- author = "Iti Chaturvedi and Erik Cambria and Sandro Cavallari and Roy E. Welsch",
- title = "Genetic Programming for Domain Adaptation in Product Reviews",
- booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020",
- year = "2020",
- editor = "Yaochu Jin",
- pages = "paper id24673",
- address = "internet",
- month = "19-24 " # jul,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, Sentiment Analysis",
- isbn13 = "978-1-7281-6929-3",
-
URL = "
http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_WCCI_2020/CEC/Papers/E-24673.pdf",
-
DOI = "
doi:10.1109/CEC48606.2020.9185713",
- size = "8 pages",
- abstract = "There is a large variety of products sold online and the websites are in several languages. Hence, it is desirable to train a model that can predict sentiments in different domains simultaneously. Previous authors have used deep learning to extract features from multiple domains. Here, each word is represented by a vector that is determined using co-occurrence data. Such a model requires that all sentences have the same length resulting in low accuracy. To overcome this challenge, we model the features in each sentence using a variable length tree called a Genetic Program. The polarity of clauses can be represented using mathematical operators such as plus or minus at internal nodes in the tree. The proposed model is evaluated on Amazon product reviews for different products and in different languages. We are able to outperform the accuracy of baseline multi-domain models in the range of 5-20percent.",
-
notes = "https://wcci2020.org/
James Cook University, Australia; MIT, United States of America; Nanyang Technological University, Singapore.
Also known as \cite{9185713}",
