PurposeAs the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.Design/methodology/approachThis paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.FindingsThe performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.Practical implicationsThe proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.Originality/valueThis study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

Purpose Due to an immense rise of social media in recent years, the purpose of this paper is to investigate who, how and why participates in creating content at football websites. Specifically, it provides a sentiment analysis of user comments from gender perspective, i.e. how differently men and women write about football. The analysis is based on user comments published on Facebook pages of the top five 2015-2016 Premier League football clubs during the 1st and the 19th week of the season. Design/methodology/approach This analysis uses a data collection via social media website and a sentiment analysis of the collected data. Findings Results show certain unexpected similarities in social media activities between male and female football fans. A comparison of the user comments from Facebook pages of the top five 2015-2016 Premier League football clubs revealed that men and women similarly express hard emotions such as anger or fear, while there is a significant difference in expressing soft emotions such as joy or sadness. Originality/value This paper provides an original insight into qualitative content analysis of male and female comments published at social media websites of the top five Premier League football clubs during the 1st and the 19th week of the 2015-2016 season.

Purpose Due to the significant rise in the use of social media in recent years, the purpose of this paper is to investigate who, how and why participates in creating content at political social networking websites utilising a content analysis of posts and comments published on Facebook during the 2015 general election campaign in Croatia. It shows consequences of a transition from traditional to social media campaigns and the effectiveness of social media at activating and moving public opinion during the general election campaign. Design/methodology/approach This study uses a data collection through a social media website, a classification of data set items by content attributes and a statistical analysis of the classified data. Findings Building on an empirical data set from Croatia, this study reveals that different political parties implement different election campaign strategies on social media to influence citizens who, consequently, respond differently to each of them. The results indicate that political messages with positive emotions evocate positive response from citizens, while neutral content is more likely to invoke negative comments and criticism, and support to the opponent. Another implication of the results is that two-way and tolerant communication of political actors increases citizen engagement, whereas unidirectional communication decreases it. Originality/value This paper provides an original insight into qualitative content analysis of posts and user comments published on Facebook during the 2015 general election campaign in Croatia.

Nowadays, fake news is one of major concerns in our society, that is a form of news consisting of deliberate disinformation or hoaxes spread via traditional news media or online social media. Thus, this study aims to explore state-of-theart methods for detecting fake news in order to design and implement classification models. Four different classification models based on deep learning with selfattention mechanism were trained and evaluated using current datasets that are available for this purpose. Three models explored traditional supervised learning, while the fourth model explored transfer learning by fine-tuning the pre-trained language model for the same task. All four models yield comparable results with the fourth model achieving the best classification accuracy.

Purpose Social media allow for observing different aspects of human behaviour, in particular, those that can be evaluated from explicit user expressions. Based on a data set of posts with user opinions collected from social media, this paper aims to show an insight into how the readers of different news portals react to online content. The focus is on users’ emotions about the content, so the findings of the analysis provide a further understanding of how marketers should structure and deliver communication content such that it promotes positive engagement behaviour. Design/methodology/approach More than 5.5 million user comments to posted messages from 15 worldwide popular news portals were collected and analysed, where each post was evaluated based on a set of variables that represent either structural (e.g. embedded in intra- or inter-message structure) or behavioural (e.g. exhibiting a certain behavioural pattern that appeared in response to a posted message) component of expressions. The conclusions are based on a set of regression models and exploratory factor analysis. Findings The findings show and theorise the influence of social media content on emotional user engagement. This provides a more comprehensive understanding of the engagement attributed to social media content and, consequently, could be a better predictor of future behaviour. Originality/value This paper provides original data analysis of user comments and emotional reactions that appeared on social media news websites in 2018.