Sentiment Analysis is a way of considering and grouping of opinions or views expressed in a text. In this age when social media technologies are generating vast amounts of data in the form of tweets, Facebook comments, blog posts, and Instagram comments, sentiment analysis of these usergenerated data provides very useful feedback. Since it is undisputable facts that twitter sentiment analysis has become an effective way in determining public sentiment about a certain topic product or issue. Thus, a lot of research have been ongoing in recent years to build efficient models for sentiment classification accuracy and precision. In this work, we analyse twitter data using support vector machine algorithm to classify tweets into positive, negative and neutral sentiments. This research try to find the relationship between feature hash bit size and the accuracy and precision of the model that is generated. We measure the effect of varying the feature has bit size on the accuracy and precision of the model. The research showed that as the feature hash bit size increases at a certain point the accuracy and precision value started decreasing with increase in the feature hash bit size.

Application security measures are the controls within software systems that protect information assets from security attacks. Cyber attacks are largely carried out through software systems running on computing systems in cyberspace. To mitigate the risks of cyber attacks on software systems, identification of entities operating within cyberspace, threats to application security and vulnerabilities, and defense mechanisms are crucial. This chapter offers a taxonomy that identifies assets in cyberspace, classifies cyber threats into eight categories (buffer overflow, malicious software, input attacks, object reuse, mobile code, social engineering, back door, and logic bomb), provides security defenses, and maps security measures to control types and functionalities. Understanding application security threats and defenses will help IT security professionals in the choice of appropriate security countermeasures for setting up strong defense-in-depth mechanisms. Individuals can also apply these safeguards to protect themselves from cyber-attacks.

Google map is a platform that gives visual representation of geographical locations on the planet earth. Google maps has many features that for displaying maps and adding external contents to the map. In recent years many institutions and organizations have customized the features and functions of Google maps to build new applications that address their specific needs. Developing nations are faced with booming population growth, inadequate infrastructure and services. To provide many important services, especially financial services it is required that people are accurately located by use of a verifiable address. Developing an effective addressing system has been a challenge for many developing nations due to inadequate road and street network. This paper discusses the use of Google maps API to link properties in Ghana, hence assigning a digital address to each landmark in the country. The paper examines the technology that Google maps API provides and how it was harnessed to develop the GhanaPostGPS addressing system. This application helps users to acquire their digital address and enables others to search the location of an address via the system. The result from the Google maps API reverse geocoding shows that there is no district in the JSON response, an indicating factor showing extra work done by the developers of the application. This clearly shows the work is not a direct replica of services offered by Google maps although some services of Google maps were employed.