@proceedings {104,
	title = {Disaggregate Hotel Evaluation by Using Diverse Aspects from User Reviews},
	year = {2019},
	month = {Feb},
	abstract = {Experienced opinions about products and services can guide a potential user for a better purchase decision. Fine-grained aspect level opinions embedded within reviews must be explored to discover experienced users{\textquoteright} latent opinion about the aspects (i.e. features of products like cost, value for money, etc.) and their relative importance. In this paper, we present an unsupervised approach for discovering coherent hotel aspects based on the user attention. This model effectively integrates techniques like topic modeling and word embeddings along with the frequent noun-adjective co-occurrence statistics to automatically discover coherent hotel aspects. Further supervised methods are used to understand the user{\textquoteright}s relative emphasis on the aspects and finally rank the hotels. This method does not assume any predefined seed words and discovers coherent level aspects by directly using user attention and word co-occurrence statistics in addition to topic modeling and word embeddings. The performance evaluation of this method was done by collecting various hotel reviews from multiple travel websites. Results show that the proposed methods improved the baseline performance up to 90\%. Hence, the results thus obtained are very promising and indicate that the system is simple, scalable and most of all accurate in ranking hotels based on the latent aspects expressed in the user reviews.},
	keywords = {Adaptation models, aspects, Coherence, coherent hotel aspects, customer satisfaction, data mining, Data models, disaggregate hotel evaluation, diverse aspects, Estimation, Feature extraction, fine-grained aspect level opinions, frequent noun-adjective co-occurrence statistics, hotel industry, hotel ranking, hotel reviews, latent aspects, Predictive models, purchase decision, statistical analysis, supervised methods, topic modeling, unsupervised learning, user attention, user reviews, word co-occurrence statistics, word embeddings},
	doi = {10.1109/BIGCOMP.2019.8679332},
	author = {B. Devkota and K. Kim and C. Zhuang and H. Miyazaki}
}
@proceedings {103,
	title = {An Exploratory Study on the Generation and Distribution of Geotagged Tweets in Nepal},
	year = {2018},
	month = {Oct},
	abstract = {Social media platforms contribute a huge part of the content available on the world wide web today. These platforms act as a rich source of real time data from live human sensors. These media disseminate spatiotemporal public opinion regarding a range of events, activities and human information behaviors. This paper explores the active user locations and spatial penetration of popular microblogging platform, Twitter, in Nepal. A heatmap visualization is used to show the intensity and distribution of the spatial patterns of Twitter activities in different parts of Nepal. Clustering is a popular technique for knowledge discovery, so spatial clustering is applied to groups tweets spatially into different classes. Such spatial clustering helps in the identification of areas of similar twitter activities and shows the distribution of the spatial patterns in different parts of Nepal. Tweet clusters are observed mainly in the main cities and the tourism centers. Further, an examination of the twitter data shared by the local Nepalese people and the foreigners are shown. This study contributes the research line by providing insights to better understand the spatiotemporal patterns and hotspots of tweets in Nepal. Such patterns and hotspots have an immense practical value that can be attributable to a place in order to derive meaningful insights related to various domains like a disease, crime, tourism, etc.},
	keywords = {active user locations, clustering, Conferences, data mining, geotagged tweets, hotspots, human information behaviors, Kernel, live human sensors, Media, microblogging platform, Nepal, pattern clustering, Security, social media, social media platforms, social networking (online), spatial clustering, spatial distribution, spatial patterns, spatial penetration, spatiotemporal patterns, spatiotemporal public opinion, time data, travel industry, tweet clusters, Twitter, twitter activities, twitter data, Urban areas, world wide web today},
	doi = {10.1109/CCCS.2018.8586827},
	author = {B. Devkota and H. Miyazaki}
}
