Impression Flow Based on Comment in Islamic Studies from Instagram using Sentiment Analysis

Islamic studies in industrial revolution 4.0 grown very rapidly. Everyone can access an information everywhere and everytime using their end devises such a smartphone and laptop base on internet access using a social media such instagram, facebook, and twitter. development of da'wah media that is not only through radio television and through studies conducted either in mosques or other places for delivering of studies and insights about Islam for all muslims in Indonesian country especially in Kalimantan Selatan. One of aplication social media for studying islam is Instagram. All of preachers is very easily make a post on Instagram for sharing a religious knowledge to users of Instagram social media, hence this case a lot of Islamic da'wah accounts in great demand by the muslims people in Indonesia. Islamic posts made by preachers on Instagram make a lot of conflict and the direction of negative or positive impressions left by users through the comments column provided from instagram. To determine the positive and negative directions of the post from a preacher. We develop a system for detecting the direction of impression from all of comment on Islamic content on Instagram created by the user using a sentimen analysis. This system analyzes comments left on a post from a preacher's Instagram story. The system that was built succeeded for classifying the filtered comments by attributing the direction of the da'wah impression posted by the preacher. The classification of the impression direction of this system contains 3 impression directions, namely positive, negative, and neutra

UTILIZATION OF SENTIMENT ANALYSIS USING THE DATA SCIENCE APPROACH TO IMPROVE CUSTOMER SATISFACTION

One of the biggest problem for customer satisfaction is how to understand the user need and the user point of view, to make it visible social media is giving huge impact especially tweeter comment . However, the number of comments submitted is very large and become difficulty to analyse. Besides the comment data on Twitter is an unstructured type of data so that if processing uses a relational database engine the results obtained are not optimal. To deal with these problems, a big data approach is needed in data extraction combined with the comment data processing model. This study uses a combination of big data in data processing and lexicon based to analyse customer comments. Data processing using big data especially with the NoSQL approach is very effective and efficient in conducting searches on unstructured data because the search for big data is based on meta text rather than cardinality between data. While the lexicon based method used depends on the completeness of the dictionary used. The purpose of this study is to analyse comments and share whether they have positive, negative, or neutral sentiments so that they can be used as parameters in decision making in an organization

Partially Observable Markov Decision Processes with Behavioral Norms

This extended abstract discusses various approaches to the constraining of Partially Observable Markov Decision Processes (POMDPs) using social norms and logical assertions in a dynamic logic framework. Whereas the exploitation of synergies among formal logic on the one hand and stochastic approaches and machine learning on the other is gaining significantly increasing interest since several years, most of the respective approaches fall into the category of relational learning in the widest sense, including inductive (stochastic) logic programming. In contrast, the use of formal knowledge (including knowledge about social norms) for the provision of hard constraints and prior knowledge for some stochastic learning or modeling task is much less frequently approached. Although we do not propose directly implementable technical solutions, it is hoped that this work is a useful contribution to a discussion about the usefulness and feasibility of approaches from norm research and formal logic in the context of stochastic behavioral models, and vice versa

An approach to description logic with support for propositional attitudes and belief fusion

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-89765-1_8Revised Selected and Invited Papers of ISWC International Workshops, URSW 2005-2007.In the (Semantic) Web, the existence or producibility of certain, consensually agreed or authoritative knowledge cannot be assumed, and criteria to judge the trustability and reputation of knowledge sources may not be given. These issues give rise to formalizations of web information which factor in heterogeneous and possibly inconsistent assertions and intentions, and make such heterogeneity explicit and manageable for reasoning mechanisms. Such approaches can provide valuable metaknowledge in contemporary application fields, like open or distributed ontologies, social software, ranking and recommender systems, and domains with a high amount of controversies, such as politics and culture. As an approach to this, we introduce a lean formalism for the Semantic Web which allows for the explicit representation of controversial individual and group opinions and goals by means of so-called social contexts, and optionally for the probabilistic belief merging of uncertain or conflicting statements. Doing so, our approach generalizes concepts such as provenance annotation and voting in the context of ontologies and other kinds of Semantic Web knowledgeThis work was partially funded by the German National Research Foundation DFG (Br609/13-1, research project “Open Ontologies and Open Knowledge Bases”) and by the Spanish National Plan of R+D, project no. TSI2005-08225-C07-0