Nargis Pervin

With the increased popularity of the smart mobile devices, mobile applications (a.k.a apps) have become essential. While the app developers face an extensive challenge to improve user satisfaction by exploiting the valuable feedbacks, the app users are overloaded with way too many apps. Extracting the valuable features from apps and mining the associated sentiments is of utmost importance for the app developers. Similarly, from the user perspective, the key preferences should be identified. This work deals with profiling users and apps using a novel LDA based aspect identification technique. Polarity aggregation technique is used to tag the weak features of the apps the developers should concentrate on. The proposed technique has been experimented on an Android review dataset to validate the efficacy compared to state-of-the-art algorithms. Experimental findings suggest superiority and applicability of our model in practical scenarios.

The accelerated growth of the Web2.0 has led to an abundance of accessible information which has been successfully harnessed by many researchers for personalizing products and services. Many personalization algorithms are focused on analyzing only the explicitly provided information and this limits the scope for a deeper understanding of the individuals’ preferences. However, analyzing the reviews posted by the users seeks to provide a better understanding of users’ personal preferences and also aids in uncovering business’ strengths and weaknesses as perceived by the users. Topic Modeling, a popular machine learning technique addresses this issue by extracting the underlying abstract topics in the textual data. In this study, we present entity-LDA (eLDA), a variation of Latent Dirichlet Allocation for topic modeling along with a dependency tree based aspect level sentiment analysis methodology for constructing user and business profiles. We conduct several experiments for evaluating the quantitative and qualitative performance of our proposed model compared to state-of-the-art methods. Experimental results demonstrate the efficacy of our proposed method both in terms topic quality and interpretability. Finally we develop a framework for constructing user and business profiles from the topic probabilities. Further we enhance the business profiles by extracting syntactic aspect level sentiments to indicate sentimental polarity for each aspects.

Wicked problems are embedded with attributes such as lack of understanding, multiple stakeholders’ involvement in solution implementation and the lack of opportunity of undoing the solution implemented. Hence, a robust methodology is required to understand its nature. Interactive Qualitative Analysis (IQA) is a systems method that caters to the need of understanding the phenomenon while also provisioning means to understand different stakehold-ers’ perceptions of the phenomenon. This study demonstrates the use of IQA in understanding a wicked problem in healthcare sector dealing with patients’ internet usage. Our analysis reveals that patients elicit the need of the internet in three new realms, namely, hospital choice, physician choice, and online support services, which were not apparent in previous studies on the same context. The study’s findings provide insights that could lead to development of strategies for meta-services within healthcare sector involving information dissemination for patients through the internet.