Word Embeddings and Deep Learning for Spanish Twitter Sentiment Analysis

Luna, José Eduardo Ochoa; Ari, Disraeli

doi:10.1007/978-3-030-11680-4_4

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…Sentance vectors can be formed by arranging word vectors into a matrix (2D) [7]. Three 2D data created by each word embedding technique such as Wod2vec, Glove and fastText can be combined into 3 layers to form 3-dimensional data (3D) [8]. The output of the feature extraction process is structured data.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification

Faisal¹,

Budiman

Abadi

et al. 2023

J. Comput. Sci. Inst.

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The research aims to compare the classification performance of natural disaster messages classification from Twitter. The research experiment covers the analysis of three-word embedding-based extraction feature techniques and five different models of deep learning. The word embedding techniques that are used in this experiment are Word2Vec, fastText, and Glove. The experiment uses five deep learning models, namely three models of different dimensions of Convolutional Neural Network (1D CNN, 2D CNN, 3D CNN), Long Short-Term Memory Network (LSTM), and Bidirectional Encoder Representations for Transformer (BERT). The models are tested on four natural disaster messages datasets: earthquakes, floods, forest fires, and hurricanes. Those models are tested for classification performance

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Section: Introductionmentioning

confidence: 99%

“…This study used three-word embedding techniques to create 2D data, namely word2vec, fastText and Glove. The application of text data classification with 3D CNN is made by combining 2D data based on word embedding consisting of three layers based on word embedding techniques word2vec [12], Glove [13] and fastText [8].…”

Section: Introductionmentioning

confidence: 99%

A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification

Faisal¹,

Budiman

Abadi

et al. 2023

J. Comput. Sci. Inst.

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show abstract

“…The word embedding-based feature extraction technique is formed by concatenating word vectors into 1-dimensional (1D) data [9] and arranging the word vectors into a two-dimensional matrix (2D data) [10], [11]. The three 2D data formed by each word embedding technique, such as Wod2vec, Glove and fastText can be combined into three layers to create three-dimensional (3D) data [12].…”

Section: Introductionmentioning

confidence: 99%

“…The application of text data classification with 3D CNN is made by combining 2D data based on word embedding which is arranged in three layers. Each layer is 2D data that are generated by using word2vec, Glove, and fastText [12].…”

Section: Introductionmentioning

confidence: 99%

Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding

Faisal

Budiman

Abadi

et al. 2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.

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A Social Community Sensor for Natural Disaster Monitoring in Indonesia Using Hybrid 2D CNN LSTM

Faisal,

Nugrahadi,

Budiman

et al. 2023

Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology

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Word Embeddings and Deep Learning for Spanish Twitter Sentiment Analysis

Cited by 3 publications

References 10 publications

A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification

A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification

Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding

A Social Community Sensor for Natural Disaster Monitoring in Indonesia Using Hybrid 2D CNN LSTM

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