Bayesian Deep Learning Enabled Sentiment Analysis on Web Intelligence Applications

In recent times,web intelligence(WI)has become a hot research topic,which utilizes Artificial Intelligence(AI)and advanced information technologies on theWeb and Internet.The users post reviews on social media and are employed for sentiment analysis(SA),which acts as feedback to business people and government.Proper SA on the reviews helps to enhance the quality of the services and products,however,web intelligence techniques are needed to raise the company profit and user fulfillment.With this motivation,this article introduces a new modified pigeon inspired optimization based feature selection(MPIO-FS)with Bayesian deep learning(BDL),named MPIOBDL model for SA on WI applications.The presented MPIO-BDL model initially involved preprocessing and feature extraction take place using Term Frequency—Inverse Document Frequency(TF-IDF)technique to derive a useful set of information from the user reviews.Besides,the MPIO-FS model is applied for the selection of optimal feature subsets,which helps to enhance classification accuracy and reduce computation complexity.Moreover,the BDL model is employed to allocate the proper class labels of the applied user review data.A comprehensive experimental results analysis highlighted the improved classification efficiency of the presented model.

Web Intelligence with Enhanced Sunflower Optimization Algorithm for Sentiment Analysis

Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches.Several natural language processing(NLP)tools are commonly used to examine the large quantity of data generated online.Particularly,sentiment analysis(SA)is an effective way of classifying the data into different classes of user opinions or sentiments.The latest advances in machine learning(ML)and deep learning(DL)approaches offer an intelligent way of analyzing sentiments.In this view,this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for sentiment analysis(WIESFO-DLSA)technique.The major intention of the WIESFO-DLSA technique is to identify the expressions or sentiments that exist in the social networking data.The WIESFO-DLSA technique initially performs pre-processing and word2vec feature extraction processes to generate a meaningful set of features.At the same time,bidirectional long short term memory(BiLSTM)model is applied for classification of sentiments into different class labels.Moreover,an enhanced sunflower optimization(ESFO)algorithm is exploited to optimally adjust the hyperparameters of the BiLSTM model.A wide range of simulation analyses is performed to report the better outcomes of the WISFO-DLSA technique and the experimental outcomes ensured its promising performance under several measures.

Connotation and Determinants of Web Collective Intelligence

With continuous development of network technology, users in network community are promoted to interact deeply, and remarkable web collective intelligence emerges in the process. As a relatively new concept, the connotation of web collective intelligence is preliminarily explored in this paper, where the network community is taken as the environment, expert users as the subject, and web comments as the carrier. Meanwhile, taking Wikipedia as an example, by means of questionnaire survey and structural equation model, a more systematic index system is constructed from the perspective of user characteristics to explore determinants of web collective intelligence quality, and potential influence of user attributes on user behavior.

Facilitating Service Discovery with Semantic Overlay

Service Oriented Architecture （SOA） and Peer-to-Peer （P2P） computing share many common characteristics. It is believed that the combination of the two emerging techniques is a very promising method in promoting the web services （WS）. Because the service discovery plays a key role in the integration, here a P2P-based framework to manage the knowledge of service and locating services is proposed. In this paper, the details of the principle, constructing and maintaining of service semantic overlay architecture have been described, and the way how the semantic overlay facilitates discovery of service resources is illustrated. To enable the semantic web service superiority, Service Ontology, which is considered as the service semantic model, is employed to depict service. The service discovery includes two phases： searching on the service semantic overlay; and local discovery in peer＇s service repository. Various solutions have been proposed to realize those two phases. Furthermore, tests are carried out to evaluate service discovery on the architecture.