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

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

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Weakly supervised aspect extraction for domain-specific texts

Aspect extraction, identifying aspects of text segments from a pre-defined set of aspects, is one of the keystones in text understanding. It benefits numerous applications, including sentiment analysis and product review summarization. Most existing aspect extraction methods heavily rely on human-curated aspect annotations of massive text segments, thus making them expensive to be applied in specific domains. Recent attempts leveraging clustering methods can alleviate such annotation effort, but they require domain-specific knowledge and effort to further filter, aggregate, and align the clustering results to desired aspects. Therefore, in this paper, we explore to extract aspects from the domain-specific raw texts with very limited supervision – only a few user-provided seed words per each aspect. Specifically, our proposed neural model is equipped with multi-head attention and self-training. The multi-head attention is learned from the seed words to ensure that the aspect-related words in text segments are weighted higher than those unrelated ones. The self-training mechanism provides more pseudo labels in addition to limited supervision. Extensive experiments on real-world datasets demonstrate the superior performance of our proposed framework, as well as the effectiveness of both the attention module and the self-training mechanism. Case studies on the attention weights further shed lights on the interpretability of our aspect extraction results

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

The essential use of natural language processing is to analyze the sentiment of the author via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying emotion in the context. It has been used in several subject areas such as stock market prediction, social media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc. Many researchers have worked on these areas and have produced significant results. These outcomes are beneficial in their respective fields, as they help to understand the overall summary in a short time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of the essential studies done so far and to provide an overview of SA models in the area of emotion AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context. Furthermore, this work also discusses di erent neural network-based approaches for analyzing sentiment. Finally, these di erent approaches were also analyzed with sample data collected from Twitter. Among the four approaches considered in each domain, the aspect-based ontology method produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85% accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved 90% accuracy among the other machine learning-based approaches.Ministerio de Educación (MOE) en Taiwán N/

Identifying sources of opinions with conditional random fields and extraction patterns

Journal ArticleRecent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a). While CRFs model source identification as a sequence tagging task, AutoSlog learns extraction patterns. Our results show that the combination of these two methods performs better than either one alone. The resulting system identifies opinion sources with 79:3% precision and 59:5% recall using a head noun matching measure, and 81:2% precision and 60:6% recall using an overlap measure