The Effect of Pre-Processing on the Classification of Twitter’s Flood Disaster Messages Using Support Vector Machine Algorithm

Delimayanti, Mera Kartika; Sari, Risna; Laya, Mauldy; Faisal, Muhammad; Pahrul,; Naryanto, Rizqi Fitri

doi:10.1109/icae50557.2020.9350387

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…SVM has been used in research on several system areas, such as: presidential election [12], health record data [18], customer satisfaction [16], fake consumer reviews [6], restaurant review [9], flood disaster news [14], Vietnamese [15], product reviews [4], [11]. The accuracy is quite high in research using the SVM algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In the presidential election system, the accuracy is 76.5% [12], customer satisfaction system 84.85% [18], and product reviews with accuracy 88.13% [11]. Various datasets are used, including Twitter [10], [12], [14], [16], [18], Cornell University, trip advisor [6], restaurant review [9], Vietnamese [15], product reviews [4], application comments, financial market news [10], and product review Amazon [11]. SVM already has the advantages of support vector and a dividing line (hyperplane) so it takes a little time to classify [12].…”

Section: Introductionmentioning

confidence: 99%

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Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Nurcahyawati¹,

Mustaffa

2023

Bulletin EEI

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High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for classification. Various challenges were encountered, including how to determine the optimal combination of pre-processing techniques, how to clean the dataset, and determine the best classification algorithm. This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. The proposed model was realized using 3 different datasets, namely Amazon product review, movie review, and airline review from Twitter. This study provides better findings than the previous results. Improved performance is generated by SVM combined with FM, resulting in 96% accuracy. So that the SVM-FM combination can be said to be the best combination for sentiment analysis on the given data set.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Nurcahyawati¹,

Mustaffa

2023

Bulletin EEI

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“…Table 8 shows the performance of the classification model from previous studies using the same dataset. Accuracy values for cases of earthquakes [7] and flood [6] obtained in this study are better when compared to previous studies. obtained with a smaller number of features compared to previous studies [11].…”

Section: Discussionmentioning

confidence: 43%

“…Then the three datasets are pre-processed with steps commonly performed in text classification, namely removing double spaces, punctuation, numbers and non-alphanumeric characters. [6], [7], [13].…”

Section: Datasetmentioning

confidence: 99%

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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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Machine learning approach for prediction model on biomass characteristic analysis

Naryanto¹,

Delimayanti²

2023

International Conference on Mechanical Engineering for Emerging Technologies (Icomeet 2021)

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The Effect of Pre-Processing on the Classification of Twitter’s Flood Disaster Messages Using Support Vector Machine Algorithm

Cited by 17 publications

References 7 publications

Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

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

Machine learning approach for prediction model on biomass characteristic analysis

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