Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in large collection of documents. After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. It includes classification hierarchy, Topic modelling methods, Posterior Inference techniques, different evolution models of latent Dirichlet allocation (LDA) and its applications in different areas of technology including Scientific Literature, Bioinformatics, Software Engineering and analysing social network is presented. Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. At the end paper is concluded with detailed discussion on challenges of topic modelling, which will definitely give researchers an insight for good research.

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Topic Modeling: A Comprehensive Review

The study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including sociology, psychology, economics, linguistics, and computer science. A number of excellent computational linguistics and linguistic surveys of the field exist. Most surveys, however, do not bring the two disciplines together to show how methods from linguistics can benefit computational sentiment analysis systems. In this survey, we show how incorporating linguistic insights, discourse information, and other contextual phenomena, in combination with the statistical exploitation of data, can result in an improvement over approaches that take advantage of only one of these perspectives. We first provide a comprehensive introduction to evaluative language from both a linguistic and computational perspective. We then argue that the standard computational definition of the concept of evaluative language neglects the dynamic nature of evaluation, in which the interpretation of a given evaluation depends on linguistic and extra-linguistic contextual factors. We thus propose a dynamic definition that incorporates update functions. The update functions allow for different contextual aspects to be incorporated into the calculation of sentiment for evaluative words or expressions, and can be applied at all levels of discourse. We explore each level and highlight which linguistic aspects contribute to accurate extraction of sentiment. We end the review by outlining what we believe the future directions of sentiment analysis are, and the role that discourse and contextual information need to play.

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Evaluative language beyond bags of words: Linguistic insights and computational applications

Competency Questions(CQs) play an important role in the ontology development lifecycle, as they represent the ontology requirements. Although the main methodologies describe and use CQs, the current practice of ontology engineering makes a superficial use of CQs. One of the main problems that hamper their proper use lies on the lack of tools that assist users to check if CQs are being fulfilled by the ontology being defined, particularly when these ontologies are defined in OWL (Ontology Web Language), under the Description Logic formalism. We propose a mechanism to support evaluating whether the ontology follows their correspondent CQs.

Evaluating Ontologies with Competency Questions.

Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users’ opinions and attempt to understand (i) changing characteristics of users’ emotions toward a social issue over time, (ii) influence of public emotions on individuals’ emotions, (iii) cause of changing opinion by social factors, and so on. We study users’ emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.

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Emotion Dynamics of Public Opinions on Twitter

Ontology is an important artifact of Semantic Web applications. Today, there are an enormous number of ontologies available on the Web. Even so, finding and identifying the right ontology is not easy. This is because the majority of ontologies are either not described or described with a general-purpose metadata vocabulary like Dublin Core. On the other hand, ontology construction, irrespective of its types (e.g., general ontology, domain ontology, application ontology), is an expensive affair both in terms of human resources and other infrastructural resources. Hence, the ideal situation would be to reuse the existing ontologies to reduce the development effort and cost, and also to improve the quality of the original ontology. In the current work we present an ontology metadata vocabulary called Metadata for Ontology Description and publication (MOD). To design the vocabulary, we also propose a set of generic guiding principles and a well-established methodology which take into account real concerns of the ontology users and practitioners.

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MOD: metadata for ontology description and publication

In this paper, we analyze the sentiments derived from the conversations that occur in social networks. Our goal is to identify the sentiments of the users in the social network through their conversations. We conduct a study to determine whether users of social networks (twitter in particular) tend to gather together according to the likeness of their sentiments. In our proposed framework, (1) we use ANEW, a lexical dictionary to identify affective emotional feelings associated to a message according to the Russell’s model of affection; (2) we design a topic modeling mechanism called Sent_LDA, based on the Latent Dirichlet Allocation (LDA) generative model, which allows us to find the topic distribution in a general conversation and we associate topics with emotions; (3) we detect communities in the network according to the density and frequency of the messages among the users; and (4) we compare the sentiments of the communities by using the Russell’s model of affect versus polarity and we measure the extent to which topic distribution strengthen likeness in the sentiments of the users of a community. This works contributes with a topic modeling methodology to analyze the sentiments in conversations that take place in social networks.

Sentiment analysis in social networks through topic modeling

Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. One of the unexplored components of human communication found online in written form is an emotional expression. However, despite the concreteness of the online expressions in written form, it remains unpredictable which kinds of emotions will be expressed in individual messages of Twitter users. To investigate this, we perform an investigation on observing emotions unfolding in a consecutive sequence of tweets for a particular user based on his/her past history. In this paper, we propose a method on given a set of tweets related with some events (identified by the usage of a hashtag), determines how those sentiments will be distributed on behalf of a person within a conversation. We present the Hidden Markov Model (HMM) to understand the nature of emotion dynamics in Twitter messages.

Modelling Emotion Dynamics on Twitter via Hidden Markov Model

The purpose of this research is to develop an ontology with subsequent testing and evaluation, for identifying utility and value. The domain that has been chosen is human nervous system (HNS) disorders. It is hypothesized here that an ontology-based patient records management system is more effective in meeting and addressing complex information needs of health-care personnel. Therefore, this study has been based on the premise that developing an ontology and using it as a component of the search interface in hospital records management systems will lead to more efficient and effective management of health-care. It is proposed here to develop an ontology of the domain of HNS disorders using a standard vocabulary such as MeSH or SNOMED CT. The principal classes of an ontology include facet analysis for arranging concepts based on their common characteristics to build mutually exclusive classes. We combine faceted theory with description logic, which helps us to better query and retrieve data by implementing an ontological model. Protégé 5.2.0 was used as ontology editor. The use of ontologies for domain modelling will be of acute help to doctors for searching patient records. In this paper we show how the faceted approach helps us to build a flexible model and retrieve better information. We use the medical domain as a case study to show examples and implementation.

HNS Ontology Using Faceted Approach

Similar to the idea of a digital library, in the recent time, a new concept has emerged called an ontology library. Principally both a digital library and an ontology library have the similar kinds of purposes and objectives to achieve (e.g., store, organize and provide access to the digital objects), except the kind of materials they deal with. While a digital library deals with the documents of various types, such as text, audio, video, images, etc., an ontology library deals with the ontologies (where an ontology is an intelligent object, often referred as a digital artifact, consisted of representations of the entities in terms of their types, properties and the relationships of a domain of discourse). 

An ontology library is a kind of organizational network that is easily accessible from any place or location and offers relevant or admissible ontologies in a well-organized manner and with a competent approach, which is based on different well-established ontology representation languages, such as RDFS, OWL, etc. The goal of an ontology library is of two-fold: (i) to provide a platform to the ontologists (a practitioner who builds the ontologies) to publish, store, preserve, share and evaluate the ontologies; (ii) to facilitate the ontofiers (an ontology user) to retrieve the right ontology at the right time to make the best (re)use of it.

Besides the term ontology library, we often come across with some other related terminologies, such as ontology repository, ontology registry, and ontology directory. Conceptually they are similar kind of things but still they have some differences. An ontology repository is a collection of the ontologies and their metadata. An ontology registry consists of a list of ontologies and metadata instead of the actual ontologies. It is an architecture that allows ontology providers and users to use, share and exploit semantic information in a real world scenario. An ontology registry offers several tools and technologies which enable the users to find out the relevant ontologies as per their need. An ontology directory is a service which provides metadata and sources of information of the ontologies. Besides providing the reference services, it supports the registration services and service requests.

The objective of this paper is to disseminate the result of our survey research designed based on some of the existing ontology libraries and to provide a set of guidelines that the future ontology libraries should possess to support the increasing complexity of ontology search and retrieval. The entire work is carried out as follows: we discuss the various concepts related to an ontology library. We elaborate some of the popular ontology libraries and make a comparative analysis of them from both the theoretical and implementation perspectives. We study their various features including the underlying designing principles, ontology exploitation facility, metadata, tools, etc. Based on this analysis, we finally propose the essential features and principles that an ontology library must follow. We envision that from the current work the various agencies namely, the ontology researchers, practitioners and ontology library developers will be highly benefitted.

Debashis Naskar

Papers

Emotion Dynamics of Public Opinions on Twitter

Sentiment analysis in social networks through topic modeling

Modelling Emotion Dynamics on Twitter via Hidden Markov Model

HNS Ontology Using Faceted Approach

Ontology And Ontology Libraries