A Deep Learning based Model using Review Associated Feature Extraction Approach for Sentiment Analysis

With the advancement of internet technologies, in the present days, online forums, social media platforms and e-commerce sites have made the product reviews process very easy. There are a lot of mobile applications, websites and forums where consumers used to share and circulate their opinions, experiences, ideas and views regarding products, brands and services. In consequence, online user reviews have become a deciding factor for many consumers prior to purchasing their selected items. The sentiment analysis is a technique to extract sentiments, feelings and insights from customer reviews and public texts. Therefore, plenty of businesses perform sentiment analysis in order to more thoroughly comprehend of their customer opinions and suggestions regarding their products and services. Furthermore, a number of scientific researchers also have a keen interest in classifying customer reviews into a set of labels employing text classification techniques. The objective of the this research work is to develop an approach to extract review associated features using Part-of-Speech (POS) tagging and design a CNN model to classify the reviews' sentiment as positive or negative. In this paper, an approach to extract review associated feature has been presented. Natural Language Processing (NLP) techniques are utilized for data preprocessing to remove uninformative data from reviews. Deep learning model CNN is used for sentiment classification and Amazon mobile reviews dataset is used for the experiment. The proposed model is experimentally evaluated and provides enhanced performance than other models also provides improved accuracy of 97.23% on Amazon mobile review dataset

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment