Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. Despite earlier studies’ good performance, these methods lack syntactic information to guide feature development for contextual semantic linkages in social media text. In this paper, we introduce an enhanced LSTM-based on dependency parsing and a graph convolutional network (DPG-LSTM) for sentiment analysis. Our research aims to investigate the importance of syntactic information in the task of social media emotional processing. To fully utilize the semantic information of social media, we adopt a hybrid attention mechanism that combines dependency parsing to capture semantic contextual information. The hybrid attention mechanism redistributes higher attention scores to words with higher dependencies generated by dependency parsing. To validate the performance of the DPG-LSTM from different perspectives, experiments have been conducted on three tweet sentiment classification datasets, sentiment140, airline reviews, and self-driving car reviews with 1,604,510 tweets. The experimental results show that the proposed DPG-LSTM model outperforms the state-of-the-art model by 2.1% recall scores, 1.4% precision scores, and 1.8% F1 scores on sentiment140.

Accurate estimation of ambient PM_2.5 concentrations is crucial for assessing air quality and health risks, particularly in regions with limited ground-based monitoring. Satellite-retrieved data products, such as top-of-atmosphere reflectance (TOAR) and aerosol optical depth (AOD), are widely used for PM_2.5 estimation. However, complex atmospheric conditions cause retrieval gaps in TOAR and AOD products, limiting their reliability. This study introduced a spatiotemporal convolutional approach to fill sampling gaps in TOAR and AOD data from the Himawari-8 geostationary satellite over the Yangtze River Delta (YRD) in 2016. Four machine-learning models (random forest, extreme gradient boosting, gradient boosting, and support vector regression) were used to estimate hourly PM_2.5 concentrations by integrating gap-filled and original TOAR and AOD data with meteorological variables. The random forest model trained on gap-filled TOAR data yielded the highest predictive accuracy (R² = 0.75, RMSE = 18.30 μg m⁻³). Significant seasonal variations in PM_2.5 estimates were found, with TOAR-based models outperforming AOD-based models. Furthermore, we observed that a substantial portion of the YRD population in non-attainment areas is at risk of cardiovascular disease due to chronic PM_2.5 exposure. This study suggests that TOAR-based models offer more reliable PM_2.5 estimates, enhancing air-quality assessments and public health-risk evaluations.

The interplay between strategic orientations has received limited attention in manufacturing small and medium enterprises (SMEs).The existing studies have repeatedly called for investigating the impacts of multiple strategic orientations on organizational performance. Moreover, prior research called for disintegrating the strategic orientation and assessing their effects on organizational performance. Thus, this study is the first of its nature – disintegrating the market orientation (MO) construct and using data from 213 SMEs of Pakistan, collected through survey method, which reports on how the deployment of entrepreneurial orientation (EO) as moderator produces synergistic effect and strengthens the MO dimensions –organizational performance links. The results indicate that the MO dimensions are positively and significantly related to organizational performance. Moreover, EO significantly and positively moderates the MO dimensions and organizational performance relationships. The study provides avenues for future research.