Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Rahmanda, Rayhan; Setiawan, Erwin Budi

doi:10.29207/resti.v6i4.4186

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…Synthetic Minority Oversampling Technique (SMOTE) adalah teknik resampling data yang umum digunakan dalam machine learning, khususnya untuk menangani masalah ketidakseimbangan kelas [26]. SMOTE menghasilkan sampel baru yang mirip dengan data minoritas dengan cara membuat sampel sintetis baru dari data yang sudah ada.…”

Section: Synthetic Minority Oversampling Technique (Smote)unclassified

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Singgalen

2023

bits

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Culinary is one of the economic activities that support national economic growth and represents the gastronomy of the archipelago in Indonesia. Culinary tourism activities have become famous for domestic and foreign tourists to experience the taste of a food based on the culture of each region. Makassar is one of the regions with diverse types of food and beverages and has a relationship with local socio-cultural values. Considering this, this study aims to analyze consumer sentiment toward food and services in ten restaurants in Makassar based on the recommendations of the Tripadvisor website using the Cross-Industry Standard Process for Data Mining (CRISPD-DM) and Service Quality (SERVQUAL) methods. The stages of CRISP-DM are as follows: the stage of understanding business processes; the stage of understanding data; the stage of preparing data; the modeling stage; the evaluation stage; and the deployment stage. The algorithms used as models are k-Nearest Neighbor (kNN), Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM). The results of this study show that the DT algorithm when using the SMOTE operator where the resulting accuracy value is 93.25%, precision is 88.74%, recall is 99.10%, and f-measure is 93.62%. In addition, the k-NN algorithm without using the SMOTE operator showed an accuracy value of 98.72%, a precision of 98.72%, a recall of 100%, and an f-measure of 99.36%. However, the resulting AUC value is 0.905 (90.5%). Meanwhile, when using the SMOTE operator, the SVM algorithm produces an accuracy value of 99.42%, a precision of 100%, a recall of 98.84%, and an f-measure of 99.42%. Meanwhile, the resulting AUC value is 1,000 (100%). Based on the ROC value, three algorithms can be used as models in the CRISPP-DM and SERVQUAL frameworks: the k-NN algorithm without SMOTE and the DT and SVM algorithms using the SMOTE operator

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Section: Synthetic Minority Oversampling Technique (Smote)unclassified

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Singgalen

2023

bits

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“…The purpose of this process is to extract valuable information that depicts the essential characteristics of a text [11]. In other words, feature extraction is a process of searching for and extracting features from tweets that can explain their characteristics [19].In this research, feature extraction is performed using the TF-IDF technique. The Term Frequency-Inverse Document Frequency or commonly known by the abbreviation TF-IDF is a renowned algorithm utilized to compute the weight of text and digitize the text based on the frequency and inverse document frequency of a word or phrase, known as a feature item [20].…”

Section: E Feature Extractionmentioning

confidence: 99%

“…The result of the feature extraction process using TF-IDF is a vector that represents the text, and each word is assigned its respective weight [19]. The formula used to calculate the TF-IDF can be seen in the following formula [23]:…”

Section: E Feature Extractionmentioning

confidence: 99%

Implementation of Naïve Bayes Algorithm in Sentiment Analysis of Twitter Social Media Users Regarding Their Interest to Pay the Tax

Wahyu Andrian,

Adline Twince Tobing,

Zuhdi Pane

et al. 2023

International Journal of Science, Technology & Management

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Since 2008, tax revenue has failed to reach the target set in the State Budget each year. Until 2021, tax revenue managed to reach the target that had been targeted in the 2021 state budget. In the midst of improving tax revenue, towards the end of February 2023, a case involving the son of a Directorate General of Taxes (DGT) that made the father called by the Corruption Eradication Commission (CEC) to be asked for an explanation of his assets. After the case, there were many calls in the community to stop paying taxes, which was assessed by Tauhid Ahmad as Executive Director of Indef as a form of decreased trust in tax collecting institutions. This can affect the amount of revenue from taxes because trust in the government is one of the factors that tend to affect public compliance in paying taxes. Which can affect the amount of revenue from taxes because trust in the government is one of the factors that tend to affect public compliance in paying taxes. One of the crowded calls is the pros and cons of the tax boycott movement on Twitter. With the pros and cWith the pros and cons of the movement that can affect tax revenues on Twitter social media, an assessment based on sentiment analysis is needed which is divided into positive, neutral, or negative categories. Sentiment analysis in this research is carried out using three variations of Naïve Bayes assisted by the TF-IDF word weighting model, namely Gaussian Naïve Bayes, Multinomial Naïve Bayes, and Bernoulli Naïve Bayes. Then Confussion Matrix is used to evaluate the model by obtaining the accuracy, precission, recall, and f1-score values and the use of Synthetic Minority Oversampling Technique (SMOTE) to handle unbalanced data. The results of this study on unbalanced data, the implementation of Bernoulli Naïve Bayes using the SMOTE technique on a dataset comparison of 80:20 resulted in better performance than the variations of Gaussian and Multinomial Naïve Bayes with accuracy results of 91.03%, precision, 71.11%, recall 71.43%, and f1-score of 71.18%.

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“…However, SMOTE does not perform oversampling based on direct sample copies. Instead, some additional examples are created outside of the original dataset to prevent overfitting, which is an advantage of this algorithm [12].…”

Section: E Synthetic Minority Over-sampling (Smote)mentioning

confidence: 99%

Sentiment Analysis of Tourist Attraction Review from TripAdvisor Using CNN and LSTM

Adrian Manurung

2023

ijoict

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The tourism sector has an important role in driving the economy. To find out the positive or negative responses of tourists, one of them is grouping through sentiment analysis using deep learning. The data used the tourist attraction dataset from TripAdvisor from several categories such as water and amusement park, nature, and museum. The methods used in this research are convolutional neural network (CNN) and long short-term memory (LSTM). In addition, Word2vec for feature extraction and Synthetic Minority Over-sampling (SMOTE) for handling imbalanced datasets will be used for this research. There are several scenarios used to perform sentiment analysis, with early stopping and with hyperparameter tuning using random search. The highest performance obtained on water and amusement park, nature, and museum category data is 83%, 97%, and 88% respectively for accuracy and 91%, 92%, and 93% respectively for F1-score. For the use of sentiment analysis methods, CNN can perform with the highest F1-score and LSTM can perform with the highest accuracy.

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Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Cited by 4 publications

References 16 publications

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Implementation of Naïve Bayes Algorithm in Sentiment Analysis of Twitter Social Media Users Regarding Their Interest to Pay the Tax

Sentiment Analysis of Tourist Attraction Review from TripAdvisor Using CNN and LSTM

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