Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry

Aljedaani, Wajdi; Rustam, Furqan; Mkaouer, Mohamed Wiem; Ghallab, Abdullatif; Rupapara, Vaibhav; Washington, Patrick Bernard; Lee, Ernesto; Ashraf, Imran

doi:10.1016/j.knosys.2022.109780

Cited by 74 publications

(30 citation statements)

References 22 publications

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“…Martín et al ( 2018 ) used hotel-related reviews to carry out comparative experiments using CNN and LSTM to conduct sentiment analysis texts. Aljedaani et al ( 2022 ) conducted sentiment analysis on online reviews of six US airlines, mainly using four dictionary-based and deep learning models including CNN, LSTM, etc. In addition, sentiment analysis of travel reviews has some research basis, but the research methods basically stay in the traditional techniques based on word filtering, co-occurrence analysis and semantic clustering (Ainin et al, 2020 ; Jardim and Mora, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Language interpretation in travel guidance platform: Text mining and sentiment analysis of TripAdvisor reviews

Miao

Chen²,

Yang³

et al. 2022

Front. Psychol.

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The opinions and feelings expressed by tourists in their reviews intuitively represent tourists' evaluation of travel destinations with distinct tones and strong emotions. Both consumers and product/service providers need help understanding and navigating the resulting information spaces, which are vast and dynamic. Traditional sentiment analysis is mostly based on statistics, which can analyze the sentiment of a large number of texts. However, it is difficult to classify the overall sentiment of a text, and the context-independent nature limits their representative power in a rich context, hurting performance in Natural Language Processing (NLP) tasks. This work proposes an aspect-based sentiment analysis model by extracting aspect-category and corresponding sentiment polarity from tourists' reviews, based on the Bidirectional Encoder Representation from Transformers (BERT) model. First, we design a text enhancement strategy which utilizes iterative translation across multiple languages, to generate a dataset of 4,000 reviews by extending a dataset of 2,000 online reviews on 1,000 tourist attractions. Then, the enhanced dataset is reorganized into 10 classifications by the Term Frequency-Inverse Document Frequency (TF-IDF) method. Finally, the aspect-based sentiment analysis is performed on the enhanced dataset, and the obtained sentiment polarity classification and prediction of the tourism review data make the expectations and appeals in tourists' language available. The experimental study generates generic and personalized recommendations for users based on the emotions in the language and helps merchants achieve more effective service and product upgrades.

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Section: Introductionmentioning

confidence: 99%

Language interpretation in travel guidance platform: Text mining and sentiment analysis of TripAdvisor reviews

Miao

Chen²,

Yang³

et al. 2022

Front. Psychol.

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“…The results showed a lower performance with traditional machine together with deep learning models. This shows that AFINN annotated tweets dataset could produce lower accuracy score than tweets annotated with Textblob or VADER [21].…”

Section: B Sentiment Analysis With Afinnmentioning

confidence: 91%

“…Textblob assigns score of −1 and 1 to each word based on the polarity and subjectivity of the text. In [21], Textblob was used to annotate US airline dataset containing 14 640 tweets reviews. The annotated dataset was trained on six supervised traditional machine learning models.…”

Section: A Sentiment Analysis With Textblobmentioning

confidence: 99%

“…VADER means Valence Aware Dictionary for sEntiment Reasoning [21]. It generates sentiment scores or intensities using a dictionary by mapping the lexicon features.…”

Section: Sentiment Analysis With Vadermentioning

confidence: 99%

“…The manual annotation of tweet is said to be the most reliable option. But, it is greeted with human bias and can be time consuming when working on a large amount of dataset [21]. The automatic annotation of tweets involves the use of pretrained models that recognize polarity of words in a given text.…”

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confidence: 99%

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Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Ogbuokiri

Ahmadi

Nia

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Many social media users express concerns about vaccines and their side effects on Twitter. These concerns lead to a compromise of confidence which brings about vaccine hesitancy. In Africa, vaccine hesitancy is a major challenge faced by health policymakers in the fight against COVID-19. Given that most tweets are geotagged, clustering them according to their sentiments could help identify locations that may likely experience vaccine hesitancy for health policy and planning. In this study, we collected 70 000 geotagged vaccine-related tweets in nine African countries, from December 2020 to February 2022. The tweets were classified into three sentiment classespositive, negative, and neutral. The quality of the classification outputs was achieved using Naíve Bayes (NB), logistic regression (LR), support vector machines (SVMs), decision tree (DT), and K-nearest neighbor (KNN) machine learning classifiers. The LR achieved the highest accuracy of 71% with an average area under the curve of 85%. The point-based location technique was used to calculate the hotspots based on the locations of the classified tweets. Locations with green, red, and gray backgrounds on the map signify a hotspot for positive, negative, and neutral sentiments. The outcome of this research shows that discussions on social media can be analyzed to identify hotspots during a disease outbreak, which could inform health policy in planning and management of vaccine hesitancy in Africa.

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