Modified EDA and Backtranslation Augmentation in Deep Learning Models for Indonesian Aspect-Based Sentiment Analysis

In the process of developing a business, aspect-based sentiment analysis (ABSA) could help extract customers' opinions on different aspects of the business from online reviews. Researchers have found great prospective in deep learning approaches to solving ABSA tasks. Furthermore, studies have also explored the implementation of text augmentation, such as Easy Data Augmentation (EDA), to improve the deep learning models’ performance using only simple operations. However, when implementing EDA to ABSA, there will be high chances that the augmented sentences could lose important aspects or sentiment-related words (target words) critical for training. Corresponding to that, another study has made adjustments to EDA for English aspect-based sentiment data provided with the target words tag. However, the solution still needs additional modifications in the case of non-tagged data. Hence, in this work, we will focus on modifying EDA that integrates POS tagging and word similarity to not only understand the context of the words but also extract the target words directly from non-tagged sentences. Additionally, the modified EDA is combined with the backtranslation method, as the latter has also shown quite a significant contribution to the model’s performance in several research studies. The proposed method is then evaluated on a small Indonesian ABSA dataset using baseline deep learning models. Results show that the augmentation method could increase the model’s performance on a limited dataset problem. In general, the best performance for aspect classification is achieved by implementing the proposed method, which increases the macro-accuracy and F1, respectively, on Long Short-Term Memory (LSTM) and Bidirectional LSTM models compared to the original EDA. The proposed method also obtained the best performance for sentiment classification using a convolutional neural network, increasing the overall accuracy by 2.2% and F1 by 3.2%. Doi: 10.28991/ESJ-2023-07-01-018 Full Text: PD

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

The Usage of Personal Data as Content in Integrated Marketing Communications

Personal user data has proven extremely valuable for firms in the digital age. The wealth of data available to firms has provided unprecedented access into the world of the consumer. Companies hoping to capitalize on their user's data have turned to several interesting outlets. This research addresses the repurposing of user data as content in marketing. By analyzing four cases of data presented as marketing communications across two companies, this research provides new insights into the public release of private user data for marketing purposes. Four cases of personal data used in marketing communications were chosen specifically for their time proximity, characteristics of the sending firms, and their disparate outcomes. These instances of marketing communications, two by Spotify and two by Netflix, were released during November and December of 2017 and each resulted in a diverse range of public opinion. An analysis of these cases was conducted using the comprehensive framework of integrated marketing communications (Tafesse & Kitchen, 2017). There is a significant difference in the perceptual outcomes of integrated marketing communication campaigns which display user data as content. This analysis provides insights into the characteristics of marketing communications and how their outcomes fit into broader marketing strategies. These case studies provide opportunities for marketers to improve their campaigns in line with their desired audience outcome. Patterns of scope, strategy, mode, and outcome do not suggest success or failure in the context of marketing communications, but rather a set of insights marketers should keep in mind when pursuing communication strategies which harness personal user data.No embargoAcademic Major: Marketin