Target-dependent Twitter Sentiment Classification
Impact in
Classified as
- Journal
- Meeting of the Association for Computational Linguistics
In The Last Decade
doi.org/w10384082 →Countries where authors are citing Target-dependent Twitter Sentiment Classification
This map shows the geographic impact of Target-dependent Twitter Sentiment 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 Target-dependent Twitter Sentiment Classification with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Target-dependent Twitter Sentiment Classification more than expected).
Fields of papers citing Target-dependent Twitter Sentiment Classification
This network shows the impact of Target-dependent Twitter Sentiment Classification. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Target-dependent Twitter Sentiment Classification.
About Target-dependent Twitter Sentiment Classification
This paper, published in 2011, received 578 indexed citations . Written by Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu and Tiejun Zhao covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (535 citations), Information Systems (136 citations), Sociology and Political Science (62 citations), Statistical and Nonlinear Physics (61 citations) and Communication (17 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/w10384082.