Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
Impact in
Classified as
- Authors
- Sida WangChristopher D. Manning
- Journal
- Meeting of the Association for Computational Linguistics
In The Last Decade
doi.org/w8888086 →Countries where authors are citing Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
This map shows the geographic impact of Baselines and Bigrams: Simple, Good Sentiment and Topic Classification. 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 Baselines and Bigrams: Simple, Good Sentiment and Topic Classification with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Baselines and Bigrams: Simple, Good Sentiment and Topic Classification more than expected).
Fields of papers citing Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
This network shows the impact of Baselines and Bigrams: Simple, Good Sentiment and Topic Classification. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Baselines and Bigrams: Simple, Good Sentiment and Topic Classification.
About Baselines and Bigrams: Simple, Good Sentiment and Topic Classification
This paper, published in 2012, received 628 indexed citations . Written by Sida Wang and Christopher D. Manning covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (556 citations), Information Systems (113 citations), Sociology and Political Science (49 citations), Computer Vision and Pattern Recognition (40 citations) and Management Science and Operations Research (38 citations). Published in Meeting of the Association for Computational Linguistics.
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.
This paper is also available at doi.org/w8888086.