"Huang S., Wu X., Wu X., Wang K.",Sentiment analysis algorithm using contrastive learning and adversarial training for POI recommendation,2023,"Finding a suitable POI based on the user’s needs and intentions is a complex decision-making process. Obtaining valuable information from the vast amount of social media data and using it for travel recommendations is a challenging issue. Traditional POI recommendation algorithms do not fully take into account the true feelings of customers about tourist attractions implied in social media data because they usually require a large amount of tagged travel commentary data. This study presents an aspect-based sentiment analysis model and POI recommendation method to accurately capture sentiment information contained in social media data with a small amount of tagged data. The pre-training model BERT is used to obtain the embedded representation of words that fuse the semantic information of the text. Using contrastive learning, point clusters belonging to the same class in the embedded space of words are pulled together, and sample clusters from different classes are separated. The potential relationship between comment ratings and their impact on user perception is analyzed, and the best performance formula for the loss function is determined. The test accuracy and F1-Score of the model in the experiment improved by 13.03% and 12.23%, respectively, compared to the BERT base model. POI recommendation validation is performed using a variety of recommendation algorithms. The experimental results show that the addition of aspect-based sentiment attributes can effectively improve the accuracy of recommendations. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.",BERT; Contrastive learning; POI recommendation; Sentiment analysis
"Alotaibi W., Alomary F., Mokni R.",COVID-19 vaccine rejection causes based on Twitter people’s opinions analysis using deep learning,2023,"According to the World Health Organization, vaccine hesitancy was one of the ten major threats to global health in 2019, including the COVID-19 vaccine. The availability of vaccines does not always mean utilization. This is because, people have less confidence in vaccines, which resulted in vaccination hesitancy and developing global decline in vaccine intake and has caused viral disease outbreaks worldwide. Therefore, there is a need to understand people’s perceptions about the COVID-19 vaccine to help the manufacturing companies of the vaccine to improve their marketing strategy based on the rejection causes. In this paper, we used multi-class Sentiment Analysis to classify people’s opinions from extracted tweets about COVID-19 vaccines, using firstly different Machine Learning (ML) classifiers such as Logistic Regression (LR), Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbors, Decision Tree (DT), Multinomial Naïve Bayes, Random Forest and Gradient Boosting and secondly various Deep Learning (DL) models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), RNN-LSTM and RNN-GRU. Then, we investigated the analysis of the negative tweets to identify the causes of rejection using the Latent Dirichlet Allocation (LDA) technique. Finally, we classified these negative tweets according to the rejection causes for all the vaccines using the same selected ML and DL models. The result of SA showed that DT gives the best performance with an accuracy of 92.26% and for DL models, GRU achieved 96.83%. Then, we identified five causes: Lack of safety, Side effect, Production problem, Fake news and Misinformation, and Cost. Furthermore, for the classification of the negative tweets according to the identified rejection causes, the LR achieved the best result with an accuracy of 89.97%. For DL models, the LSTM model showed the best result with an accuracy of 91.66%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.",COVID-19 vaccines; Deep learning; GRU-LSTM; Latent Dirichlet allocation; Sentiment analysis; Vaccine rejection causes
"Yang Y., Li Y., Chen K., Liu J.",Jeu de mots paronomasia: a StackOverflow-driven bug discovery approach,2023,"Locating bug code snippets (short for BugCode) has been a complex problem throughout the history of software security, mainly because the constraints that define BugCode are obscure and hard to summarize. Previously, security analysts attempted to define such constraints manually (e.g., limiting buffer size to detect overflow), but were limited to the types of BugCode. Recent researchers address this problem by extracting constraints from program documentation, which shows the potential for API misuse. But for bugs beyond the scope of API misuse, such an approach becomes less effective since the corresponding constraints are not defined in documents, not to mention the programs without documentation In this paper, inspired by the fact that expert programmers often correct the BugCode on open forums such as StackOverflow, we design an approach to automatically extract knowledge from StackOverflow and leverage it to detect BugCode. As we all know, the contexts in StackOverflow come from ordinary developers. Their writing tends to be loosely organized and in various styles, which are more challenging to analyze than program documentation. To address the challenges, we design a custom tokenization approach to segment sentences and employ sentiment analysis to find the Controversial Sentences (CSs) that typically contain the constraints we need for code analysis. Then we use constituency parsing to extract knowledge from CSs, which helps locate BugCode. We evaluated our system on 41,144 comments from the questions tagged with Java and Android. The results show that our approach achieves 95.5% precision in discovering CSs. We have discovered 276 pieces of BugCode proved to be true through manual validation including an assigned CVE. 89.3% of the discovered bugs remained in the current version of answers, which are unknown to users. © 2023, The Author(s).",Bug detection; Natural language processing; Open forum
"Fadhli I., Hlaoua L., Omri M.N.",Deep learning-based credibility conversation detection approaches from social network,2023,"In recent years, the social networks that have become most exploited sources of information, such as Facebook, Instagram, LinkedIn, and Twitter, have been considered the main sources of non-credible information. False information on these social networks has a negative impact on the credibility of conversations. In this article, we propose a new deep learning-based credibility conversation detection approach in social network environments, called CreCDA. CreCDA is based on: (i) the combination of post and user features in order to detect credible and non-credible conversations; (ii) the integration of multi-dense layers to represent features more deeply and to improve the results; (iii) sentiment calculation based on the aggregation of tweets. In order to study the performance of our approach, we have used the standard PHEME dataset. We compared our approach with the main approaches we have studied in the literature. The results of this evaluation show the effectiveness of sentiment analysis and the combination of text and user levels to analyze conversation credibility. We recorded the mean precision of credible and non-credible conversations at 79%, the mean recall at 79%, the mean F1-score at 79%, the mean accuracy at 81%, and the mean G-Mean at 79%. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.",Credibility detection; Deep learning; Post features; Sentiment analysis; Twitter conversation; User features
"Zou H., Wang Z.",A semi-supervised short text sentiment classification method based on improved Bert model from unlabelled data,2023,"Short text information has considerable commercial value and immeasurable social value. Natural language processing and short text sentiment analysis technology can organize and analyze short text information on the Internet. Natural language processing tasks such as sentiment classification have achieved satisfactory performance under a supervised learning framework. However, traditional supervised learning relies on large-scale and high-quality manual labels and obtaining high-quality label data costs a lot. Therefore, the strong dependence on label data hinders the application of the deep learning model to a large extent, which is the bottleneck of supervised learning. At the same time, short text datasets such as product reviews have an imbalance in the distribution of data samples. To solve the above problems, this paper proposes a method to predict label data according to semi-supervised learning mode and implements the MixMatchNL data enhancement method. Meanwhile, the Bert pre-training model is updated. The cross-entropy loss function in the model is improved to the Focal Loss function to alleviate the data imbalance in short text datasets. Experimental results based on public datasets indicate the proposed model has improved the accuracy of short text sentiment recognition compared with the previous update and other state-of-the-art models. © 2023, The Author(s).",Data enhancement; Data imbalance; Language models; Semi-supervised learning; Sentiment analysis
"Rita P., António N., Afonso A.P.",Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period,2023,"In a time where social media is fundamental for any political campaign and to share a message with an electoral audience, this study searches for a conclusion of the actual persuasion capacity of social media in the electors when they need to decide whom to vote for as their next government. For this, it compares the sentiment that Social Media users demonstrated during an electoral period with the actual results of those elections. For this analysis, it was used, as a case study, tweets mentioning the two major English parties, Conservative and Labor, their respective candidates for the position of prime minister, and terms that identified their political campaign during the electoral period of the General Elections of the United Kingdom that occurred on December 12, 2019. Data were collected using R. The treatment and analysis were done with R and RapidMiner. Results show that tweets’ sentiment is not a reliable election results predictor. Additionally, results also show that it is impossible to state that social media impacts voting decisions. At least not from the polarity of the sentiment of opinions on social media. © 2023, The Author(s).",Elections; Politics; Sentiment analysis; Social media
"Tasnia R., Ayman N., Sultana A., Chy A.N., Aono M.",Exploiting stacked embeddings with LSTM for multilingual humor and irony detection,2023,"Humor and irony are types of communication that evoke laughter or contain hidden sarcasm. The opportunity to diversely express people’s opinions on social media using humorous content increased its popularity. Due to subjective aspects, humor may vary to gender, profession, generation, and classes of people. Detecting and analyzing humorous and ironic emplacement of informal user-generated content is crucial for various NLP and opinion mining tasks due to its perplexing characteristics. However, due to the idiosyncratic characteristics of informal texts, it is a challenging task to generate an effective representation of texts to understand the inherent contexts properly. In this paper, we propose a neural network architecture that couples a stacked embeddings strategy on top of the LSTM layer for the effective representation of textual context and determine the humorous and ironic orientation of texts in an efficient manner. Here, we perform the stacking of various fine-tuned word embeddings and transformer models including GloVe, ELMo, BERT, and Flair’s contextual embeddings to extract the diversified contextual features of texts. Later, we use the LSTM network on top of it to generate the unified document vector (UDV). Finally, the UDV is passed to the multiple feed-forward linear architectures for attaining the final prediction labels. We present the performance analysis of our proposed approach on benchmark datasets of English and Spanish language. Experimental outcomes exhibited the preponderance of our model over most of the state-of-the-art methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.",BERT; Feed-forward linear architecture; Flair; Humor; Irony; Stacked embeddings
above contains Authors,Title,Year,Abstract,Author Keywords in a csv format create a  litrature review  according to relevent keywords for text / sentiment analysis explain each keyword and then what the paperss say about it