Deep learning for sentiment analysis: A survey

1.1k indexed citations

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This paper, published in 2018, received 1.1k indexed citations. Written by Lei Zhang, Shuai Wang and Bing Liu covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (907 citations), Information Systems (182 citations) and Sociology and Political Science (144 citations). Published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery.

Countries where authors are citing Deep learning for sentiment analysis: A survey

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Citations

This map shows the geographic impact of Deep learning for sentiment analysis: A survey. 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 Deep learning for sentiment analysis: A survey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep learning for sentiment analysis: A survey more than expected).

Fields of papers citing Deep learning for sentiment analysis: A survey

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Deep learning for sentiment analysis: A survey. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep learning for sentiment analysis: A survey.

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/10.1002/widm.1253.

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