Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
Impact in
Classified as
- Journal
- International Conference on Computational Linguistics
In The Last Decade
doi.org/w46637901 →Countries where authors are citing Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
This map shows the geographic impact of Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. 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 Enhanced Sentiment Learning Using Twitter Hashtags and Smileys with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Enhanced Sentiment Learning Using Twitter Hashtags and Smileys more than expected).
Fields of papers citing Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
This network shows the impact of Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Enhanced Sentiment Learning Using Twitter Hashtags and Smileys.
About Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
This paper, published in 2010, received 451 indexed citations . Written by Dmitry Davidov, Oren Tsur and Ari Rappoport covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (391 citations), Information Systems (126 citations), Statistical and Nonlinear Physics (89 citations), Sociology and Political Science (54 citations) and Human-Computer Interaction (23 citations). Published in International Conference on 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/w46637901.