Purpose: The main purpose of this paper is to analyze a tourist destination using sentiment analysis techniques with data from Twitter and Instagram to find the most representative entities (or places) and perceptions (or aspects) of the users. Design/methodology/approach: The authors used 90,725 Instagram posts and 235,755 Twitter tweets to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers on both social media sites. The authors used several approaches for sentiment classification for English and Spanish texts, including deep learning models. Findings: The best results in a test set were obtained using a bidirectional encoder representations from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were subsequently used to analyze the data sets. It was then possible to identify the most important entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners, travelers and tourism managers so that services could be improved and better marketing strategies formulated. Research limitations/implications: The authors propose a Spanish-Tourism-BERT model for performing sentiment classification together with a process to find places through hashtags and to reveal the important negative aspects of each place. Practical implications: The study enables managers and practitioners to implement the Spanish-BERT model with our Spanish Tourism data set that the authors released for adoption in applications to find both positive and negative perceptions. Originality/value: This study presents a novel approach on how to apply sentiment analysis in the tourism domain. First, the way to evaluate the different existing models and tools is presented; second, a model is trained using BERT (deep learning model); third, an approach of how to identify the acceptance of the places of a destination through hashtags is presented and, finally, the evaluation of why the users express positivity (negativity) through the identification of entities and aspects.

A more accurate crop yield estimation is essential for optimizing agricultural productivity and resource management. Traditional machine learning models, such as linear regression and convolutional neural networks (CNNs), often struggle to integrate multimodal data sources effectively, limiting their predictive accuracy. In this study, we propose the AgriTransformer model, a transformer-based model that enhances crop yield prediction by leveraging attention mechanisms for multimodal data fusion. The AgriTransformer model incorporates tabular agricultural data and vegetation indices (VI), allowing dynamic feature interaction and improved interpretability. Experimental results have demonstrated that AgriTransformer significantly outperforms conventional approaches, achieving an R² of 0.919, compared to 0.884 for the best-performing linear regression model. The findings highlight the importance of structured tabular data in yield estimation, while VI serves as a complementary feature that increases the prediction capability and confidence. This study highlights the potential of transformer-based architectures in precision agriculture, offering a scalable and adaptable framework for crop yield forecasting. The AgriTransformer model enhances predictive accuracy and generalization across diverse agricultural conditions by prioritizing relevant features through attention mechanisms.

Purpose: The main aim of this paper is to build an approach to analyze the tourist content posted on social media. The approach incorporates information extraction, cleaning, data processing, descriptive and content analysis and can be used on different social media platforms such as Instagram, Facebook, etc. This work proposes an approach to social media analytics in traveler-generated content (TGC), and the authors use Twitter to apply this study and examine data about the city and the province of Granada.Design/methodology/approach: In order to identify what people are talking and posting on social media about places, events, restaurants, hotels, etc. the authors propose the following approach for data collection, cleaning and data analysis. The authors first identify the main keywords for the place of study. A descriptive analysis is subsequently performed, and this includes post metrics with geo-tagged analysis and user metrics, retweets and likes, comments, videos, photos and followers. The text is then cleaned. Finally, content analysis is conducted, and this includes word frequency calculation, sentiment and emotion detection and word clouds. Topic modeling was also performed with latent Dirichlet association (LDA).Findings: The authors used the framework to collect 262,859 tweets about Granada. The most important hashtags are #Alhambra and #SierraNevada, and the most prolific user is @AlhambraCultura. The approach uses a seasonal context, and the posted tweets are divided into two periods (spring–summer and autumn–winter). Word frequency was calculated and again Granada, Alhambra are the most frequent words in both periods in English and Spanish. The topic models show the subjects that are mentioned in both languages, and although there are certain small differences in terms of language and season, the Alhambra, Sierra Nevada and gastronomy stand out as the most important topics.Research limitations/implications: Extremely difficult to identify sarcasm, posts may be ambiguous, users may use both Spanish and English words in their tweets and tweets may contain spelling mistakes, colloquialisms or even abbreviations. Multilingualism represents also an important limitation since it is not clear how tweets written in different languages should be processed. The size of the data set is also an important factor since the greater the amount of data, the better the results. One of the largest limitations is the small number of geo-tagged tweets as geo-tagging would provide information about the place where the tweet was posted and opinions of it.Originality/value: This study proposes an interesting way to analyze social media data, bridging tourism and social media literature in the data analysis context and contributes to discover patterns and features of the tourism destination through social media. The approach used provides the prospective traveler with an overview of the most popular places and the major posters for a particular tourist destination. From a business perspective, it informs managers of the most influential users, and the information obtained can be extremely useful for managing their tourism products in that region.

Introduction: The use of information and communication technologies such as the Internet, smartphones, social media, and gaming has gained significant popularity in recent years. While the benefits are immense and ICTs have become essential in people's daily lives, the inappropriate use of these technologies has led to addiction, causing negative consequences in family, academic, and work environments.Methods: This study analyzes existing research related to ICT addiction (Internet, smartphone, social media, and gaming), reviewing relevant contributions. Historical trends, regions, relevance, factors, and instruments were analyzed to map out the existing research on ICT addiction.Results and discussion: The findings revealed that although the number of relevant studies has grown in recent years, there is still a lack of attention on ICT addiction and its relationship with psychological factors, social factors, physical factors, phenomenological experiences, and treatment/prevention approaches. In this regard, psychology scholars should consider appropriate methods to raise awareness about ICT addiction and emphasize the need for an in-depth understanding of the meaning, context, and practices associated with Internet, smartphone, social media, and gaming addiction.

Wind energy is a non-programmable form of generation, hence, accurate and reliable wind energy prediction is of great importance for the efficient operation of wind farms. This article presents a study for the prediction of active power for the Villonaco Wind Farm (VWF), located in southern Ecuador at approximately 2700 m above sea level. Through the use of artificial neural networks, experimental tests are developed based on the models of Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) to obtain a hybrid model that fits the best characteristics of the individual models. Data from the active power SCADA (Supervisory Control and Data Acquisition) system for the years 2014 to 2018 are used to train and validate the models. Hybrid model is presented as the most appropriate option by the values obtained, viz., the mean absolute error (MAE), the mean squared error (MSE), and mean absolute percentage error (MAPE) that were 0.1365, 0.0974, and 144.26, respectively, outperforming to the others wind power forecast models.