Solving the twitter sentiment analysis problem based on a machine learning-based approach

Kermani, Fatemeh Zarisfi; Sadeghi, Faramarz; Eslami, Esfandiar

doi:10.1007/s12065-019-00301-x

Cited by 21 publications

(6 citation statements)

References 20 publications

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“…However, Adwan et al [ 32 ] also reviewed a large number of techniques and they found a mix of accuracy scores, with some papers passing 80% accuracy while others still perform below 80% even with new algorithms [ 33 ]. Among those who have improved their accuracy, some only focus on specific politics-related data sets [ 34 ], some propose methods that require a large number of steps [ 35 ], while others address the issues with tweets, such as Twitter-specific language [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Monselise¹,

Chang²,

Ferreira³

et al. 2021

J Med Internet Res

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Background As a number of vaccines for COVID-19 are given emergency use authorization by local health agencies and are being administered in multiple countries, it is crucial to gain public trust in these vaccines to ensure herd immunity through vaccination. One way to gauge public sentiment regarding vaccines for the goal of increasing vaccination rates is by analyzing social media such as Twitter. Objective The goal of this research was to understand public sentiment toward COVID-19 vaccines by analyzing discussions about the vaccines on social media for a period of 60 days when the vaccines were started in the United States. Using the combination of topic detection and sentiment analysis, we identified different types of concerns regarding vaccines that were expressed by different groups of the public on social media. Methods To better understand public sentiment, we collected tweets for exactly 60 days starting from December 16, 2020 that contained hashtags or keywords related to COVID-19 vaccines. We detected and analyzed different topics of discussion of these tweets as well as their emotional content. Vaccine topics were identified by nonnegative matrix factorization, and emotional content was identified using the Valence Aware Dictionary and sEntiment Reasoner sentiment analysis library as well as by using sentence bidirectional encoder representations from transformer embeddings and comparing the embedding to different emotions using cosine similarity. Results After removing all duplicates and retweets, 7,948,886 tweets were collected during the 60-day time period. Topic modeling resulted in 50 topics; of those, we selected 12 topics with the highest volume of tweets for analysis. Administration and access to vaccines were some of the major concerns of the public. Additionally, we classified the tweets in each topic into 1 of the 5 emotions and found fear to be the leading emotion in the tweets, followed by joy. Conclusions This research focused not only on negative emotions that may have led to vaccine hesitancy but also on positive emotions toward the vaccine. By identifying both positive and negative emotions, we were able to identify the public's response to the vaccines overall and to news events related to the vaccines. These results are useful for developing plans for disseminating authoritative health information and for better communication to build understanding and trust.

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Section: Introductionmentioning

confidence: 99%

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Monselise¹,

Chang²,

Ferreira³

et al. 2021

J Med Internet Res

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“…Zarisfi et al . [ 25 ] used SVM and MNB with TF-IDF extraction on four Twitter datasets, namely the Strict Obama-McCain Debate dataset, the Obama-McCain Debate dataset, the STS-Gold dataset, and the Stanford testing dataset. Semantic scoring based on tweet class, semantic similarity, SWN scoring, and TF-IDF methods have been suggested for representing the features in the vector space.…”

Section: Background Literaturementioning

confidence: 99%

Enhancing machine learning-based sentiment analysis through feature extraction techniques

A. Semary,

Ahmed,

Amin

et al. 2024

PLoS ONE

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A crucial part of sentiment classification is featuring extraction because it involves extracting valuable information from text data, which affects the model’s performance. The goal of this paper is to help in selecting a suitable feature extraction method to enhance the performance of sentiment analysis tasks. In order to provide directions for future machine learning and feature extraction research, it is important to analyze and summarize feature extraction techniques methodically from a machine learning standpoint. There are several methods under consideration, including Bag-of-words (BOW), Word2Vector, N-gram, Term Frequency- Inverse Document Frequency (TF-IDF), Hashing Vectorizer (HV), and Global vector for word representation (GloVe). To prove the ability of each feature extractor, we applied it to the Twitter US airlines and Amazon musical instrument reviews datasets. Finally, we trained a random forest classifier using 70% of the training data and 30% of the testing data, enabling us to evaluate and compare the performance using different metrics. Based on our results, we find that the TD-IDF technique demonstrates superior performance, with an accuracy of 99% in the Amazon reviews dataset and 96% in the Twitter US airlines dataset. This study underscores the paramount significance of feature extraction in sentiment analysis, endowing pragmatic insights to elevate model performance and steer future research pursuits.

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“…We applied various common performance metrics to evaluate how well the suggested model performs. We specifically applied F1-measure, recall, accuracy, and precision ( Al Amrani et al, 2018 ; Zarisfi Kermani et al, 2020 ). A confusion matrix can be used to illustrate the DL model and generate all four metrics.…”

Section: Proposed Systemmentioning

confidence: 99%

Improving sentiment classification using a RoBERTa-based hybrid model

Semary,

Ahmed,

Amin

et al. 2023

Front. Hum. Neurosci.

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IntroductionSeveral attempts have been made to enhance text-based sentiment analysis’s performance. The classifiers and word embedding models have been among the most prominent attempts. This work aims to develop a hybrid deep learning approach that combines the advantages of transformer models and sequence models with the elimination of sequence models’ shortcomings.MethodsIn this paper, we present a hybrid model based on the transformer model and deep learning models to enhance sentiment classification process. Robustly optimized BERT (RoBERTa) was selected for the representative vectors of the input sentences and the Long Short-Term Memory (LSTM) model in conjunction with the Convolutional Neural Networks (CNN) model was used to improve the suggested model’s ability to comprehend the semantics and context of each input sentence. We tested the proposed model with two datasets with different topics. The first dataset is a Twitter review of US airlines and the second is the IMDb movie reviews dataset. We propose using word embeddings in conjunction with the SMOTE technique to overcome the challenge of imbalanced classes of the Twitter dataset.ResultsWith an accuracy of 96.28% on the IMDb reviews dataset and 94.2% on the Twitter reviews dataset, the hybrid model that has been suggested outperforms the standard methods.DiscussionIt is clear from these results that the proposed hybrid RoBERTa–(CNN+ LSTM) method is an effective model in sentiment classification.

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Solving the twitter sentiment analysis problem based on a machine learning-based approach

Cited by 21 publications

References 20 publications

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis

Enhancing machine learning-based sentiment analysis through feature extraction techniques

Improving sentiment classification using a RoBERTa-based hybrid model

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