A hybrid model for aspect-based sentiment analysis on customer feedback: research on the mobile commerce sector in Vietnam

Feedback and comments on mobile commerce applications are extremely useful and valuable information sources that reflect the quality of products or services to determine whether data is positive or negative and help businesses monitor brand and product sentiment in customers’ feedback and understand customers’ needs. However, the increasing number of comments makes it increasingly difficult to understand customers using manual methods. To solve this problem, this study builds a hybrid research model based on aspect mining and comment classification for aspect-based sentiment analysis (ABSA) to deeply comprehend the customer and their experiences. Based on previous classification results, we first construct a dictionary of positive and negative words in the e-commerce field. Then, the POS tagging technique is applied for word classification in Vietnamese to extract aspects of model commerce related to positive or negative words. The model is implemented with machine and deep learning methods on a corpus comprising more than 1,000,000 customer opinions collected from Vietnam's four largest mobile commerce applications. Experimental results show that the Bi-LSTM method has the highest accuracy with 92.01%; it is selected for the proposed model to analyze the viewpoint of words on real data. The findings are that the proposed hybrid model can be applied to monitor online customer experience in real time, enable administrators to make timely and accurate decisions, and improve the quality of products and services to take a competitive advantage

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201

A Survey on Deep Learning Techniques for Sentiment Analysis

Social media is a rich source of information nowadays. If we look into social media, sentiment analysis is one of the challenging problems. Sentiment analysis is a substantial area of research in the field of Natural Language Processing. This survey paper reviews and provides the comparative study of deep learning approaches CNN, RNN, LSTM and ensemble-based methods

Understanding Vietnamese consumers’ perception and word-of-mouth intentions towards Airbnb

PurposeThis paper aims to provide a comprehensive understanding of Vietnamese consumers' perceived value and to explore the relationships between its constructs, satisfaction and (e)word-of-mouth (WOM) intentions towards Airbnb. Moreover, the relationship between traditional WOM and electronic WOM (eWOM) was also investigated. Design/methodology/approachAn electronic survey was applied to collect data on a sample of Vietnamese Airbnb guests. A total of 352 questionnaires were collected, from which 163 eligible Airbnb users remained for data analysis. The partial least square approach to structural equation modelling was used to analyse the data. FindingsThe findings suggested that monetary, functional and hedonic benefits significantly impact Vietnamese customer satisfaction (CS) with Airbnb accommodation, which, in turn, acts as a direct effect and mediator in encouraging customers' (e)WOM-giving intentions. Moreover, traditional WOM intention positively influences eWOM giving intention. Originality/valueThis study provides a better comprehension of customers' perceived value that influences CS and their (e)WOM intentions towards Airbnb. Secondly, it extends the literature on WOM intentions from the message communicator's perspective by confirming the positive association between traditional and eWOM-giving intentions. Finally, this paper reveals insights into the sharing accommodation in a fast-growing market in South East Asia (Vietnam), which supports sharing accommodation platforms and service providers to develop appropriate marketing strategies.info:eu-repo/semantics/publishedVersio

Text pre-processing of multilingual for sentiment analysis based on social network data

Sentiment analysis (SA) is an enduring area for research especially in the field of text analysis. Text pre-processing is an important aspect to perform SA accurately. This paper presents a text processing model for SA, using natural language processing techniques for twitter data. The basic phases for machine learning are text collection, text cleaning, pre-processing, feature extractions in a text and then categorize the data according to the SA techniques. Keeping the focus on twitter data, the data is extracted in domain specific manner. In data cleaning phase, noisy data, missing data, punctuation, tags and emoticons have been considered. For pre-processing, tokenization is performed which is followed by stop word removal (SWR). The proposed article provides an insight of the techniques, that are used for text pre-processing, the impact of their presence on the dataset. The accuracy of classification techniques has been improved after applying text pre-processing and dimensionality has been reduced. The proposed corpus can be utilized in the area of market analysis, customer behaviour, polling analysis, and brand monitoring. The text pre-processing process can serve as the baseline to apply predictive analysis, machine learning and deep learning algorithms which can be extended according to problem definition

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

The Role of Digital Marketing Platforms on Supply Chain Management for Customer Satisfaction and Loyalty in Small and Medium Enterprises (SMEs) at Indonesia

Abstract. The main aim is to investigate the digital technologies supporting small and medium enterprises (SMEs) operating in creative industries in their supply chain management strategies. In the globalization era, digital marketing platforms play a significant role in increasing customer loyalty and indirectly effect on the economic growth of a community. In conjunction with its essential issue, the current study aims to investigate the role of digital marketing platforms (online media) and its structural relationship to the consumer satisfaction and loyalty in SMEs at Aceh province, Indonesia. This study designed using Quantitative analysis with a cross-sectional study through a survey questionnaire. This study involved all of the customers the SMEs products in Aceh province, Indonesia. A total of 219 customers have participated in answering the survey questionnaire via Google forms. The data analyzed using the Analysis of Moments Structure by assisting the statistical software IBM - AMOS Version 22. The digital platforms performed by using the Sobel test. The results of this study found that the digital marketing for supply chain (online media) significantly affect consumer satisfaction. Then, digital supply chain has a significant effect on consumer satisfaction. Also, this study found that the product review does not significantly affect the consumer loyalty. Besides that, the consumer satisfaction and the use of online media directly affect the consumer loyalty. In conclusions, this study successfully investigated the online media digital supply chain, and consumer satisfaction and its structural relationship on consumer loyalty in SMEs products that viewed as a digital marketing perspective. Then, this study comprehensively evaluated the role of digital supply chain, consumer satisfaction, and consumer loyalty in providing SME products with competitive advantages that contribute to regional economic growth. Supply chain management practices were also statistically significant, and mediated the relationship between customer satisfaction and product quality and flexibilit