Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations

The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, current recommendation methods based on graph embeddings have shown state-of-the-art performance. These methods commonly encode latent rating patterns and content features. Different from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Our approach has the advantage of providing explanations which leverage aspect-based opinions given by users about recommended items. Furthermore, we also provide examples of the applicability of recommendations utilizing aspect opinions as explanations in a visualization dashboard, which allows obtaining information about the most and least liked aspects of similar users obtained from the embeddings of an input graph

Rating and aspect-based opinion graph embeddings for explainable recommendations

The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent recommendation methods based on graph embeddings have shown state-of-the-art performance. In general, these methods encode latent rating patterns and content features. Differently from previous work, in this paper, we propose to exploit embeddings extracted from graphs that combine information from ratings and aspect-based opinions expressed in textual reviews. We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. Additionally, our method has the advantage of providing explanations that involve the coverage of aspect-based opinions given by users about recommended items.Comment: arXiv admin note: substantial text overlap with arXiv:2107.0322

A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss

Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.Comment: Accepted by SIGIR 2020. Updated the results of balanced accuracy scores in Table 3 since we found a bug in our source code. Nevertheless, our model still achieves higher balanced accuracy scores than the baselines after we fixed this bu

A survey on opinion summarization technique s for social media

The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization

Learning Explainable User Sentiment and Preferences for Information Filtering

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often result from interactions such as sharing or liking, but also from interactions in natural language. The continuous growth of the amount of content and of digital traces has led to information overload: surrounded by large volumes of information, people are facing difficulties when searching for information relevant to their interests. To improve user experience, information systems must be able to assist users in achieving their search goals, effectively and efficiently. This thesis is concerned with two important challenges that information systems need to address in order to significantly improve search experience and overcome information overload. First, these systems need to model accurately the variety of user traces, and second, they need to meaningfully explain search results and recommendations to users. To address these challenges, this thesis proposes novel methods based on machine learning to model user sentiment and preferences for information filtering systems, which are effective, scalable, and easily interpretable by humans. We focus on two prominent types of user traces in social networks: on the one hand, user comments accompanied by unary preferences such as likes, and on the other hand, user reviews accompanied by numerical preferences such as star ratings. In both cases, we advocate that by better understanding user text through mining its semantics and modeling its structure, we can not only improve information filtering, but also explain predictions to users. Within this context, we aim to answer three main research questions, namely: (i)~how do item semantics help to predict unary preferences; (ii)~how do sentiments of free-form user texts help to predict unary preferences; and (iii)~how to model fine-grained numerical preferences from user review texts. Our goal is to model and extract from user text the knowledge required to answer these questions, and to obtain insights on how to design better information filtering systems that are more effective and improve user experience. To answer the first question, we formulate the recommendation problem based on unary preferences as a top-N retrieval task and we define an appropriate dataset and metrics for measuring performance. Then, we propose and evaluate several content-based methods based on semantic similarities under presence or absence of preferences. To answer the second question, we propose a sentiment-aware neighborhood model which integrates the sentiment of user comments with unary preferences, either through fixed or through learned mapping functions. For the latter type, we propose a learning algorithm which adapts the sentiment of user comments to unary preferences at collective or individual levels. To answer the third question, we cast the problem of modeling user attitude toward aspects of items as a weakly supervised problem, and we propose a weighted multiple-instance learning method for solving it. Lastly, we show that the learned saliency weights, apart from being easily interpretable, are useful indicators for review segmentation and summarization

Unleashing the Potential of Argument Mining for IS Research: A Systematic Review and Research Agenda

Argument mining (AM) represents the unique use of natural language processing (NLP) techniques to extract arguments from unstructured data automatically. Despite expanding on commonly used NLP techniques, such as sentiment analysis, AM has hardly been applied in information systems (IS) research yet. Consequentially, knowledge about the potentials for the usage of AM on IS use cases appears to be still limited. First, we introduce AM and its current usage in fields beyond IS. To address this research gap, we conducted a systematic literature review on IS literature to identify IS use cases that can potentially be extended with AM. We develop eleven text-based IS research topics that provide structure and context to the use cases and their AM potentials. Finally, we formulate a novel research agenda to guide both researchers and practitioners to design, compare and evaluate the use of AM for text-based applications and research streams in IS