In this paper, we propose an ontology building method, called human-centric faceted approach for ontology construction (HCFOC). HCFOC uses the human-centric approach, improvised with the idea of selective dissemination of information (SDI), to deal with context. Further, this ontology construction process makes use of facet analysis and an analytico-synthetic classification approach. This novel fusion contributes to the originality of HCFOC and distinguishes it from other existing ontology construction methodologies. Based on HCFOC, an ontology of the tourism domain has been designed using the Protégé-5.5.0 ontology editor. The HCFOC methodology has provided the necessary flexibility, extensibility, robustness and has facilitated the capturing of background knowledge. It models the tourism ontology in such a way that it is able to deal with the context of a tourist’s information need with precision. This is evident from the result that more than 90% of the user’s queries were successfully met. The use of domain knowledge and techniques from both library and information science and computer science has helped in the realization of the desired purpose of this ontology construction process. It is envisaged that HCFOC will have implications for ontology developers. The demonstrated tourism ontology can support any tourism information retrieval system.

PurposeThis study demonstrates the synthesis of a knowledge organization framework from tourist reviews and an ontological model with its implementation in graph database, which is based on this framework. The aim is to influence place-making outcomes at tourist destinations.Design/methodology/approachThe faceted classification approach has been used for generating and validating the framework based on online reviews about urban tourism parks. The framework was used to develop an ontology using Protégé ontology editor that was implemented using GraphDB.FindingsThree fundamental facet categories, namely Component, Aspect and Outcome, each consisting of several sub-facets, were synthesized from the analyses of the reviews. Besides helping in constructing the ontology, the analysis also helped in calculating an importance-score for the reviews that helped in ranked information retrieval.Research limitations/implicationsThe analyses of the reviews were done manually and may carry human bias. But it is robust as it is based on a canonical faceted methodology.Practical implicationsIt is envisaged that this study will help tourist destination planners in decision-making by easing the utilization of tourist generated reviews by the knowledge management systems they use. Opinions of tourists will be induced in destination planning thereby helping in the production of quality “places.”Originality/valueThe presented faceted framework aims to specifically aid knowledge organization pertaining to online reviews related to tourist destinations. The focus is on organizing knowledge to facilitate tourism development for better place-making outcomes, which is an important area of research though it has little contributions.

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Purpose: This study aims at classification of sentiment reviews of Twitter data in the domain of climatology using machine learning techniques. It focuses on the text classification in order to determine the people’s intension about the climatic issues i.e., climate change, climate variability, environmental aspects etc. This paper portrays a comparison of results obtained by applying different classification algorithms like Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier, Neural Network classifier etc. These algorithms are used to classify a sentimental review and people’s emotions associated with climate. Design/Methodology/Approach: Total 2265 climate reviews data have been taken from Twitter’s developers’ account. After that, we pre-processed the total dataset by removing various symbols, HTTP tags, punctuation, etc. The pre-processed text were analysed and represented through Topic modelling, Multi Dimensional Scaling (MDS) and also Visualization of Heatmap. Next, bag of words are evaluated through various algorithms such as Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier and Neural Network classifier. After applying above mentioned classifier, datasets are tested and scores are noted. For the experiment, 70 % of total reviews (i.e.1586) are used for model training and 30% of total reviews (i.e. 680) are used for testing the models. Findings: By performing different algorithms, it shows that Random Forest classifier algorithm works well than other mentioned classifiers and most of the people have positive sentiment towards climate according to Valence Aware Dictionary for Sentiment Reasoning (VADER).

Purpose: This study aims to explore pertinent knowledge through the Sentiment Analysis technique and to link with relevant, pin-pointed documents. Design/Methodology/Approach: While information is essential ‘information overload’ is a big problem when we search for specific information. To get rid of psychological stress, mistakes in decision making or disregarding of relevant information, a methodology has been developed which may be suitable for researchers to extract pertinent knowledge from huge amount of research publications in a particular domain (‘climatology’ has been chosen for demonstration) within the shortest possible time. The study presents, how exactly relevant information can be retrieved there through sentiment analysis and through which a preliminary knowledge base can be gained. For this, ‘R’ software has been used to do the desired manipulation on the collected data. The steps involve pre-processing of introductory text, tokenization, polarity detection and analysis of text through sentiment analysis. Findings: It has been found that knowledge derived through sentiment analysis and abstract of the linked documents fairly match with each other, which validates the relevance and importance of the linked documents. Again, the impact factor of the prestigious journal having global coverage, where most of the linked documents were published also shows the importance of the linked documents/papers.