Learning Word Vectors for Sentiment Analysis

1.8k indexed citations

Abstract

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About

This paper, published in 2011, received 1.8k indexed citations. Written by Andrew L. Maas, Dan Huang, Andrew Y. Ng and Christopher Potts covering the research area of Artificial Intelligence. It is primarily cited by scholars working on Artificial Intelligence (1.7k citations), Computer Vision and Pattern Recognition (262 citations) and Information Systems (195 citations). Published in Meeting of the Association for Computational Linguistics.

In The Last Decade

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Countries where authors are citing Learning Word Vectors for Sentiment Analysis

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Citations

This map shows the geographic impact of Learning Word Vectors for Sentiment Analysis. 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 Learning Word Vectors for Sentiment Analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Learning Word Vectors for Sentiment Analysis more than expected).

Fields of papers citing Learning Word Vectors for Sentiment Analysis

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Learning Word Vectors for Sentiment Analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Learning Word Vectors for Sentiment Analysis.

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/w9442795.

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