A thorough analysis of people’s sentiment about a business, an event or an individual is necessary for business development, event analysis and popularity assessment. Social networks are rich sources of obtaining user opinions about people, events and products. Sentiment analysis conducted using multiple user comments and messages on microblogs is an interesting field of data mining and natural language processing (NLP). Different techniques and algorithms have recently been developed for conducting sentiment analysis on Twitter. Different proposed classification and pure NLP-based methods have different behaviours in predicting sentiment orientation. In this study, we combined the results of the classic classifiers and NLP-based methods to propose a new approach for Twitter sentiment analysis. The proposed method uses a fuzzy measure for determining the importance of each classifier to make the final decision. Fuzzy measures are used with the Choquet fuzzy integral for fusing the classifier outputs in order to generate the final label. Our experiments with different Twitter sentiment datasets show that fuzzy integral-based classifier fusion improves the average accuracy of sentiment classification.
In functional electrical stimulation controllers developed based on a tracking approach, the desired movement (usually the joint angles trajectories) is used as fhe input of the controller. In answer to the question, how the desired movement should he individually tailored, a new method based on a Mu1:i-Layer Perceptron (W) has been developed to generate the subject-dependent trajectories of the joint a n g h during sit-to-stand transfer. The size of the MLP h m been reduced significantly by choosing a suitable set of inputs. Ouiputs of the implemented MLP are the coeficients of the Fourier half amplitude cosine expansions of the joint angles. Since these coefticients describe the inherent dynamics of the system, we c o d d avoid implementing the usual embedded feedback in the body of the M P .In comparison with a model-based algorithm (with a maximum prediction error of 10.5%), this method predicted fhe movement more accurately (with a maximum error of 5.2%).
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