48 research outputs found
Analisis Sentimen Masykarakat Indonesia Terhadap Gatra Ekonomi Ketahanan Nasional Menggunakan Fuzzy Ontology-Based Semantic Knowledge
Kampanye diantara dua kubu acap kali meramaikan media sosial yang telah menjadi target kampanye dimana total pengguna media sosial di Indonesia telah mencapai 130 juta pengguna. Memanfaatkan momentum ramainya media sosial pada tahun pemilu dan kampanye, penulis mencoba menggali sentimen masyarakat melalui Twitter terhadap gatra ekonomi dalam konsepsi ketahanan nasional menggunakan Fuzzy ontology-based semantic knowledge. Ontologi pada biasanya dianggap tidak terlalu efektif dalam mengekstrak informasi dari tweets, sehingga digunakanlah konsep Fuzzy-ontology based semantic knowledge.
Fuzzy ontology-based semantic knowledge merupakan salah satu cara analisis sentimen menggunakan pendekatan gabungan lexicon-based, ontologi, dan fuzzy logic untuk menghasilkan apakah suatu tweet dapat dikategorikan sebagai strong negative, negative, netral, positive, maupun strong positive.
Pada akhirnya, ontologi biasa tidak dapat mengklasifikasikan masuk kedalam sentimen apa sebuah tweet jika tweet tersebut memiliki lebih dari satu nilai SentiWord. Dari 2032 tweet bersentimen, terdapat 205 tweet yang memiliki lebih dari satu
nilai SentiWord sehingga diperlukan penerapan FuzzyDL untuk memecahkan permasalahan tersebut. Dengan menggunakan metode ini, didapatkan akurasi 78%, dengan tingkat presisi 93%, recall 73%, dan function measure 82%.
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The campaign between the two camps often enlivened social media which has become the target of the campaign where the total number of social media users in Indonesia has reached 130 million users. Utilizing the momentum of the hectic social media in the election year and campaign, the author tries to explore the public sentiment through Twitter on gatra economy in the concept of national resilience using Fuzzy ontology-based semantic knowledge. Ontology is usually considered to be not very effective in extracting information from tweets, so the concept of Fuzzy-ontology based semantic knowledge is used. Fuzzy ontology-based semantic knowledge is one method of sentiment analysis using a combined approach of lexicon- based, ontology, and fuzzy logic to produce whether a tweet can be categorized as strong negative, negative, neutral, positive, or strong positive.
In the end, ordinary ontologies cannot classify what sentiment is a tweet if the tweet has more than one SentiWord value. Of the 2032 sentiment tweets, there are 205 tweets that have more than one SentiWord value, so FuzzyDL is needed to solve these problems. By using this method, an accuracy of 78% is obtained, with a precision level of 93%, a recall of 73%, and a function measure of 82%
Commonsense Knowledge in Sentiment Analysis of Ordinance Reactions for Smart Governance
Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping.
We have then enhanced the mapping technique to link multiple tweets to SCC. In order to promote transparency in government through increased public participation, we have conducted sentiment analysis of tweets in order to evaluate the opinion of the public with respect to ordinances passed in a particular region.
Our final objective is to develop a mapping algorithm in order to directly relate ordinances to tweets. In order to fulfill this objective, we have developed a mapping technique known as TOLCS (Tweets Ordinance Linkage by Commonsense and Semantics). This technique uses pragmatic aspects in Commonsense Knowledge as well as semantic aspects by domain knowledge. By reducing the sample space of big data to be processed, this method represents an efficient way to accomplish this task.
The ultimate goal of the project is to see how closely a given region is heading towards the concept of Smart City
Natural Language Processing in-and-for Design Research
We review the scholarly contributions that utilise Natural Language
Processing (NLP) methods to support the design process. Using a heuristic
approach, we collected 223 articles published in 32 journals and within the
period 1991-present. We present state-of-the-art NLP in-and-for design research
by reviewing these articles according to the type of natural language text
sources: internal reports, design concepts, discourse transcripts, technical
publications, consumer opinions, and others. Upon summarizing and identifying
the gaps in these contributions, we utilise an existing design innovation
framework to identify the applications that are currently being supported by
NLP. We then propose a few methodological and theoretical directions for future
NLP in-and-for design research
Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities
This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development
Emotion Quantification Using Variational Quantum State Fidelity Estimation
Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset
Data properties and the performance of sentiment classification for electronic commerce applications
Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.This study was partially funded by Korea National Research Foundation through Global Research Network Program (Project no. 2016S1A2A2912265) and EU funded project Policy Compass (Project no. 283700)
Investigating transportation research based on social media analysis: A systematic mapping review
Social media is a pool of users’ thoughts, opinions, surrounding environment, situation and others. This pool can be used as a real-time and feedback data source for many domains such as transportation. It can be used to get instant feedback from commuters; their opinions toward the transportation network and their complaints, in addition to the traffic situation, road conditions, events detection and many others. The problem is in how to utilize social media data to achieve one or more of these targets. A systematic review was conducted in the field of transportation-related research based on social media analysis (TRRSMA) from the years between 2008 and 2018; 74 papers were identified from an initial set of 703 papers extracted from 4 digital libraries. This review will structure the field and give
an overview based on the following grounds: activity, keywords, approaches, social media data and platforms and focus of the researches. It will show the trend in the research subjects by countries, in addition to the activity trends, platforms usage trend and others. Further
analysis of the most employed approach (Lexicons) and data (text) will be also shown. Finally, challenges and future works are drawn and proposed
PGLDA: enhancing the precision of topic modelling using poisson gamma (PG) and latent dirichlet allocation (LDA) for text information retrieval
The Poisson document length distribution has been used extensively in the past for
modeling topics with the expectation that its effect will disintegrate at the end of the
model definition. This procedure often leads to down Playing word correlation with
topics and reducing retrieved documents' precision or accuracy. The existing
document model, such as the Latent Dirichlet Allocation (LDA) model, does not
accommodate words' semantic representation. Therefore, in this thesis, the PoissonGamma
Latent Dirichlet Allocation (PGLDA) model for modeling word
dependencies in topic modeling is introduced. The PGLDA model relaxes the words
independence assumption in the existing Latent Dirichlet Allocation (LDA) model
by introducing the Gamma distribution that captures the correlation between adjacent
words in documents. The PGLDA is hybridized with the distributed representation of
documents (Doc2Vec) and topics (Topic2Vec) to form a new model named
PGLDA2Vec. The hybridization process was achieved by averaging the Doc2Vec
and Topic2Vec vectors to form new word representation vectors, combined with
topics with the largest estimated probability using PGLDA. Model estimations for
PGLDA and PGLDA2Vec models were achieved by combining the Laplacian
approximation of log-likelihood for PGLDA and Feed-Forward Neural Network
(FFN) approaches of Doc2Vec and Topic2Vec. The proposed PGLDA and the
hybrid PGLDA2Vec models were assessed using precision, micro F1 scores,
perplexity, and coherence score. The empirical analysis results using three real-world
datasets (20 Newsgroups, AG'News, and Reuters) showed that the hybrid
PGLDA2Vec model with an average precision of 86.6%, and an average F1 score of
96.3%, across the three datasets is better than other competing models reviewed