The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews

Khomsah, Siti; Ramadhani, Rima Dias; Wijaya, Sena

doi:10.29207/resti.v6i3.3711

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…There are three groups of techniques based on the dimensions of the output data, namely, one dimension (1D), two dimensions (2D), and three dimensions (3D). Each of these techniques will use three-word embedding techniques that are popularly used in classification studies, namely word2vec [14], fastText [15], and Glove [16], [17]. By using the three-word embedding models, data structures with different dimensions are created.…”

Section: Feature Extractionmentioning

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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Section: Feature Extractionmentioning

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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“…Several studies have used text mining and word embedding [1], [12], [13]. The traditional word embedding model still needs to be more accurate when applied directly to analyzing sentiments and emotions because the main problem of the learning word insertion algorithm is that it can only model the word context without involving feeling or emotional information in the text [14].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

Purwitasari¹,

Ansyah²,

Kurniawan³

et al. 2023

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

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As a result of the COVID-19 pandemic, there have been restrictions on activities outside the home which has caused people to interact more and express their emotions through social media platforms, one of which is Twitter. Previous studies on emotion classification used only one feature extraction, namely the lexicon based or word embedding. Feature extraction using the emotion lexicon has the advantage of recognizing emotional words in a sentence while feature extraction using word embedding has the advantage of recognizing the semantic meaning. Therefore, the main contribution to this research is to use two lexicon feature extraction and word embedding to classify emotions. The classification technique used in this research is the Ensemble Voting Classifier by selecting the two best classifiers to try on both types of feature extraction. The experimental results for both types of feature extraction are the same, indicating that the best classifiers are Random Forest and SVM. Models using both types of feature extraction show increased accuracy compared to using only one feature extraction. The results of this emotional analysis can be used to determine the public's reaction to an event, product, or public policy.

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Unleashing the power of 2D CNN with attention and pre-trained embeddings for enhanced online review analysis

Shetty,

Fadhel Aljunid,

Manjaiah

2023

International Journal of Computers and Applications

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The Accuracy Comparison Between Word2Vec and FastText On Sentiment Analysis of Hotel Reviews

Cited by 6 publications

References 14 publications

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

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

A Hybrid Method on Emotion Detection for Indonesian Tweets of COVID-19

Unleashing the power of 2D CNN with attention and pre-trained embeddings for enhanced online review analysis

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