Classifying Political Tweets Using Naïve Bayes and Support Vector Machines

Hamoud, Ahmed Al; Alwehaibi, Ali; Bikdash, Marwan

doi:10.1007/978-3-319-92058-0_71

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…Hamoud et al, (Hamoud et al, 2018) have used the Bag of Words (BOW), TF and TF-IDF on the Twitter data for the classification of political tweets. Of the classification algorithms, they have used SVM and NB.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis with Machine Learning Methods on Social Media

Başarslan

Kayaalp

2020

ADCAIJ

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Social media has become an important part of our everyday life due to the widespread use of the Internet. Of the social media services, Twitter is among the most used ones around the world. People share their opinions by writing tweets about numerous subjects, such as politics, sports, economy, etc. Millions of tweets per day create a huge dataset, which drew attention of the data scientists to focus on these data for sentiment analysis. The sentiment analysis focuses to identify the social media posts of users about a specific topic and categorize them as positive, negative or neutral. Thus, the study aims to investigate the effect of types of text representation on the performance of sentiment analysis. In this study, two datasets were used in the experiments. The first one is the user reviews about movies from the IMDB, which has been labeled by Kotzias, and the second one is the Twitter tweets, including the tweets of users about health topic in English in 2019, collected using the Twitter API. The Python programming language was used in the study both for implementing the classification models using the Naïve Bayes (NB), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) algorithms, and for categorizing the sentiments as positive, negative and neutral. The feature extraction from the dataset was performed using Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec (W2V) modeling techniques. The success percentages of the classification algorithms were compared at the end. According to the experimental results, Artificial Neural Network had the best accuracy performance in both datasets compared to the others.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis with Machine Learning Methods on Social Media

Başarslan

Kayaalp

2020

ADCAIJ

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“…They used SVM and NB classification algorithms. According to the results, BOW-enabled SVM provides the highest accuracy and F-measure [4]. Symeonidis et al used Linear SVC, Bernoulli NB, Logistic Regression (LR), and CNN, which are four popular ML algorithms.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Başarslan

Kayaalp

2021

Sakarya University Journal of Computer and Information Sciences

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Thanks to social media, people are now able to leave guiding comments quickly about their favorite restaurants, movies, etc. This has paved the way for the field of sentiment analysis, which brings together various disciplines. In this study, Yelp restaurant reviews and IMDB movie reviews dataset were used together with the data collected from Twitter. Word2Vec (W2V), Global Vector (GloVe) and Bidirectional Encoder Representation (BERT) word embedding methods, Term Frequency-Reverse Document Frequency (TF-IDF), and the Bag-of-Words (BOW) were used on these datasets. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Support Vector Machine (SVM), and Naive Bayes (NB) were used in the sentiment analysis models. Accuracy, F-measure (F), Sensitivity (Sens), Precision (Pre), and Receiver Operating Characteristics (ROC) were used in the evaluation of the model performance. The Accuracy rates of the models created by the Machine Learning (ML) and Deep Learning (DL) methods using the IMDB dataset were in the range of 81%-90% and 84%-94%, respectively. These rates were in the range of 80%-86% and 81%-89% for the Yelp dataset, and in the range of 75%-79% and 85%-98% for the Twitter dataset. The models that incorporated the BERT word embedding method have the best performance, compared to the other models with ML and DL. Therefore, BERT method is recommended for this type of analysis in future studies.

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“…Como para o trabalho proposto oúnico interesseé saber se um dado ingrediente está ou não contido em uma receita, a DTM será codificada como uma matriz binária. Em seguida, a técnica de TF-IDF [Hamoud et al 2018]é aplicadaà DTM para a obtenção da relevância de cada ingrediente em cada receita [Jayaraman et al 2017], sendo a base de dados final constituída pelas frequências do TF-IDF.…”

Section: Base De Dados Propostaunclassified

Uma Abordagem de Análise de Textos para a Classificação de Receitas Culinárias Baseadas em Documentos em Português Brasileiro

Britto¹,

Oliveira²,

Pacífico³

et al. 2019

Anais Do XVI Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

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Neste trabalho, a classificação de receitas culinárias é abordada através da elaboração de uma ferramenta computacional própria para a análise de documentos textuais escritos em Português. A ferramenta proposta será parte fundamental no desenvolvimento de sistemas de recomendação de receitas para os brasileiros, no intuito do incentivo à prática de hábitos alimentares saudáveis por essa população. Uma base de dados nova, obtida através de páginas web brasileiras, é elaborada e testada pelo uso de algoritmos obtidos da literatura de Aprendizagem de Máquina. Experimentos foram efetuados no intuito da seleção dos melhores classificadores para a composição dos módulos de reconhecimento dos sistemas de recomendação a serem desenvolvidos.

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Classifying Political Tweets Using Naïve Bayes and Support Vector Machines

Cited by 13 publications

References 13 publications

Sentiment Analysis with Machine Learning Methods on Social Media

Sentiment Analysis with Machine Learning Methods on Social Media

Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach

Uma Abordagem de Análise de Textos para a Classificação de Receitas Culinárias Baseadas em Documentos em Português Brasileiro

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