Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

Feature selection and sentiment analysis are two common studies that are currently being conducted; consistent with the advancements in computing and growing the use of social media. High dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the features. Furthermore, most reviews from social media carry a lot of noise and irrelevant information. Therefore, this study proposes a new text-feature selection method that uses a combination of rough set theory (RST) and teaching-learning based optimization (TLBO), which is known as RSTLBO. The framework to develop the proposed RSTLBO includes numerous stages: (1) acquiring the standard datasets (user reviews of six major U.S. airlines) which are used to validate search result feature selection methods, (2) pre-processing of the dataset using text processing methods. This involves applying text processing methods from natural language processing techniques, combined with linguistic processing techniques to produce high classification results, (3) employing the RSTLBO method, and (4) using the selected features from the previous process for sentiment classification using the Support Vector Machine (SVM) technique. Results show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text processing. More importantly, the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.     

Improved crossover firefly algorithm based deep Beleif network for low-resolution face recognition

Multimedia Tools and Applications - Face detection by low-resolution image (LR) is one of the key aspects of Human-Computer Interaction(HCI). Due to the LR image, which has changes in pose,...     

Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA

Engineering with Computers - Air overpressure (AOp) is one of the most important undesirable effects induced by blasting operations in the mining or tunneling projects. Hence, the present precise...     

EETP-MAC: energy efficient traffic prioritization for medium access control in wireless body area networks

Wireless body area network (WBAN) has witnessed significant attentions in the healthcare domain using biomedical sensor-based monitoring of heterogeneous nature of vital signs of a patient’s body. The design of frequency band, MAC superframe structure, and slots allocation to the heterogeneous nature of the patient’s packets have become the challenging problems in WBAN due to the diverse QoS requirements. In this context, this paper proposes an Energy Efficient Traffic Prioritization for Medium Access Control (EETP-MAC) protocol, which provides sufficient slots with higher bandwidth and guard bands to avoid channels interference causing longer delay. Specifically, the design of EETP-MAC is broadly divided in to four folds. Firstly, patient data traffic prioritization is presented with broad categorization including Non-Constrained Data (NCD), Delay-Constrained Data (DCD), Reliability-Constrained Data (RCD) and Critical Data (CD). Secondly, a modified superframe structure design is proposed for effectively handling the traffic prioritization. Thirdly, threshold based slot allocation technique is developed to reduce contention by effectively quantifying criticality on patient data. Forth, an energy efficient frame design is presented focusing on beacon interval, superframe duration, and packet size and inactive period. Simulations are performed to comparatively evaluate the performance of the proposed EETP-MAC with the state-of-the-art MAC protocols. The comparative evaluation attests the benefit of EETP-MAC in terms of efficient slot allocation resulting in lower delay and energy consumption.