Sentiment Analysis Using Word2vec And Long Short-Term Memory (LSTM) For Indonesian Hotel Reviews

Muhammad, Putra Fissabil; Kusumaningrum, Retno; Wibowo, Adi

doi:10.1016/j.procs.2021.01.061

Cited by 115 publications

(78 citation statements)

References 7 publications

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“…Although currently NLP developments mainly implement Transformer [1] [2], the computation cost and the need for huge datasets leads to the use of different methods [3] [4] [5] [6] [7], which are based on the semantic proximity between words. Our work implements the well-established Word2Vec word embedding technique as presented in Figure 1.…”

Section: Previous Workmentioning

confidence: 99%

Production2Vec: a hybrid recommender system combining semantic and product complexity approach to improve industrial resiliency

Pachot

Albouy-Kissi

et al. 2021

2021 2nd International Conference on Artificial Intelligence and Information Systems

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COVID-19 health crisis highlights the fragility of European industrial strategies and leads us to develop more agile, distributed, and resilient production models at a territorial level. There are two major challenges in this regard: one is to find solutions to secure supplies and/or industrial value chains, and the other is to identify companies that have the potential to transform their production quickly to cope with an emergency situation. We extended the Word2Vec vector space with products and economic activities allowing us to calculate proximities. We present a methodology based on semantic proximity and productive complexities to assess the ability of an A-company to produce a product B and to anticipate customer/supplier-type collaboration according to industrial quality standards. We consider recommendation topics by intertwining machine learning techniques with semantic approaches, referring to area ontologies incorporating territorial dimensionality. CCS CONCEPTS• Natural language processing; • Recommender systems; • Supply chain management;

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Section: Previous Workmentioning

confidence: 99%

Production2Vec: a hybrid recommender system combining semantic and product complexity approach to improve industrial resiliency

Pachot

Albouy-Kissi

et al. 2021

2021 2nd International Conference on Artificial Intelligence and Information Systems

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“…Onan combined word embedding methods with cluster analysis, and it was proved that the text analysis accuracy was improved by the method [20]. Muhammad et al utilized Word2Vec and LSTM to analyze hotel reviews and found that the recognition precision rate could reach 85% [21]. The Word2Vec method has been studied by domestic and foreign scholars, and word vectors extracted in most scenarios have good performance.…”

Section: Word Vector Generationmentioning

confidence: 99%

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

Yan

et al. 2021

Applied Sciences

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In recent years, online course learning has gradually become the mainstream of learning. As the key data reflecting the quality of online courses, users’ comments are very important for improving the quality of online courses. The sentiment information contained in comments is the guide of course improvement. A new ensemble model is proposed for sentiment analysis. The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction. Moreover, the multi-objective gray wolf optimization (MOGWO) ensemble method is adopted to integrate the models mentioned above. The experimental results show that the sentiment recognition accuracy of the proposed model is higher than that of the other seven comparison models, with an F1score over 91%, and the recognition results of different emotion levels indicate the stability of the proposed ensemble model.

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“…In many natural language processing(NLP) studies [28,29], the method of word embedding combined with RNN has a breakthrough performance. According to the experimental results of different RNN structures in the last chapter, we adopt the BiLSTM for feature extraction.…”

Section: Model Structurementioning

confidence: 99%

A MiRNA Target Prediction Model based on Distributed Representation Learning and Deep Learning

Sun¹,

Xiong²,

Sun³

et al. 2021

Preprint

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Background: MicroRNAs (miRNAs) are a kind of non-coding RNA, which plays an essential role in gene regulation by binding to messenger RNAs(mRNAs). Accurate and rapid identification of miRNA target genes is helpful to reveal the mechanism of transcriptome regulation, which is of great significance for the study of cancer and other diseases. Many bioinformatics methods have been proposed to solve this problem, but the previous research did not further study the encoding of the base sequence. Results: In this study, we developed a novel method combining word embedding and deep learning for human miRNA targets at the site level prediction, which is inspired by the similarity between natural language and biological sequences. First, the wor2vec model was used to mine the distribution representation of miRNAs and mRNAs. Then, the data features are fully extracted automatically from temporal and spatial via the stacked Bidirectional Long short-term memory(BiLSTM) network. We compare the effects of different embedding methods on model accuracy in different deep learning models, and the results prove that using word2vec can improve the accuracy of deep learning models. In addition, we performed visual analysis on the distributed represented sequences and found hidden similarity relationships between bases. Finally, compared with different advanced methods and data sets, the results show that our proposed method has gotten better performance in multiple evaluation aspects. Conclusions: We present a novel method for predicting miRNA target sites consisting of word2vec and the BiLSTM model and demonstrate that this method can realize automatic feature extraction and has higher accuracy. Furthermore, we process miRNA and mRNA as two languages for the first time and explore their biological significance through visual analysis.

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Sentiment Analysis Using Word2vec And Long Short-Term Memory (LSTM) For Indonesian Hotel Reviews

Cited by 115 publications

References 7 publications

Production2Vec: a hybrid recommender system combining semantic and product complexity approach to improve industrial resiliency

Production2Vec: a hybrid recommender system combining semantic and product complexity approach to improve industrial resiliency

Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese

A MiRNA Target Prediction Model based on Distributed Representation Learning and Deep Learning

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