Huina Mao
Artificial Intelligence · with Bollen · 2009–2011
A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 74 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 21 citations into it; #3,703 of 35,345 by trust here, #3,440 by citations
- Information Systems — 8 citations into it
- Statistical and Nonlinear Physics — 7 citations into it
- Natural Language Processing — 5 citations into it
Whom Huina Mao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Bo Pang
- Ellen Riloff
- Theresa Wilson
- Janyce Wiebe
- Zhiyuan Cheng
- Michael Gamon
- Tetsuya Nasukawa
- Jeonghee Yi
- Lillian Lee
- Brendan T. O'Connor
Who trusts Huina Mao overall
- Saif M. Mohammad
- Pollyanna Gonçalves
- Manoochehr Ghiassi
- Ingrid E. Fisher
- Mohamed M. Mostafa
- Lin Yue
- Qing Li
- Emre Kıcıman
- Daniel Gayo-Avello
- Matheus Araújo
Papers from Huina Mao's perspective
Ordered by how strongly the researchers Huina Mao trusts endorse each paper. Same corpus, a different order for every researcher.
- Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis
- Sentiment analysis: capturing favorability using natural language processing
- Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques (Third IEEE International Conference on Data Mining)
- Learning Extraction Patterns for Subjective Expressions
- Sentiment classification on customer feedback data: noisy data, large feature vectors, and the role of linguistic analysis (International Conference on Computational Linguistics)
- Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales
- ARSA: a sentiment-aware model for predicting sales performance using blogs (Annual International ACM SIGIR Conference on Research and Development in Information Retrieval)
- Learning subjective nouns using extraction pattern bootstrapping
- An algorithm for suffix stripping
- Capturing Global Mood Levels using Blog Posts
Their papers
- Twitter mood predicts the stock market (2010) — 5,439 citations
- Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena (2009) — 1,236 citations
- Loose tweets: an analysis of privacy leaks on twitter (2011) — 214 citations