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This research work focused on transmission channel characterization of Free-Space-Optical (FSO) communications technology for deploying in the developing country like Bangladesh. To meet the tremendous amount of data traffic, mobile operators and ISPs need better solution than the existing RF and fiber optic communications. Moreover, Bangladesh is entering in the era of satellite communications by launching its own satellite.  So, Bangladesh needs such communication technology that provides higher channel bandwidth, sophisticated transmission security and can cope channel dispersion. FSO is a good candidate that can meet all these features. The transmission channel characterization plays a significant role in optimizing the performance of FSO link.  In this work, the channel characterization of FSO technology from the weather perspective of Bangladesh has been investigated thoroughly. The obtained results show that the atmospheric scattering effect does not hamper the short range FSO link performance, whereas, the atmospheric turbulence effect is not favorable to deploy FSO technology with reasonable quality signal unless it is optimized properly using antenna aperture averaging technique.

Sentiment Analysis studies people's attitudes, opinions, evaluations, emotions, sentiments toward some entities such as products, topics, individuals, services, issues and classify them whether the opinion or evaluations inclines to that entities or not. It is getting more research focus in recent years due to its benefits for scientific and commercial purposes. This research aims at developing a better approach for sentiment analysis at the sentence level by using a combination of lexicon resources and a machine learning method. Moreover, as reviews data on the internet is unstructured and has much noise, this research uses different preprocessing techniques to clean the data before processing in different algorithms discussed in subsequent sections. Additionally, the lexicon building processes, how the lexicon is handled and combined with the machine learning algorithm for predicting sentiment is also discussed. In sentiment analysis, sentence's sentiment can be classified into three classes: positive sentiment, negative sentiment, or neutral. However, in this research work, we have excluded neutral sentiment for avoiding ambiguity and unnecessary complexity. The experiment results show that the proposed algorithm outperforms compared to the baseline machine learning algorithms. We have used four distinct datasets and different performance measures to check and validate the proposed method's robustness.

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