What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Natural language processing
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Natural language processing, Sentiment analysis, Lexicon and Word.
His work deals with themes such as Social media and Stance detection, which intersect with Artificial intelligence.
His study ties his expertise on Valence together with the subject of Natural language processing.
His Sentiment analysis study combines topics in areas such as Crowdsourcing, Data mining and Phrase.
Saif M. Mohammad combines subjects such as Context, Information retrieval and Set with his study of Phrase.
His biological study spans a wide range of topics, including Affect, WordNet, Variety and Association.
His most cited work include:
- CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON (987 citations)
- Sentiment analysis of short informal texts (554 citations)
- Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon (514 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Natural language processing, Sentiment analysis, Lexicon and Word.
His research integrates issues of Crowdsourcing, Social media and Association in his study of Artificial intelligence.
His Natural language processing research is multidisciplinary, incorporating elements of SemEval, Stance detection and Arabic.
His Sentiment analysis study integrates concerns from other disciplines, such as Valence, Affect, Data science and Set.
Saif M. Mohammad usually deals with Lexicon and limits it to topics linked to Support vector machine and Social media mining.
His research in Word tackles topics such as Context which are related to areas like Supervised learning.
He most often published in these fields:
- Artificial intelligence (70.68%)
- Natural language processing (66.17%)
- Sentiment analysis (24.06%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (70.68%)
- Natural language processing (66.17%)
- Cognitive psychology (8.27%)
In recent papers he was focusing on the following fields of study:
Saif M. Mohammad mostly deals with Artificial intelligence, Natural language processing, Cognitive psychology, Sentiment analysis and Citation.
His Visualization and Phrase study, which is part of a larger body of work in Artificial intelligence, is frequently linked to Field, Demographic analysis and Work, bridging the gap between disciplines.
The Lexicon research Saif M. Mohammad does as part of his general Natural language processing study is frequently linked to other disciplines of science, such as Dashboard, therefore creating a link between diverse domains of science.
His research integrates issues of Utterance, TRACE and Narrative in his study of Cognitive psychology.
The concepts of his Sentiment analysis study are interwoven with issues in Affect and Data science.
Within one scientific family, Saif M. Mohammad focuses on topics pertaining to Social media under Data science, and may sometimes address concerns connected to Crowdsourcing.
Between 2017 and 2021, his most popular works were:
- SemEval-2018 Task 1: Affect in Tweets (242 citations)
- Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words (129 citations)
- Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems (118 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Natural language processing
- Machine learning
Saif M. Mohammad mainly investigates Artificial intelligence, Natural language processing, Cognitive psychology, Affect and Sentiment analysis.
His Artificial intelligence study focuses on SemEval in particular.
His Natural language processing research includes themes of Labeled data, Arabic, Emotion classification and Citation.
State, Web search engine, Publishing and History are fields of study that overlap with his Citation research.
His studies in Cognitive psychology integrate themes in fields like Bag-of-words model, Bigram, F1 score and Personality.
His research combines Affective computing and Sentiment analysis.
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