Support vector machines and Word2vec for text classification with semantic features

By Joseph Lilleberg, Yun Zhu, Yanqing Zhang. IEEE International Conference on Cognitive Informatics and Cognitive Computing, 2015

446 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/63d1c2ce22bb9a1f1cd0136fd53323bb174891ce

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ugur Kursuncu
2. Manas Gaur
3. Amit P. Sheth
4. Krishnaprasad Thirunarayan
5. Ismailcem Budak Arpinar
6. Dejan Ravšelj
7. Aleksander Aristovnik
8. Lan Umek

In Information Systems

1. Ugur Kursuncu
2. Manas Gaur
3. Amit P. Sheth
4. Dejan Ravšelj
5. Aleksander Aristovnik
6. Lan Umek

Citations in the corpus

• What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding (2018, background): “In recent studies [19], [20], the researchers have shown that word embedding models perform well over short texts.”
• A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape (2020, methodology): “text [50] was used with the following parameters: sublinear term frequency (tf) scaling, smooth inverse document frequency (idf) weights, unicode transformation format (utf)-8 encoding, l2 norm regularization, min data frequency = 1, max data frequency = 10.”