Soft behaviour modelling of user communities

A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model

Ranking Product Aspects Based on Consumer Reviews

The Internet has become an excellent source for gathering consumer?s opinions or reviews. For product numerous consumer reviews of product are available on internet .Consumer reviews or opinions are useful for both firms & users as they contain rich & valuable knowledge about product. The business firm needs different reviews of customers for development of product. The user can make wise purchasing decision by looking at customer reviews. There are reviews on various aspects of the products. The reviews are numerous, diverse and not precise leading to difficulties in information gathering and knowledge acquisition. A product may have hundreds of aspects. Some of the aspects are important than the others. Therefore we are developing the system to mine those aspects and rank them which will help for better product development. This proposed method is named as ?A product aspect ranking framework?. Among reviews of consumer for particular product, it first identifies aspects in the reviews by a shallow dependency parser and then analyzes consumer opinions on these aspects via a sentiment classifier. Then a probabilistic aspect ranking algorithm is used, which effectively exploits the aspect frequency as well as the influence of consumer?s opinions given to each aspect over their overall opinions on the product in a unified probabilistic model

Classification of drugs reviews using W-LRSVM model

Opinion mining provided less opportunity to discuss their experiences about drugs so reviewing about it was difficult. Recent findings show that online reviews and blogs on drugs are important for patients, marketers and industries. Collecting the information for drugs from the website and analyzing is a challenge. A model is designed by proposing an algorithm which crawls information from the web to analyze reviews of drugs. Reviews were crawled for five different drugs using the algorithm. The W-Bayesian Logistic Regression and Support Vector Machine (W-LRSVM) model was trained for different split ratios to obtain the accuracy of 97.46%. Experimental results on reviews of five different drugs showed that the proposed model gave better results compared to other classifier