Chinese comments sentiment classification based on word2vec and SVMperf

By Dongwen Zhang, Hua Xu, Zengcai Su, Yunfeng Xu. Expert systems with applications, 2015

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How researchers see this paper

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Sentiment Analysis of Comment Texts Based on BiLSTM (2019, methodology): “The widely used word representation is the distributed word vector obtained by Word2vec technology [6].”
Classification of sentiment reviews using n-gram machine learning approach (2016, methodology): “Keywords: Sentiment analysis, Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), ngram, IMDb dataset URL: abi.tripathy@gmail.com (Abinash Tripathy), agrawala96@gmail.com (Ankit Agrawal), skrath@nitrkl.ac.in (Santanu Kumar Rath) Preprint submitted”
Gender Prediction for Chinese Social Media Data (2017, methodology): “Some research is starting to use word2vec for text classification of Chinese texts, especially for sentiment analysis (Su et al., 2014; Bai et al., 2014; Zhang et al., 2015), and we would like to explore the use of word2vec techniques in the gender classification task.”
Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism (2020, background): “Figure 1 describes the Word2vec process, which involves two distinct components: Continuous Bag of Words (CBOW) and skip-gram [10].”