Due to the continuous and rapid growth of social media, opinionated contents are actively created by users in different languages about various products, services, events, and political parties. The automated classification of these contents prompted the need for multilingual sentiment analysis researches. However, the majority of research efforts are devoted to English and Arabic, English and German, English and French languages, while a great share of information is available in other languages such as Hausa. This paper proposes multilingual sentiment analysis of English and Hausa tweets using an Enhanced Feature Acquisition Method (EFAM). The method uses machine learning approach to integrate two newly defined Hausa features (Hausa Lexical Feature and Hausa Sentiment Intensifiers) and English feature to measure classification performance and to synthesize a more accurate sentiment classification procedure. The approach has been evaluated using several experiments with different classifiers in both monolingual and multilingual datasets. The experimental results reveal the effectiveness of the approach in enhancing feature integration for multilingual sentiment analysis. Similarly, by using features drawn from multiple languages, we can construct machine learning classifiers with an average precision of over 65%.

The entity of intelligent building is integrated with diversified service function of control, automation and communication of devices in its environment, and to perform them in joined manner via intelligent tasks. Rapid improvement in sensor technologies and advancement in electronics have given rise to heterogeneous systems growth in intelligent building. Most of these subsystems are dissimilar and not intended to perform interoperation task. Consequently, it is rather difficult to perform decision making with the combination of these systems considering the variety of data that are not efficient in adapting to the changing environment. One of the recent decision support solutions provided was Left–right Hidden Markov Model (LR-HMM) which uses left-right algorithm to improve accuracy of prediction based on single timely decision. However, it leads to low accuracy when multiple timely decisions are performed. Therefore, to ensure timely decision, the accuracy of prediction should be improved when performing multiple decisions. We propose a new decision model to improve performance in such situations. The goal is to improve the accuracy of prediction when multiple decisions are performed. Experiments are conducted to evaluate the performance of the proposed Re-estimated Ergodic Hidden Markov Model (RE-HMM), and show that it improves the average accuracy compared with LR-HMM. It is examined when tested on the Local Area Network (LAN) settings.

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