Proceedings ArticleDOI
Sentiment Analysis using Word2vec-CNN-BiLSTM Classification
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TLDR
In this paper, CNN-BiLSTM model associated with Word2vec word embedding achieved 9148% accuracy in short text classification, which proved that the hybrid network model performs better than the single structure neural network in short texts.Abstract:
Traditional neural network based short text classification algorithms for sentiment classification is easy to find the errors In order to solve this problem, the Word Vector Model (Word2vec), Bidirectional Long-term and Short-term Memory networks (BiLSTM) and convolutional neural network (CNN) are combined The experiment shows that the accuracy of CNN-BiLSTM model associated with Word2vec word embedding achieved 9148% This proves that the hybrid network model performs better than the single structure neural network in short textread more
Citations
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CBGRU: A Detection Method of Smart Contract Vulnerability Based on a Hybrid Model
TL;DR: A novel hybrid deep learning model named CBGRU is proposed that strategically combines different word embedding with different deep learning methods and has great smart contract vulnerability detection performance through a series of experiments.
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Sentiment analysis for user reviews using Bi-LSTM self-attention based CNN model
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Combination of GRU and CNN Deep Learning Models for Sentiment Analysis on French Customer Reviews Using XLNet Model
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Hybrid Deep Learning CNN-Bidirectional LSTM and Manhattan Distance for Japanese Automated Short Answer Grading: Use case in Japanese Language Studies
Anak Agung Putri Ratna,Prima Dewi Purnamasari,Nadhifa Khalisha Anandra,Dyah Lalita Luhurkinanti +3 more
TL;DR: In this article , an Automatic Essay Grading System (SIMPLE-O) designed using hybrid CNN and Bidirectional LSTM and Manhattan Distance for Japanese language course essay grading is presented.
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An Efficient Deep Learning for Thai Sentiment Analysis
TL;DR: In this paper , a sentiment analysis method is proposed for Thai sentiment classification in the hotel domain, where the continuous bag-of-words (CBOW) and skip-gram approaches were applied to create word embeddings of different vector dimensions.
References
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Proceedings Article
Unified Language Model Pre-training for Natural Language Understanding and Generation
Li Dong,Nan Yang,Wenhui Wang,Furu Wei,Xiaodong Liu,Yu Wang,Jianfeng Gao,Ming Zhou,Hsiao-Wuen Hon +8 more
TL;DR: UniLM as mentioned in this paper is a unified pre-trained language model that can be fine-tuned for both natural language understanding and generation tasks, achieving state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement).
Proceedings ArticleDOI
Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis
Alec Yenter,Abhishek Verma +1 more
TL;DR: A novel approach to sentiment analysis through the use of combined kernel from multiple branches of convolutional neural network (CNN) with Long Short-term Memory (LSTM) layers produces a model with the highest reported accuracy on the Internet Movie Database review sentiment dataset.
Journal ArticleDOI
Opinion mining using ensemble text hidden Markov models for text classification
Mangi Kang,Jaelim Ahn,Kichun Lee +2 more
TL;DR: A new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon and has potential to classify implicit opinions.
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Multi-Channel Lexicon Integrated CNN-BiLSTM Models for Sentiment Analysis
Joosung Yoon,Hyeoncheol Kim +1 more
TL;DR: This work improved sentiment classifier for predicting document-level sentiments from Twitter by using multi-channel lexicon embedidngs and applied multi- channel method on lexicon to improve lexicon features.