiment analysis from multimodal content Hit paper breakdown → | 2015 | 392 |
| 20 | Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis Hit paper breakdown → | 2015 | 385 |
About Erik Cambria
Erik Cambria is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Computer Vision and Pattern Recognition, Management Science and Operations Research and Social Psychology, having authored 535 papers that have together received 37.8k indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (248 papers), Topic Modeling (222 papers), Advanced Text Analysis Techniques (178 papers), Natural Language Processing Techniques (74 papers), Emotion and Mood Recognition (60 papers), Text and Document Classification Technologies (51 papers), Stock Market Forecasting Methods (37 papers) and Complex Network Analysis Techniques (27 papers). The work is most often cited by research in Artificial Intelligence (27.9k citations), Experimental and Cognitive Psychology (5.5k citations), Management Science and Operations Research (2.6k citations), Computer Vision and Pattern Recognition (4.3k citations) and Signal Processing (2.2k citations). Erik Cambria has collaborated with scholars based in Singapore, China and United Kingdom. Frequent co-authors include Soujanya Poria, Amir Hussain, Devamanyu Hazarika, Alexander Gelbukh, Tom Young, Bebo White, Catherine Havasi, Louis‐Philippe Morency, Navonil Majumder and Shaoxiong Ji. Their work appears in journals such as Information Fusion, Cognitive Computation, IEEE Intelligent Systems, Knowledge-Based Systems and Neurocomputing.
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.