Novel Methods for Forensic Multimedia Data Analysis: Part I

The increased usage of digital media in daily life has resulted in the demand for novel multimedia data analysis techniques that can help to use these data for forensic purposes. Processing of such data for police investigation and as evidence in a court of law, such that data interpretation is reliable, trustworthy, and efficient in terms of human time and other resources required, will help greatly to speed up investigation and make investigation more effective. If such data are to be used as evidence in a court of law, techniques that can confirm origin and integrity are necessary. In this chapter, we are proposing a new concept for new multimedia processing techniques for varied multimedia sources. We describe the background and motivation for our work. The overall system architecture is explained. We present the data to be used. After a review of the state of the art of related work of the multimedia data we consider in this work, we describe the method and techniques we are developing that go beyond the state of the art. The work will be continued in a Chapter Part II of this topic

Evaluasi Kepuasan Pelanggan Hotel Berdasarkan Analisa Sentiment Pada Review Pelanggan

Customer relationship management (CRM) memiliki pengaruh yang sangat besar bagi kinerja perusahaan. Hubungan pelanggan dengan perusahaan saat ini sangat mudah untuk dilakukan, salah satunya melalui website pada review online. Review online akan sangat membantu perusahaan untuk mengetahui hal apa dari bisnis tersebut yang disenangi pelanggan maupun yang tidak. Untuk mempermudah perusahaan dalam mengetahui kepuasan pelanggan, diusulkan penelitian untuk mencari sentiment kepuasan dari setiap review sesuai dengan kategori aspect hotel kemudian melakukan evaluasi kepuasan. Aspect yang dimaksud terdiri dari: location, meal, service, comfort serta cleanliness hotel. Penelitian ini mengambil teks review dalam bahasa Inggris. Kategorisasi aspect akan dilakukan dengan beberapa tahapan, pertama menggunakan Latent Dirichlet Allocation (LDA) sebagai metode untuk menemukan hidden topic dari review. Latent Dirichlet Allocation (LDA) memiliki kekurangan untuk mengklasifikasikan dokumen ke dalam salah satu aspect secara langsung. Sehingga pada tahap kedua diusulkan metode Semantic Similarity untuk mengkategorikan setiap hidden topic review yang dihasilkan oleh Latent Dirichlet Allocation (LDA) pada 5 aspect hotel. Kemudian dalam menghitung Semantic Similatiry, term list akan diperluas dengan menggunakan metode Term Frequency-Inverse Cluster Frequency (TF-ICF). Akhirnya, dilakukan proses klasifikasi terhadap sentiment pelanggan (puas atau tidak puas) menggunakan Word Embedding untuk mengekstraksi setiap kata dan dokumen menjadi vector kata yang kemudian akan digunakan sebagai input untuk proses klasifikasi menggunakan metode Long Short Tem Memmory (LSTM). setelah ditemukan sentimen pada setiap aspect, selanjutnya akan dilakukan evaluasi hasil. Performa dari setiap metode dievaluasi menggunakan precision, recall dan F1-Measure. Hasil dari uji coba menunjukkan bahwa performa kategorisasi aspect tertinggi dilakukan dengan melakukan penggabungan metode Latent Dirichlet Allocation (LDA) untuk mencari hidden topic, digabungkan dengan Term Frequency-Inverse Cluster Frequency (TF-ICF) 100% untuk peluasan term dan Semantic Similarity untuk kategorisasi aspect yang mendapatkan hasil performa hingga mencapai 85% dan performa Word Embedding untuk representasi angka vector dengan Long Short Term Memmory (LSTM) untuk klasifikasi sentiment sangat tinggi yang mendapatkan performa mencapai 94%. Sehingga, peneliti melakukan penggabungan metode LDA+TF-ICF 100%+Semantic Similarity untuk melakukan kategorisasi aspect lalu menggunakan Word Embedding+LSTM untuk melakukan klasifikasi sentiment pada setiap review. Kemudian, pada evaluasi akhir yang dilakukan, peneliti mendapatkan bahwa aspect comfort hotel memiliki review dengan sentiment negative sangat tinggi yang mencapai 11,369% dibanding dengan sentiment review pada aspect lainnya (location: 0.464, meal: 0.696, service: 3.016, dan cleanliness: 1.160) sehingga pihak manajemen hotel perlu melakukan perbaikan-perbaikan untuk lebih memperhatikan kenyamanan pelanggan dengan tujuan untuk mengurangi jumlah review negative pada aspect comfort tersebut. Hasil juga menunjukkan bahwa perubahan sentiment (pada positive atau negative sentiment) dipengaruhi oleh aspect yang dimiliki oleh setiap review. ================================================================================================ Customer relationship management (CRM) has a huge influence on company performance. Nowadays, customers can contact companies in easy ways, one of them is through the website on an online review. Online reviews will greatly help the company to find out any of the business that makes customers like it or not. To help companies determine customer satisfaction, the proposed research to find satisfaction sentiment of each review in accordance with aspects of the category of the hotel then do an evaluation of satisfaction. Aspect is composed of location, meal, service, comfort, and cleanliness of the hotel. This research will take a review text in English. These aspects were classified in several stages, first using the Latent Dirichlet Allocation (LDA) was used as a method to find the hidden topic of a document. Latent Dirichlet Allocation (LDA) has the disadvantage to classify documents into one aspect directly. So that in the second stage the Semantic Similarity method was proposed to categorize each hidden topic review produced by Latent Dirichlet Allocation (LDA) on 5 aspects of the hotel. Then in calculating the Semantic Similarity, term list will be expanded by using Cluster Term Frequency-Inverse Frequency (TF-ICF). Finally, the classification of customer sentiment (satisfied or dissatisfied) is done using Word Embedding to extract each word and document into a word vector which will then be used as input for the classification process using the LSTM method. After finding sentiment on each aspect, then the results evaluation will be carried out. The performance of each method is evaluated using precision, recall and F1-Measure. The results of the trials show that the highest performance of aspect categorization is done by combining the Latent Dirichlet Allocation (LDA) method to search hidden topics, combined with 100% Term Frequency-Inverse Cluster Frequency (TF-ICF) for expansion term and Semantic Similarity for categorization that get performance results up to 85% and Word Embedding for word vector representation combined with Long Short Term Memmory (LSTM) is getting very high sentiment classifications that get a performance of 94%. So, the researcher merged the LDA + TF-ICF 100% + Semantic Similarity method to categorize aspects and then used Word Embedding + LSTM to classify sentiments in each review. Then, at the final evaluation, the researcher found that the comfort aspect of the hotel had a review with very high negative sentiment which reached 11,369% compared to other aspects of the review sentiment (location: 0.464, meal: 0.696, service: 3.016, dan cleanliness: 1.160) so the hotel management needs to make improvements to pay more attention to customer convenience in order to reduce the number of negative reviews on the comfort aspect. The results also show that changes in sentiment (in positive or negative sentiments) are influenced by the aspects of each review

제품 상호작용 시 정성적 사용자 경험 이해를 위한 의미망 분석 방법

학위논문 (박사)-- 서울대학교 대학원 공과대학 산업·조선공학부, 2017. 8. 윤명환.Qualitative research provides useful insights with which to analyze the User Experience (UX). This is distinguished from quantitative research by its inductive form of logic and the research aim of understanding holistic phenomena. Since qualitative research aims to identify intangible factors and explore phenomena without simplifying contextual information, it is difficult to exclude a researchers subjectivity during their analysis. In addition, interpreting and analyzing qualitative materials requires much time and effort. Therefore, this dissertation suggests a systematic research method that utilizes user expression data to understand UX. The research starts by transforming textual data into numerical representations using semantic network analysisthree major issues were elucidated from the limitations of existing methods: (1) examining the representativeness of the sample size, (2) eliciting important user values (UV), and (3) evaluating product attributes (PA) with numerical inferences. First, the representativeness of sample size was examined by observing the stability of a semantic network. Among the semantic networks generated from the text, subnetworks were sampled from the original network to vary the sample size. Then, similarities between subnetworks and the original were calculated by applying correlation analysis to node-level centralities. Three case studies that were composed of two interview datasets and one online review data were presentedthese proved that this method could be applicable for both small and large samples. Second, a mixed-method research approach was introduced to suggest appropriate camera shutter press sounds. In qualitative research, important UVs were elicited by analyzing terms with high centralities in a semantic network. The elicited UVs were then used as questionnaire items in quantitative research to represent UV with numerical values. The result demonstrated user satisfaction models for shutter press sounds and the relationships between UV and PA by adopting the concept of psychoacoustic variables. Third, the importance of UV and their relations to PA were examined based on qualitative research on vacuum cleaners. Seven types of network centrality were used to weight the UVs, which resulted in UX quantification models. These models goodness-of-fit were compared to the results of quantitative research. Then, the links between UV and PA nodes were identified. Since statistical analysis without a proper theoretical interpretation may mislead users, qualitative data can assist quantitative research by examining the sematic associations between UV and PA. Compared to traditional qualitative studies, the proposed method in this dissertation has a competitive edge for reducing the cost, effort, and subjectivity. Determining the smallest sample size that can achieve network stability is a novel data collection strategy that attempts to maximize effectiveness while minimizing both cost and effort. Utilizing this method allows UX researchers and practitioners to collect the optimal sample size by gradually increasing their sample sizes. Important UVs were elicited in the process of evaluating UX, and their importance was quantified to build a UX quantification model. Transferring qualitative descriptions to the quantitative models allows researchers to understand UX more efficiently by reducing the process of collecting numerical data on each UV. Lastly, important PA and their relations to UV were identified. Although centrality measures were not proportional to the correlation level, semantic associations between UV and PA could be identified. Considering that huge amounts of text data are being generated and collected every day, the suggested method is expected to be useful for practical applications when developing products.CHAPTER 1. Introduction 1 1.1. Background and motivation 1 1.2. Research objective 6 1.2.1. Examine representativeness 7 1.2.2. Identify user values (UV) 7 1.2.3. Relate product attributes (PA) 8 1.3. Dissertation outline 9 CHAPTER 2. Literature Review 11 2.1. Semantic network analysis 11 2.1.1. Definition 11 2.1.2. Co-occurrence 12 2.1.3. Network statistics 14 2.2. Sample size 19 2.2.1. Reliability of qualitative text data 19 2.2.2. Sample size of HCI studies 20 2.3. User experience (UX) evaluation techniques 22 2.3.1. User value 22 2.3.2. Quantification model 23 2.4. Product design 25 2.4.1. User-Centered Design (UCD) 25 2.4.2. Design method 26 CHAPTER 3. Evaluating Representativeness of Unstructured Text Data 29 3.1. Overview 29 3.2. Method 31 3.2.1. Datasets 31 3.2.2. Research process 33 3.3. Results 40 3.3.1. Descriptive statistics and network-level statistics 40 3.3.2. Number of resampling 44 3.3.3. Network stability analysis 45 3.3.4. Relationship between network characteristics and stability 49 3.4. Discussion 52 CHAPTER 4. Identifying User Values using Qualitative Data for Camera Shutter Sounds 57 4.1. Overview 57 4.2. Measure 59 4.2.1. Loudness (N) 61 4.2.2. Sharpness A (S(A)), Sharpness Z (S(Z)) 62 4.2.3. Roughness (R) 63 4.3. Research process 65 4.3.1. Eliciting PA of camera shutter sounds 66 4.3.2. Conducting jury test on existing camera shutter sounds 67 4.3.3. Eliciting important UVs 67 4.3.4. Evaluating effective PAs 68 4.3.5. Modifying camera shutter sound 69 4.3.6. Conducting jury test on modified shutter sounds 71 4.4. Results 72 4.4.1. User values (UV) 72 4.4.2. User group identification 74 4.4.3. Psychoacoustic analysis of sound samples 75 4.4.4. Regression model of user satisfaction 76 4.4.5. Effect of psychoacoustic variables on UV 77 4.4.6. Effect of PA on psychoacoustic variable 80 4.5. Discussion 80 CHAPTER 5. Identifying User Values and Product Attributes using Qualitative Data on Vacuum Cleaners 87 5.1. Overview 87 5.2. Method 91 5.2.1. Eliciting important UV and PA 93 5.2.2. Suggesting UX quantification model 95 5.2.3. Identifying relevant UV to PA 97 5.3. Results 100 5.3.1. Important UVs 100 5.3.2. UX quantification model 104 5.3.3. Relationship between UV and PA 109 5.3.4. The role of centrality measures 116 5.4. Discussion 118 CHAPTER 6. Conclusion and Discussion 121 6.1. Summary of findings 121 6.2. Practical implications of the research 123 6.3. Limitations and future research 124 BIBLIOGRAPHY 127 APPENDIX A 151 APPENDIX B 154 ABSTRACT (in Korean) 162Docto