The use of sentiment analysis has been increasing over time. Sentiment analysis was born in computer science, but researchers have begun to use this technique in different disciplines as it can be used in various fields. As competition increases, businesses have understood that they need to improve the quality of their services to satisfy their customers. The processing of human behavior data with sentiment analysis has become an important factor in determining the quality of service for businesses. In this study, bibliometric analyses were carried out to determine the place of sentiment analysis in the context of service quality in the literature. To achieve the aim of the study, first, a bibliometric analysis of studies related to sentiment analysis was carried out. Then, a bibliometric analysis of the use of sentiment analysis in the context of service quality was carried out, and the sentiment analysis technique was evaluated in the context of service quality in the literature. In addition to bibliometric analyses, the titles and abstracts of sentiment analysis studies in the context of service quality were analyzed using content analysis to determine the subjects and techniques of the studies. It was noted that the use of the sentiment analysis technique in the field of service quality began in 2016 and that there are still few studies in this area, and that it is used in certain sectors.

Equality of opportunity in education requires access to quality learning opportunities for all, but socioeconomic inequalities, geographical barriers, disability and the digital divide limit this access. Artificial intelligence (AI) technologies can reduce these inequalities through personalized learning, disability-accessible content and multilingual-cultural adaptations. They can also ease teachers' workload and facilitate early identification of at-risk students. However, the benefits of AI may not trickle down equally to all groups; lack of access to technology and biases in algorithms may exclude some groups. AI tools therefore need to be transparent, explainable and ethically grounded. This chapter examines the dimensions of the digital divide, the contributions of AI, the effects of algorithmic biases, and teachers' AI literacy, and offers recommendations for inclusive AI design and policy.

This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods' time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the model’s purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords "flood", "meter", "rain", "irrigation", "infrastructure", "sewerage", "sewage", "maintenance hole ", "aski", "waterless", "water" were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.

The intermittent nature of photovoltaic power generation makes short-term forecasting critical for grid management. In this study, a hybrid model combining a one-dimensional convolutional neural network (1D-CNN), Long Short-Term Memory (LSTM), and multi-head attention (MHA) mechanism was developed and evaluated on a 300-day dataset at 15 min (H1) and 60 min (H4) forecast horizons. The model generates prediction intervals with 80% nominal coverage using a quantile regression approach trained with the pinball loss function. The hyperparameters were determined through Optuna-based Tree-structured Parzen Estimator (TPE) optimisation. The LSTM, 1D-CNN-LSTM, LSTM-MHA, and 1D-CNN-LSTM-MHA models were compared under the same experimental setting. The 1D-CNN-LSTM-MHA model achieved the best deterministic performance at both forecast horizons. At the H1 horizon, R² = 0.9370 and nRMSE = 7.13% were obtained, whereas at the H4 horizon, R² = 0.9327 and nRMSE = 7.37% were achieved. In the probabilistic evaluation, this model produced the lowest PINAW and Winkler score values. In the statistical comparison, the performance differences in the 1D-CNN-LSTM-MHA model relative to the LSTM reference model were statistically significant in the DM-MAE, DM-RMSE, Clark–West, and Fisher-Z tests at both horizons. The statistical results indicate that the contribution of the attention mechanism becomes more evident when combined with the convolutional component, whereas adding attention alone to the LSTM did not produce a statistically significant improvement. This study provides a comparative evaluation framework for short-term PV active power forecasting by combining probabilistic forecasting through quantile regression with statistical model comparison.