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Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%; with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words. [ABSTRACT FROM AUTHOR]

Traditional agricultural practices, extensive use of inputs, and abrupt changes in climate have been of great concern to agriculture production around the world, especially in developing countries. Therefore, it is very vital to adopt and expand Climate-Smart agricultural (CSA) practices. By the cross-sectional data of 350 cotton farmers from major cotton-growing districts of Punjab Pakistan, adoption of CSA practices such as irrigation and soil and crop management practices is evaluated, and factors which affect farmer adoption decision and its impact on poverty, income, and yield are estimated by using logistic regression and propensity score matching (PSM) respectively. The results found that education, access to credit, tubewell ownership, farming experience, and access to extension services positively influenced farmers' adoption behavior. Further, PSM results revealed that adoption of CSA practices is economical, financially, environmentally desirable, and pro-poor. According to these findings, ultimately adoption would help in reducing the negative impact of climate change on the cotton crop by ensuring profits, removing the barriers in the adoption, disseminating the information about CSA, and strictly enforcing the regulations for CSA. [ABSTRACT FROM AUTHOR]