PTE2ASC | word embedding resources for sentiment classification | Predictive Analytics library

PTE2ASC is a Python library typically used in Retail, Analytics, Predictive Analytics, Pytorch applications. PTE2ASC has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However PTE2ASC build file is not available. You can download it from GitHub.

This is a word embedding resource built by ourselves with PTE which is a semisupervised representation learning tool proposed by [Tang et al., 2015]. This tool could leverage both labeled and unlabeled data to build a large-scale heterogeneous network and use the network to train the word vectors. In our implementation, on one hand, the labeled data is collected from Amazon by [McAuley et al., 2015]. Specifically, we pick 6 domains, i.e., Books, CDs, Clothing, Electronics, Restaurant and Health and each review is automatically assigned with a positive category if its rating score is 4 or 5 and a negative category if its rating score is 1 or 2. On the other hand, the unlabeled data is the data from SemEval-2015 Task [Pontiki et al., 2015]. The vocabulary size is about 1.2 million and the dimensionality of word vector is 300. [Tang et al., 2015] Jian Tang, Meng Qu, and Qiaozhu Mei. PTE: predictive text embedding through large-scale heterogeneous text networks. In Proceedings of SIGKDD2015, pages 1165–1174, 2015. [McAuley et al., 2015] Julian J. McAuley, Rahul Pandey, and Jure Leskovec. Inferring networks of substitutable and complementary products. In Proceedings of SIGKDD2015, pages 785–794, 2015. [Pontiki et al., 2015] Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. Semeval-2015 task 12: Aspect based sentiment analysis. In Proceedings of NAACL-HLT-2015, pages 486–495, 2015. The word embedding resource is released at