Exploiting BERT and RoBERTa to Improve Performance for Aspect Based Sentiment Analysis

Sentiment Analysis also known as opinion mining is a type of text research that analyses people’s opinions expressed in written language. Sentiment analysis brings together various research areas such as Natural Language Processing (NLP), Data Mining, and Text Mining, and is fast becoming of major importance to companies and organizations as it is started to incorporate online commerce data for analysis. Often the data on which sentiment analysis is performed will be reviews. The data can range from reviews of a small product to a big multinational corporation. The goal of performing sentiment analysis is to extract information from those reviews to gauge public opinion for market research, monitor brand and product reputation, and understand customer experiences. Reviews written on the online platform are often in the form of free text and they do not have any standard structure. Dealing with unstructured data is a challenging problem. Sentiment analysis can be done at different levels, and the focus of this research is on aspect-level sentiment analysis. In aspect-level sentiment analysis, there are two tasks that need to be addressed. The first task is aspect identification which is the process of discovering those attributes of the object that people are commenting on. These attributes of the object are called aspects. The second task is the sentiment classification of those reviews using these extracted aspects. For the sentiment analysis, transformer-based pre-trained models such as BERT (Bidirectional Encoder Representations from Transformers) and RoBERTa (A robustly optimized BERT) are used in this research as they make use of embedding vector space that is rich in context. The purpose of this research is to propose a framework for extracting the aspects from the data which can be applied to these pre-trained models. For the first part of the experiment, both the BERT and RoBERTa models are developed without the aspect-based approach. For the second part of the experiment, the aspect-based approach is applied to the same models and their results are compared and evaluated against the equivalent models. The experiment results show that aspect-based approach has increased the performance of the models by almost 1% than the traditional models and the BERT model with the aspect-based approach had the highest accuracy and performance among all the models evaluated in this research.

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

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

A novel deterministic approach for aspect-based opinion mining in tourism products reviews

This work proposes an extension of Bing Liu's aspect-based opinion mining approach in order to apply it to the tourism domain. The extension concerns with the fact that users refer differently to different kinds of products when writing reviews on the Web. Since Liu's approach is focused on physical product reviews, it could not be directly applied to the tourism domain, which presents features that are not considered by the model. Through a detailed study of on-line tourism product reviews, we found these features and then model them in our extension, proposing the use of new and more complex NLP-based rules for the tasks of subjective and sentiment classification at the aspect-level. We also entail the task of opinion visualization and summarization and propose new methods to help users digest the vast availability of opinions in an easy manner. Our work also included the development of a generic architecture for an aspect-based opinion mining tool, which we then used to create a prototype and analyze opinions from TripAdvisor in the context of the tourism industry in Los Lagos, a Chilean administrative region also known as the Lake District. Results prove that our extension is able to perform better than Liu's model in the tourism domain, improving both Accuracy and Recall for the tasks of subjective and sentiment classification. Particularly, the approach is very effective in determining the sentiment orientation of opinions, achieving an F-measure of 92% for the task. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions, using a non-extended approach for this task. Finally, results also showed the effectiveness of our design when applied to solving the industry's special issues in the Lake District, since almost 80% of the users that used our tool considered that our tool adds valuable information to their business

Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202