Recommending Items with Conditions Enhancing User Experiences Based on Sentiment Analysis of Reviews

ABSTRACT In this paper we propose a new method of recommending not only items of interest to the user but also the conditions enhancing user experiences with those items, such as recommending to go to a restaurant for seafood. This method is based on the sentiment analysis of user reviews, predicts sentiments that the user might express about the aspects determined in an application, and identifies the most valuable aspects of user's potential experience with the item. Furthermore, our method recommends the items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant, e.g. lunch vs. dinner, or what to have there, such as seafood. We tested our method on three applications (restaurants, hotels and beauty&spas) and experimentally showed that those users who followed our recommendations of items with their corresponding conditions had better experiences, as defined by the overall rating, than others

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. 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Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. doi:10.1109/acii.2013.90Nicolaou, M. A., Gunes, H., & Pantic, M. (2011). Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space. IEEE Transactions on Affective Computing, 2(2), 92-105. doi:10.1109/t-affc.2011.9Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. (2016). Audio-Visual Emotion Recognition Using Big Data Towards 5G. Mobile Networks and Applications, 21(5), 753-763. doi:10.1007/s11036-016-0685-9Zhou, F., Jianxin Jiao, R., & Linsey, J. S. (2015). Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews. Journal of Mechanical Design, 137(7). doi:10.1115/1.4030159Ceci, F., Goncalves, A. L., & Weber, R. (2016). A model for sentiment analysis based on ontology and cases. IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. 2015 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2015.7351718Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Matsiola, M., Dimoulas, C., Kalliris, G., & Veglis, A. A. (2018). Augmenting User Interaction Experience Through Embedded Multimodal Media Agents in Social Networks. Information Retrieval and Management, 1972-1993. doi:10.4018/978-1-5225-5191-1.ch088Rosaci, D. (2007). CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents. Information Systems, 32(6), 793-825. doi:10.1016/j.is.2006.06.003Buccafurri, F., Comi, A., Lax, G., & Rosaci, D. (2016). Experimenting with Certified Reputation in a Competitive Multi-Agent Scenario. 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Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

토큰 단위 분류모델을 위한 중요 토큰 포착 및 시퀀스 인코더 설계 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 정교민.With the development of internet, a great of volume of data have accumulated over time. Therefore, dealing long sequential data can become a core problem in web services. For example, streaming services such as YouTube, Netflx and Tictoc have used the user's viewing history sequence to recommend videos that users may like. Such systems have replaced the user's viewed video with each item or token to predict what item or token will be viewed next. These tasks have been defined as Token-Level Classification (TLC) tasks. Given the sequence of tokens, TLC identifies the labels of tokens in the required portion of this sequence. As mentioned above, TLC can be applied to various recommendation Systems. In addition, most of Natural Language Processing (NLP) tasks can also be formulated as TLC problem. For example, sentence and each word within the sentence can be expressed as token-level sequence. In particular, in the case of information extraction, it can be changed to a TLC task that distinguishes whether a specific word span in the sentence is information. The characteristics of TLC datasets are that they are very sparse and long. Therefore, it is a very important problem to extract only important information from the sequences and properly encode them. In this thesis, we propose the method to solve the two academic questions of TLC in Recommendation Systems and information extraction: 1) How to capture important tokens from the token sequence and 2) How to encode a token sequence into model. As deep neural networks (DNNs) have shown outstanding performance in various web application tasks, we design the RNN and Transformer-based model for recommendation systems, and information extractions. In this dissertation, we propose novel models that can extract important tokens for recommendation systems and information extraction systems. In recommendation systems, we design a BART-based system that can capture important portion of token sequence through self-attention mechanisms and consider both bidirectional and left-to-right directional information. In information systems, we present relation network-based models to focus important parts such as opinion target and neighbor words.인터넷의 발달로, 많은 양의 데이터가 시간이 지남에 따라 축적되었다. 이로인해 긴 순차적 데이터를 처리하는 것은 웹 서비스의 핵심 문제가 되었다. 예를 들어, 유튜브, 넷플릭스, 틱톡과 같은 스트리밍 서비스는 사용자의 시청 기록 시퀀스를 사용하여 사용자가 좋아할 만한 비디오를 추천한다. 이러한 시스템은 다음에 어떤 항목이나 토큰을 볼 것인지를 예측하기 위해 사용자가 본 비디오를 각 항목 또는 토큰으로 대체하여 사용할 수 있다. 이러한 작업은 토큰 수준 분류(TLC) 작업으로 정의한다. 토큰 시퀀스가 주어지면, TLC는 이 시퀀스의 필요한 부분에서 토큰의 라벨을 식별한다. 이렇게와 같이, TLC는 다양한 추천 시스템에 적용될 수 있다. 또한 대부분의 자연어 처리(NLP) 작업은 TLC 문제로 공식화될 수 있다. 예를 들어, 문장과 문장 내의 각 단어는 토큰 레벨 시퀀스로 표현될 수 있다. 특히 정보 추출의 경우 문장의 특정 단어 간격이 정보인지 여부를 구분하는 TLC 작업으로 바뀔 수 있다. TLC 데이터 세트의 특징은 매우 희박(Sparse)하고 길다는 것이다. 따라서 시퀀스에서 중요한 정보만 추출하여 적절히 인코딩하는 것은 매우 중요한 문제이다. 본 논문에서는 권장 시스템과 정보 추출에서 TLC의 두 가지 학문적 질문- 1) 토큰 시퀀스에서 중요한 토큰을 캡처하는 방법 및 2) 토큰 시퀀스를 모델로 인코딩하는 방법 을 해결하는 방법을 제안한다. 심층 신경망(DNN)이 다양한 웹 애플리케이션 작업에서 뛰어난 성능을 보여 왔기 때문에 추천 시스템 및 정보 추출을 위한 RNN 및 트랜스포머 기반 모델을 설계한다. 먼저 우리는 자기 주의 메커니즘을 통해 토큰 시퀀스의 중요한 부분을 포착하고 양방향 및 좌우 방향 정보를 모두 고려할 수 있는 BART 기반 추천 시스템을 설계한다. 정보 시스템에서, 우리는 의견 대상과 이웃 단어와 같은 중요한 부분에 초점을 맞추기 위해 관계 네트워크 기반 모델을 제시한다.1. Introduction 1 2. Token-level Classification in Recommendation Systems 8 2.1 Overview 8 2.2 Hierarchical RNN-based Recommendation Systems 19 2.3 Entangled Bidirectional Encoder to Auto-regressive Decoder for Sequential Recommendation 27 3. Token-level Classification in Information Extraction 39 3.1 Overview 39 3.2 RABERT: Relation-Aware BERT for Target-Oriented Opinion Words Extraction 49 3.3 Gated Relational Target-aware Encoder and Local Context-aware Decoder for Target-oriented Opinion Words Extraction 58 4. Conclusion 79박

Sentiment Analysis of Twitter Data for a Tourism Recommender System in Bangladesh

The exponentially expanding Digital Universe is generating huge amount of data containing valuable information. The tourism industry, which is one of the fastest growing economic sectors, can benefit from the myriad of digital data travelers generate in every phase of their travel- planning, booking, traveling, feedback etc. One application of tourism related data can be to provide personalized destination recommendations. The primary objective of this research is to facilitate the business development of a tourism recommendation system for Bangladesh called “JatraLog”. Sentiment based recommendation is one of the features that will be employed in the recommendation system. This thesis aims to address two research goals: firstly, to study Sentiment Analysis as a tourism recommendation tool and secondly, to investigate twitter as a potential source of valuable tourism related data for providing recommendations for different countries, specifically Bangladesh. Sentiment Analysis can be defined as a Text Classification problem, where a document or text is classified into two groups: positive or negative, and in some cases a third group, i.e. neutral. For this thesis, two sets of tourism related English language tweets were collected from Twitter using keywords. The first set contains only the tweets and the second set contains geo-location and timestamp along with the tweets. Then the collected tweets were automatically labeled as positive or negative depending on whether the tweets contained positive or negative emoticons respectively. After they were labeled, 90% of the tweets from the first set were used to train a Naive Bayes Sentiment Classifier and the remaining 10% were used to test the accuracy of the Classifier. The Classifier accuracy was found to be approximately 86.5%. The second set was used to retrieve statistical information required to address the second research goal, i.e. investigating Twitter as a potential source of sentiment data for a destination recommendation system