Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.

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.

Now-a-days the role of Information and Communication Technology (ICT) to develop and strengthen the sector of agriculture has become seemingly promising. ICT can help a farmer to get relevant information regarding different agricultural information like crop, diseases, marketing, etc. Also, studies have depicted that mobile phone penetration in Nepal have increased in the recent years. This have opened a new possibility for implementing SMS (Short Message Service) based information dissemination services in order to reduce information asymmetry prominent in the country due to extreme geography. This work proposes an approach based on simple SMS service for disseminating agricultural knowledge to the needy farmers in a cost effective and easily deployable manner. Here, the farmers can get consultancy expert advices by sending simple queries (in romanized Nepali language) as a SMS to a central knowledge base. But, the end users, may not provide correct spelling in their queires. Therefore, we integrated a domain specific (i.e. romanized Nepali) spell checker for improving robustness of the system. The purposed approach is seemingly promising than other advanced mobile applications because the general farmer community use simple handset with basic features only.

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' 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'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.