Classification of User Comment Using Word2vec and SVM Classifier

Kurnia, Rafly Indra

doi:10.30534/ijatcse/2020/90912020

Cited by 22 publications

(17 citation statements)

References 16 publications

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“…As future works, we aim to: Increase the size of PROMISE_exp to use and explore others feature extraction techniques such as word2vec [ 41 ], AUR-BoW [ 10 ], Hierarchal BoW [ 42 ] and Online Unsupervised Multi-view Feature Selection [ 43 ]. Use advanced multi-view strategy to combine the different feature set in order to avoid redundant information.…”

Section: Discussionmentioning

confidence: 99%

Software Requirements Classification Using Machine Learning Algorithms

Canedo

Mendes

2020

Entropy

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The correct classification of requirements has become an essential task within software engineering. This study shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions “Which works best (Bag of Words (BoW) vs. Term Frequency–Inverse Document Frequency (TF-IDF) vs. Chi Squared (CHI2)) for classifying Software Requirements into Functional Requirements (FR) and Non-Functional Requirements (NF), and the sub-classes of Non-Functional Requirements?” and “Which Machine Learning Algorithm provides the best performance for the requirements classification task?”. The data used to perform the research was the PROMISE_exp, a recently made dataset that expands the already known PROMISE repository, a repository that contains labeled software requirements. All the documents from the database were cleaned with a set of normalization steps and the two feature extractions, and feature selection techniques used were BoW, TF-IDF and CHI2 respectively. The algorithms used for classification were Logist Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB) and k-Nearest Neighbors (kNN). The novelty of our work is the data used to perform the experiment, the details of the steps used to reproduce the classification, and the comparison between BoW, TF-IDF and CHI2 for this repository not having been covered by other studies. This work will serve as a reference for the software engineering community and will help other researchers to understand the requirement classification process. We noticed that the use of TF-IDF followed by the use of LR had a better classification result to differentiate requirements, with an F-measure of 0.91 in binary classification (tying with SVM in that case), 0.74 in NF classification and 0.78 in general classification. As future work we intend to compare more algorithms and new forms to improve the precision of our models.

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Section: Discussionmentioning

confidence: 99%

Software Requirements Classification Using Machine Learning Algorithms

Canedo

Mendes

2020

Entropy

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“…Word2vec is a popular method for learning word embeddings based on a two-layer neural network to convert the text data into a set of vectors (Mikolov et al, 2013 ). Unlike TF-IDF, word2vec could consider more context when processing each word (Kurnia et al, 2020 ). We applied word2vec with a skip-gram training algorithm given by the Gensim library (Rehurek and Sojka, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification

Ren

Singh

et al. 2022

Front. Big Data

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There are increasingly strict regulations surrounding the purchase and use of combustible tobacco products (i.e., cigarettes); simultaneously, the use of other tobacco products, including e-cigarettes (i.e., vaping products), has dramatically increased. However, public attitudes toward vaping vary widely, and the health effects of vaping are still largely unknown. As a popular social media, Twitter contains rich information shared by users about their behaviors and experiences, including opinions on vaping. It is very challenging to identify vaping-related tweets to source useful information manually. In the current study, we proposed to develop a detection model to accurately identify vaping-related tweets using machine learning and deep learning methods. Specifically, we applied seven popular machine learning and deep learning algorithms, including Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, Multilayer Perception, Transformer Neural Network, and stacking and voting ensemble models to build our customized classification model. We extracted a set of sample tweets during an outbreak of e-cigarette or vaping-related lung injury (EVALI) in 2019 and created an annotated corpus to train and evaluate these models. After comparing the performance of each model, we found that the stacking ensemble learning achieved the highest performance with an F1-score of 0.97. All models could achieve 0.90 or higher after tuning hyperparameters. The ensemble learning model has the best average performance. Our study findings provide informative guidelines and practical implications for the automated detection of themed social media data for public opinions and health surveillance purposes.

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“…Classifying user interests is one of the most important steps in personalized advertising as it provides information about users’ interests that could be used by marketers or advertisers. There have been various studies to classify users’ interests [ 25 , 26 , 27 , 28 , 29 ]. A study on SNS suggests a classification method to classify users’ active communication through comments into a grading system using Word2vec and support vector machine (SVM) classifier [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…There have been various studies to classify users’ interests [ 25 , 26 , 27 , 28 , 29 ]. A study on SNS suggests a classification method to classify users’ active communication through comments into a grading system using Word2vec and support vector machine (SVM) classifier [ 25 ]. The weighting ensemble model was proposed to classify the user’s emotions into multi-label binaries by analyzing the content created by the user, and this model performs the classification result without hyperparameter adjustment or overfitting [ 26 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

Hong

Choi

Lim

et al. 2020

Sensors

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The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations.

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Classification of User Comment Using Word2vec and SVM Classifier

Cited by 22 publications

References 16 publications

Software Requirements Classification Using Machine Learning Algorithms

Software Requirements Classification Using Machine Learning Algorithms

Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification

Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks

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