Recent data provided by UNHCR indicated that 85% of the world's displaced people are hosted in developing countries, while Asia and the Pacific are homes to about 3.5 million refugees. These hosting countries are often not well equipped with the resources needed to accommodate for the huge surplus in the number of refugees. The ability to predict the population growth of refugees thus enables refugee-hosting countries and NGOs to prepare for refugee migration beforehand, resulting in better infrastructure and opportunities for the refugees expected to enter a country. Advanced analytics could assist experts to chart where refugees are likely to head next, study the signs of future influx, prepare for reroute plans and raise crisis funds. In this paper, we present a regression model that predicts the anticipated number of refugee population in 20 Asian refugee-hosting countries. Using time-series analysis, we establish the pattern of refugee growth for Asian countries with a history of an average population of 2,000 refugees within the last 25 years as well as the last decade. Our model considers several input factors affecting the refugee population growth and predicts the number of refugees between 2017 to 2022 with promising results.

Sindhi is one of the most ancient languages in the world and it has its own written and spoken scripts. After the rigorous study it was found that a lot of research work has been done in different languages, but word by word labelling of Sindhi language had not been done yet. In this research study, word labelling was done on 100 sentences of Romanized Sindhi texts using Python online tool. The dataset was collected from different sources which include Sindhi newspaper, blogs and social media webpages. From this dataset, a rule-based model has been applied for the Parts-of-Speech (POS) tagging of the Romanized Sindhi sentences. A total of 624 words of Romanized Sindhi texts were tested and successfully tagged by the SindhiNLP tool in which 482 words were tagged as nouns and pronouns, 92 words tagged as verbs and 50 words tagged as determinants.

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Sentiment analysis is an important part of natural language processing (NLP). This study evaluated the sentiment of Romanized Sindhi Text (RST) using a hybrid approach and ground truth values. The methodology of sentiment analysis involves three major steps: input data, process on tool, analysis of data and evaluation of results. One hundred RST sentences were used in this study's sentiment analysis, which can be positive, neutral, or negative. The statements in the corpus of this study are simple to understand and are used in everyday life. This research used an online Python tool to process a text and get results in the form of outcomes. The results showed that 86% of the sentences have neutral sentiments, 9% of the total results of sentiment analysis have negative sentiments, and only 5% of sentences of Romanized Sindhi Text have positive sentiments. The accuracy of the RST was measured on an online calculator and the value was 87.02% on the basis of ground truth values. An error ratio of 12.98% was calculated on the basis accuracy found on the online calculator of confusion matrix.