On the frontiers of Twitter data and sentiment analysis in election prediction: a review.

Election prediction using sentiment analysis is a rapidly growing field that utilizes natural language processing and machine learning techniques to predict the outcome of political elections by analyzing the sentiment of online conversations and news articles. Sentiment analysis, or opinion mining, involves using text analysis to identify and extract subjective information from text data sources. In the context of election prediction, sentiment analysis can be used to gauge public opinion and predict the likely winner of an election. Significant progress has been made in election prediction in the last two decades. Yet, it becomes easier to have its comprehensive view if it has been appropriately classified approach-wise, citation-wise, and technology-wise. The main objective of this article is to examine and consolidate the progress made in research about election prediction using Twitter data. The aim is to provide a comprehensive overview of the current state-of-the-art practices in this field while identifying potential avenues for further research and exploration.

Mass-Producible 2D Nanocomposite-Based Temperature-Independent All-Printed Relative Humidity Sensor.

Relative humidity sensors are widely studied under the categories of both environmental and biosensors owing to their vast reaching applications. The research on humidity sensors is mainly divided into two concentration areas including novel material development and novel device structure. Another approach focuses on the development of printed sensors with performance comparable to the sensors fabricated via conventional techniques. The major challenges in the research on relative humidity sensors include the range of detection, sensitivity (especially at lower %RH), transient response time, and dependence on temperature. Temperature dependence is one of the least studied parameters in relative humidity sensor development. In this work, relative humidity sensors were fabricated using all-printed approaches that are also compatible with mass production, resulting in low cost and easy development. Laser-induced graphene (LIG)-based printed electrodes were used as the transducers, while the 2D MoS2 and graphene nanocomposite was used as the active layer material with the built-in property of temperature independence. The exfoliation process of 2D MoS2 was based on wet grinding, while graphene for the active layer was obtained by scratching the graphene grown on the polyimide (PI) surface via laser ablation. The resulting sensors showed an excellent output response for a full range of 0%RH to 100%RH, having no dependence on the surrounding temperature, and excellent response and recovery times of 4 and 2 s, respectively. The developed sensors can be confidently employed for a wide range of humidity sensing applications where the temperature of the surrounding environment is not constant.