Sentiment analysis for Malay language: systematic literature review

Recent research and developments in Sentiment Analysis (SA) have simplified sentiment detection and classification from textual content. The related domains for these studies are diverse and comprise fields such as tourism, costumer review, finance, software engineering, speech conversation, social media content, news and so on. SA research and developments field have been done on various languages such as Chinese and English language. However, SA research on other languages such as Malay language is still scarce. Thus, there is a need for constructing SA research specifically for Malay language. To understand trends and to support practitioners and researchers with comprehension information with regard to SA for Malay language, this study exhibit to review published articles on SA for Malay language. From five online databases including ACM, Emerald insight, IEEE Xplore, Science Direct, and Scopus, 2433 scientific articles were obtained. Moreover, through the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement, 10 articles have been chosen for the review process. Those articles have been reviewed depend on a few categories consisting of the aim of the study, SA classification techniques, as well as the domain and source of content. As a result, the conducted systematic literature review shed some light about the starting point to research in term of SA for Malay language

A case study on smart band

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 윤명환 .The aim of this study is to prove that the consumer review-based text mining methods proposed in the paper for cross-cultural design are effective. To prove it, we took Mi band 3 as a case study where we compared the cross-cultural differences in product preference of users from different cultural regions with this method. With the development of global market, more and more products and services are sold across the globe. Users from different cultures have different behaviors, cognitive styles, and value systems. Therefore, product should be designed to meet the needs and preferences of users from different cultural groups. In the field of cross-cultural design, existing studies are mainly focused on traditional usability and UX research methods. However, these methods expose some disadvantages when applied into cross-cultural design contexts. E-commerce websites provide a large volume of product reviews and it is easy to collect review data online. There is no need to employ foreign participants or make a survey onsite or remotely, which will save much more cost and time. There is a new trend that customer reviews are examined to know consumer opinions. Neverlessness, there are not many studies by analyzing online reviews in the field of cross-cultural design. Thus, my research proposed consumer review-based text mining methods for cross-cultural design, which consist of aspect-level opinion mining, sentiment analysis, and semantic network analysis. We collected review data from the following three websites: Naver of South Korea, Jingdong of China, and Amazon of the United States. Text mining methods including opinion mining, sentiment analysis, and semantic network analysis were performed. Firstly, product aspects were extracted from reviews according to word frequency. This indicates how much users are paying attention to different aspects of the product. Aspect-level sentiment analysis was conducted to find out customer satisfaction with different product aspects. Then, the words most associated with each product aspect were listed. Cluster analysis was conducted and the topic of each cluster was summarized. Data visualization of each dataset was done. Lastly, cross-cultural difference among three countries from the results was observed and discussed. Though there exist similar issues in product preferences of users from South Korea, China, and the United States, cross-cultural differences about Mi band 3 are shown in many product aspects. Korean tend to take Mi band as a fashionable, cool, yet not useful wearable device. They often buy it as a nice gift. They are interested in the appearance of the strap and often buy straps of different colors and materials. Korean do not enjoy outdoor activities as much as American. And the function of NFC is not prevalent in Korea. Thus, the smart band is not useful to Korean. These can explain why Korean do not care about quality of the smart band and do not want to buy Mi band at a high price. Korean think that the language of Korean on the display, application, and manual is the most important feature. The length of Korean texts is longer than Chinese to convey the same information. On the other hand, Korean prefer to check message notification on smart band rather than call notification. Therefore, Korean need a larger size for screen. Chinese are more concerned about different kinds of functions including fitness tracker (step counting, heart rate monitoring, and sleep monitoring), notification, and NFC. These different functions are all important and practical to Chinese. American enjoy outdoor activities and tend to use smart band mostly as activity tracker. They care more about activity tracker function including heart rate monitoring and step counting than Korean and Chinese. They have a higher requirement about the accuracy of measured data and have more negative reviews on activity tracker function than Korean and Chinese. Besides, they need the mode for swimming. Because American usually use the smart band for outdoor activities, they complain a lot that the screen is prone to scratches and is invisible under the outdoor sunlight. Also, they pay attention to the quality of screen and strap, expecting the material make the screen and strap durable. Besides, battery is the most significant aspect to American. They always try to test each function to find which function makes battery life short. The results of the case study prove that the consumer review-based text mining method proposed in the paper can generate cross-cultural difference in product preference effectively, which is helpful to cross-cultural design research. And this method is relatively easy and fast compared to other conventional methods.Chapter 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Objective 3 1.3 Organization of the Thesis 4 Chapter 2. Literature Review 5 2.1 Cross-Cultural Design 5 2.1.1 Definition 5 2.1.2 Necessity 6 2.1.3 Method 7 2.2 Opinion Mining and Sentiment Analysis 10 2.2.1 Aspect Level Opinion Mining 10 2.2.2 Cross-Lingual Opinion Mining 11 2.3 Semantic Network Analysis 13 Chapter 3. Methodology 15 3.1 Data Collection 15 3.2 Data Processing 16 3.2.1 Text Preprocessing 16 3.2.2 Opinion Mining and Sentiment Analysis 16 3.2.3 Semantic Network Analysis 17 3.2.4 Result Sample 18 Chapter 4. Result 20 4.1 Overview 20 4.2 Opinion Mining and Sentiment Analysis 21 4.2.1 Normalized Frequency 21 4.2.2 Sentiment Analysis 23 4.3 Semantic Network Analysis 26 4.3.1 Associated Words 26 4.3.1 Cluster Analysis 31 4.3.1 Data Visualization 34 4.4 Results based on Aspects 37 4.4.1 Battery 37 4.4.2 Price 39 4.4.3 Function 41 4.4.4 Step Counting 43 4.4.5 Korean 45 4.4.6 Heart Rate Monitoring 47 4.4.7 Sleep Monitoring 49 4.4.8 Quality 51 4.4.9 Notification 53 4.4.10 Screen 55 4.4.11 Exercise 57 4.4.12 App 59 4.4.13 Call 61 4.4.14 Connection 63 4.4.15 Waterproof 65 4.4.16 Display 67 4.4.17 Message 69 4.4.18 Alarm 71 4.4.19 Gift 73 4.4.20 Strap 75 Chapter 5. Conclusion 78 5.1 Summary of Findings 78 5.2 Future Research 80 Bibliography 82Maste

Sentiment Analysis in Sales Estimation: An Econometric Analysis of Product Listings and Reviews in a Chinese Cross-Border E-Commerce Context

Since the advent of electronic word-of-mouth communication, particularly in the form of user-generated reviews on e-commerce platforms, research has been undertaken to quantify and draw insights from this growing wealth of data. Coinciding developments in machine learning and natural language processing have enabled the systemic analysis of these texts, heightening the role of user feedback from a simple information channel between users to an indispensable source of “big data” information regarding consumer sentiment and behaviour. While otherwise extensive, contemporary research into the role of consumer sentiment, and, in particular, its effect on sales outcomes, is largely built around data gathered from Western e-commerce platforms, most notably Amazon. This has potentially limited its generalizability to wider contexts. In addition, many studies simplify the role of feedback valence by interpreting the sentiment polarity of a written review as equivalent to its corresponding numerical rating – a conflation that seems to go against existing research into rating inflation and other biases. This study seeks to further this field of e-commerce research by accounting for these issues. Us-ing cross-sectional data gathered from an industry-leading Chinese cross-border e-commerce platform, this study analyses the relationships between user-generated review sentiments and order amounts in a new context. By applying three different sentiment analysis tools to a total of 451,375 product reviews, overall sentiment polarity and subjectivity metrics were calculated for 8,319 product listings. Using these values, alongside other control variables (including numerical ratings, separate from sentiment polarities) from the listings, econometric regression models de-scribing the relationships were estimated and interpreted. The findings of this study demonstrate that, on a broad level, the notion of review sentiment polarity being positively related to sales outcomes is generalizable beyond the Western context. The role of a more nuanced aspect of review sentiments, namely the subjectivity of reviews, is found to be seemingly different from existing research into Western platforms, albeit somewhat inconclusively. The findings also support the notion that review sentiment polarity is not directly represented by its corresponding numerical rating, and that future studies should continue to differentiate between these two metrics. This study leaves open the exact causal nature of these relationships, requiring future research using time series data over multiple years. In addition, a greater variety of product categories could be studied in order to confirm the overall generalizability of these findings

Latent sentiment model for weakly-supervised cross-lingual sentiment classification

In this paper, we present a novel weakly-supervised method for crosslingual sentiment analysis. In specific, we propose a latent sentiment model (LSM) based on latent Dirichlet allocation where sentiment labels are considered as topics. Prior information extracted from English sentiment lexicons through machine translation are incorporated into LSM model learning, where preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. An efficient parameter estimation procedure using variational Bayes is presented. Experimental results on the Chinese product reviews show that the weakly-supervised LSM model performs comparably to supervised classifiers such as Support vector Machines with an average of 81% accuracy achieved over a total of 5484 review documents. Moreover, starting with a generic sentiment lexicon, the LSM model is able to extract highly domainspecific polarity words from text