Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion

Alhakiem, Hanif Reangga; Setiawan, Erwin Budi

doi:10.29207/resti.v6i5.4429

Cited by 8 publications

(6 citation statements)

References 14 publications

(18 reference statements)

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“…1. *name of corresponding author (Alhakiem & Setiawan, 2022). The crawling is done using the SNScrape library provided in the Python programming language.…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Saddam

Dewantara

Solichin

2023

SinkrOn

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Floods can cause negative impacts in various aspects, starting from economic, social, to health aspects. Even quoted from the site gis.bnpb.go.id, during 2022, there have been 1031 cases of flood disasters in Indonesia. Meanwhile in Jakarta, in 2022 there have been 14 cases of floods that caused hundreds of people to lose their homes. Several approaches can be taken to determine public opinion about flooding, one of which is text mining with an analysis of community sentiment. Sentiment analysis aims to determine public opinion regarding flood management in the capital city of Jakarta based on positive, neutral, and negative categories. To get the public sentiment, researchers carried out several stages, including the preprocessing stage. After obtaining public sentiment regarding flood management through the preprocessing stage, then classification is carried out based on public opinion so that it can be used by related parties for evaluation material in flood handling. In this study, the classification method used is the SVM method which is one of the supervised learning methods in machine learning. After classification, the next stage is the testing process using the K-Fold Cross Validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 414 positive sentiments and 2464 negative sentiments related to flood handling in DKI Jakarta, while the results obtained from the test results show that the accuracy reaches 88.6%, the precision reaches 88.6% and recall reached 89.4%.

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“…1. *name of corresponding author (Alhakiem & Setiawan, 2022). The crawling is done using the SNScrape library provided in the Python programming language.…”

Section: Methodsmentioning

confidence: 99%

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Saddam

Dewantara

Solichin

2023

SinkrOn

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“…Pengumpulan data Twitter adalah proses melakukan ekstraksi data pengguna dan tweet dari Twitter berdasarkan kata kunci tertentu. Penambangan data dilakukan melalui penggunaan Application Programming Integration (API) [14]. Pengumpulan dilakukan menggunakan pustaka SNScrape yang disediakan dalam bahasa pemrograman Python.…”

Section: A Data Collectionunclassified

Analisis Sentimen Fenomena PHK Massal Menggunakan Naive Bayes dan Support Vector Machine

Saddam,

Kurniawan D,

Indra

2023

JPIT

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Termination of employment (PHK) on a large scale has a very significant impact on society and the economy. Mass layoffs have led to an increase in the number of unemployed people. Many people who have lost their jobs without a stable source of income struggle to find new jobs. This exacerbated the situation on the labor market and increased the number of unemployed people. Mass layoffs can also reduce economic activity and consumption. The sentiment analysis carried out aims to determine public sentiment regarding the phenomenon of mass layoffs that are currently happening in Indonesia based on positive and negative categories. In this study, the classification method used is the SVM method, which is one of the supervised learning methods in machine learning and also uses Nave Bayes as a comparison method. After classification, the next stage is the testing process using the K-fold cross-validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 108 positive sentiments and 333 negative sentiments related to mass layoffs, while the results obtained from the test results using the SVM method show an accuracy of up to 84% while using the Nave Bayes method shows an accuracy of up to 74.1 percent

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“…The research resulted in an F1-score of 91.24% on the signal aspect and 88.75% on the service aspect by applying SMOTE in overcoming unbalanced data labeling. And in other aspect-based sentiment research [15] Alhakiem and Erwin in 2022, applied TF-IDF feature extraction and FastText feature expansion to logistic regression, then overcame unbalanced data by using SMOTE. It was concluded that by using SMOTE, F1-Score increased by 3.33% in the signal aspect and 12.91% in the service aspect.…”

Section: Introductionmentioning

confidence: 99%

Aspect-based Sentiment Analysis on Social Media Using Convolutional Neural Network (CNN) Method

Ramadhan

Setiawan

2023

bits

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Social media are a platform for people to express their opinions on various topics, one of which is Twitter. Movie reviews are a frequently found topic on Twitter that contains a person's opinion of a movie that has been watched. But since opinions are subjective, it is difficult to determine an accurate assessment of a movie. In addition, the diverse aspects of a movie make it difficult to judge whether a review is positive or negative. Referring to that problem, a method is needed to perform sentiment analysis of the problem. In this study, sentiment analysis of movie reviews was carried out based on aspects of plot, acting, and director. This research also performs classification using a CNN model and combines several techniques, namely TF-IDF feature extraction, FastText feature expansion, and SMOTE to calculate the accuracy value and F1-Score. The final results obtained in this study are in the aspect of the plot getting an accuracy of 73.81% (+12,22%) and F1-score 73.72% (+15,93%), the acting aspect obtaining an accuracy value of 89.30% (+0,54%) and F1-score 89.26% (+50,80%), and in the aspect of the director having an accuracy of 87.37% (+0,28%) and F1-score 87.35% (+84,39%). Based on these results, each application of techniques such as TF-IDF, FastText, and SMOTE can increase the accuracy value and F1-Score of the model built.

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Aspect-Bas1ed Sentiment Analysis on Twitter Using Logistic Regression with FastText Feature Expansion

Cited by 8 publications

References 14 publications

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Analisis Sentimen Fenomena PHK Massal Menggunakan Naive Bayes dan Support Vector Machine

Aspect-based Sentiment Analysis on Social Media Using Convolutional Neural Network (CNN) Method

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