For decades, researchers have experimented with the possibility that machines can equal human linguistic capabilities. Recently, advances in the field of natural language processing (NLP) as well as a substantial increase in available naturally occurring linguistic data on social media platforms have made more advanced methodologies such as sentiment analysis (SA) gain substantial momentum on contemporary applications. This document compiles what the authors consider to be some of the most important concepts related to SA, as well as techniques and processes necessary for the various stages of its implementation. Furthermore, specific applications related to the extraction and classification of social media data using novel SA techniques are presented and quantified, with an emphasis on those used for the identification of mental health degradation during the COVID-19 pandemic. Finally, the authors present several conclusions highlighting the most prominent benefits and drawbacks of the methods discussed, followed by a brief discussion of possible future applications of certain methods of interest.

Harmful algae blooms (HABs) pose critical threats to aquatic ecosystems and human economies, driven by their rapid proliferation, oxygen depletion capacity, toxin release, and biodiversity impacts. These blooms, increasingly exacerbated by climate change, compromise water quality in both marine and freshwater ecosystems, significantly affecting marine life and coastal economies based on fishing and tourism while also posing serious risks to inland water bodies. This article examines the role of hyperspectral imaging (HSI) in monitoring HABs. HSI, with its superior spectral resolution, enables the precise classification and mapping of diverse algae species, emerging as a pivotal tool in environmental surveillance. An array of HSI techniques, algorithms, and deployment platforms are evaluated, analyzing their efficacy across varied geographical contexts. Notably, hyperspectral sensor-based studies achieved up to 90% classification accuracy, with regression-based chlorophyll-a (Chl-a) estimations frequently reaching coefficients of determination ($R^2$) above 0.80. These quantitative findings underscore the potential of HSI for robust HAB diagnostics and early warning systems. Furthermore, we explore the current limitations and future potential of HSI in HAB management, highlighting its strategic importance in addressing the growing environmental and economic challenges posed by HABs. This paper seeks to provide a comprehensive insight into HSI’s capabilities, fostering its integration in global strategies against HAB proliferation.

Hyperspectral imaging has become a valuable remote sensing tool due to the development of advanced remote acquisition systems with high spatial and spectral resolution, and the continuous developments on more efficient computing resources to handle the high volume of data. For this reason, hyperspectral image analysis has found important uses in precision agriculture, where the health status of crops in various stages of the production process can be assessed from their spectral signatures. This has similarly been the case for rice cultivation, which represents one of the most valuable crops worldwide in terms of gross production value, global consumption rates, and food security reserves. To maximize the productivity of this activity and minimize economic and food crop losses, various precision agriculture techniques to optimize yields by managing production inputs and monitoring plant health have been developed. Such applications include landcover classification, cultivar identification, nitrogen level assessment, chlorophyll content estimation and the identification of various factors, such as the presence of pests, weeds, disease or pollutants. The current work highlights and summarizes various aspects of interest of the main studies on hyperspectral imaging applications for rice cultivation. For instance, several tables summarize the most relevant work on the application of hyperspectral imaging for rice cultivation based on their acquisition methods, spectral region, rice species, and inferred magnitudes, among other parameters. In addition, we identify challenges across the field that limit the widespread deployment of hyperspectral imaging applications. Among these challenges, adequate modeling of various dynamic local factors and their influence on the analysis is a main concern. The main objective of this review is to provide a reference for future works that addresses the main challenges, and accelerate the development of deployable end user technologies to meet current global Sustainable Development Goals, in a manner that is resilient towards the increasingly dynamic growing conditions of rice plants expected by global climate change.

Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.

<!-- *** Custom HTML *** -->Hyperspectral images (HSIs) contain spectral information on the order of hundreds of different wavelengths, providing information beyond the visible range. Such spectral sensitivity is often used for the classification of objects of interest within a spatial scene in fields, such as studies of the atmosphere, vegetation and agriculture, and coastal environments. The classification task involves the processing of high-dimensional data which fuels the need for efficient algorithms that better use computational resources. Classification algorithms based on sparse representation classification perform classification with high accuracy by incorporating all the relevant information of a given scene in a sparse domain. However, such an approach requires solving a computationally expensive optimization problem with time complexity Ω ( n² ) . We propose a method that approximates the least squares solution of the sparse representation classification problem for HSIs using the Moore–Penrose pseudoinverse. The resulting time complexity of this approach reduces to O ( n² ) . The impact on the classification accuracy and execution time is compared to the state-of-the-art methods for three varied datasets. Our experimental results show that it is possible to obtain comparable classification performance current methods, with as much as 82% of a reduction in execution time, opening the door for the adoption of this technology in scenarios where classification of high-dimensional data is time critical.

Hyperspectral Images' data structure contains information from spectral ranges far beyond the limits of conventional, visible light imaging devices. This additional information comes at the cost of having unwieldy large image sizes, which presents an important computational resource consideration for many applications. Recent studies have shown that the usual large amount of redundancy in Hyperspectral Images can be discarded to avoid classification noise by means of sparse signal processing principles. To improve on classification performance, class-dependent Sparse-Representation classification (cd-SRC) algorithm includes Euclidean distance information between the sparse representation of a sample pixel to be classified and the training classes. The current work describes the use of Manhattan (or City Block) distance for improving the cd-SRC classification procedure. Our results show that classification performance using Euclidean Distance is comparable to the Manhattan Distance metric, which requires a smaller number of significantly less computationally expensive operations. In addition, we show that sparse representation classification has a significant advantage in classification performance compared to established Hyperspectral Image classification algorithms, such as the well-known Minimum Euclidean Distance and Support Vector Machine classifiers.