Lillian Lee
A general-audience introduction to the area of "sentiment analysis", the computational treatment of subjective, opinion-oriented language (an example application is determining whether a review is "thumbs up" or "thumbs down"). Some challenges, applications to business-intelligence tasks, and potential future directions are described.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
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Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment analysis due to no opinion term labeling needed, but it fails to interpret why a sentiment polarity is derived for the aspect. To address this problem, recent works fine-tune pre-trained Transformer encoders for ALSC to extract an aspect-centric dependency tree that can locate the opinion words. However, the induced opinion words only provide an intuitive cue far below human-level interpretability. Besides, the pre-tr…
Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA). However, some triplets extracted by ASTE are confusing, since the sentiment in a triplet extracted by ASTE is the sentiment that the sentence expresses toward the aspect term rather than the sentiment of the aspect term and opinion term pair. In this paper, we introduce a more fine-grained Aspect-Sentiment-Opinion Triplet Extraction (ASOTE) Task. ASOTE also extracts aspect term, sentiment …
Aspect based sentiment analysis, predicting sentiment polarity of given aspects, has drawn extensive attention. Previous attention-based models emphasize using aspect semantics to help extract opinion features for classification. However, these works are either not able to capture opinion spans as a whole, or not able to capture variable-length opinion spans. In this paper, we present a neat and effective structured attention model by aggregating multiple linear-chain CRFs. Such a design allows the model to extract aspect-specific opinion spans and then evaluate sentiment polarity by exploitin…
Aspect-level sentiment classification (ALSC) and aspect oriented opinion words extraction (AOWE) are two highly relevant aspect-based sentiment analysis (ABSA) subtasks. They respectively aim to detect the sentiment polarity and extract the corresponding opinion words toward a given aspect in a sentence. Previous works separate them and focus on one of them by training neural models on small-scale labeled data, while neglecting the connections between them. In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE t…
Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, (1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and …
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis
本文提出实体-方面-观点-情感四元组抽取任务EASQE，将方面词分层分解为实体和方面以避免信息丢失，并构建四个数据集及两阶段序列标注Trigger-Opinion基线框架，实验表明其显著优于现有方法。
Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence. Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations. The attention is expected to concentrate on opinion words for accurate sentiment prediction. However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects. In this paper, we propose an alternative hard-selection approach, which determines…
Multi-domain Multilingual Sentiment Analysis in Industry: Predicting Aspect-based Opinion Quadruples
本文探讨使用大语言模型进行基于方面的多领域多语言四元组意见提取，证明单一微调多领域模型性能可媲美专门模型且降低复杂性，并分享了结构化预测任务中的实践经验。
Sentiment analysis can be regarded as a relation extraction problem in which the sentiment of some opinion holder towards a certain aspect of a product, theme or event needs to be extracted. We present a novel neural architecture for sentiment analysis as a relation extraction problem that addresses this problem by dividing it into three subtasks: i) identification of aspect and opinion terms, ii) labeling of opinion terms with a sentiment, and iii) extraction of relations between opinion terms and aspect terms. For each subtask, we propose a neural network based component and combine all of t…
Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of data for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of Sen…
Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising …
The Internet contains a wealth of public opinion on food safety, including views on food adulteration, food-borne diseases, agricultural pollution, irregular food distribution, and food production issues. In order to systematically collect and analyse public opinion on food safety, we developed IFoodCloud, a platform for the real-time sentiment analysis of public opinion on food safety in China. It collects data from more than 3,100 public sources that can be used to explore public opinion trends, public sentiment, and regional attention differences of food safety incidents. At the same time, …
Target-based sentiment analysis involves opinion target extraction and target sentiment classification. However, most of the existing works usually studied one of these two sub-tasks alone, which hinders their practical use. This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme. Our framework involves two stacked recurrent neural networks: The upper one predicts the unified tags to produce the final output results of the primary target-based sentiment analysis; The lower …
Recently Convolutional Neural Networks (CNNs) models have proven remarkable results for text classification and sentiment analysis. In this paper, we present our approach on the task of classifying business reviews using word embeddings on a large-scale dataset provided by Yelp: Yelp 2017 challenge dataset. We compare word-based CNN using several pre-trained word embeddings and end-to-end vector representations for text reviews classification. We conduct several experiments to capture the semantic relationship between business reviews and we use deep learning techniques that prove that the obt…
The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms. However, the extraction of aspect-sentiment pairs lacks opinion terms as a reference, while co-extraction of aspect and opinion terms would not lead to meaningful pairs without determining their sentiment dependencies. To address the issue, we present a novel view of ABSA as an opinion triplet extraction task, and propose a multi-task learning framework to jointly extract aspect terms an…
Opinion summarization aims to profile a target by extracting opinions from multiple documents. Most existing work approaches the task in a semi-supervised manner due to the difficulty of obtaining high-quality annotation from thousands of documents. Among them, some use aspect and sentiment analysis as a proxy for identifying opinions. In this work, we propose a new framework, FineSum, which advances this frontier in three aspects: (1) minimal supervision, where only aspect names and a few aspect/sentiment keywords are available; (2) fine-grained opinion analysis, where sentiment analysis dril…
Opinion mining and Sentiment analysis have emerged as a field of study since the widespread of World Wide Web and internet. Opinion refers to extraction of those lines or phrase in the raw and huge data which express an opinion. Sentiment analysis on the other hand identifies the polarity of the opinion being extracted. In this paper we propose the sentiment analysis in collaboration with opinion extraction, summarization, and tracking the records of the students. The paper modifies the existing algorithm in order to obtain the collaborated opinion about the students. The resultant opinion is …
iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples
针对ABSA中含隐式方面与观点的四元组抽取研究不足的问题，提出iACOS方法，通过添加隐式标记、序列标注、多头注意力多标签分类及自适应负样本多任务学习，在两个基准数据集上F1分数显著优于现有基线。
Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction
针对低资源领域缺乏标注数据导致监督方法效果减弱的问题，提出一种简单新颖的无监督方法，用于提取句子中方面词对应的意见词及情感极性，并在四个基准数据集上验证了有效性，为意见词挖掘建立了无监督基准。
OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis
针对现有ABSA基准数据集领域受限、数据粒度不足等问题，本文推出覆盖三个新领域的OATS四元组抽取数据集，并建立初始基线，以助力ABSA的全面研究。
Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks
本文提出了一种针对捷克语的ABSA数据集和翻译对齐方法，通过实验展示了其在低资源语言中的应用效果和改进。
In this paper, we propose a variational approach to unsupervised sentiment analysis. Instead of using ground truth provided by domain experts, we use target-opinion word pairs as a supervision signal. For example, in a document snippet "the room is big," (room, big) is a target-opinion word pair. These word pairs can be extracted by using dependency parsers and simple rules. Our objective function is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment classifier. By introducing a latent variable, i.e., the sentiment polarity, to the objective function…
Opinion Tree Parsing for Aspect-based Sentiment Analysis
针对方面级情感分析中预训练生成模型计算成本高、缺乏情感元素结构显式建模的问题，提出基于上下文无关观点语法的神经图表观点树解析模型，在保证性能的同时大幅提升了推理速度。
Enhancing Investment Opinion Ranking through Argument-Based Sentiment Analysis
研究提出双管齐下的论据挖掘技术，通过目标价与收盘价差异及论据评分排序投资观点，有效提升推荐系统识别高利润潜力观点的能力，并扩展至风险分析。
The classification of opinion texts in positive and negative is becoming a subject of great interest in sentiment analysis. The existence of many labeled opinions motivates the use of statistical and machine-learning methods. First-order statistics have proven to be very limited in this field. The Opinum approach is based on the order of the words without using any syntactic and semantic information. It consists of building one probabilistic model for the positive and another one for the negative opinions. Then the test opinions are compared to both models and a decision and confidence measure…
Opinion Mining and Sentiment Analysis is a process of identifying opinions in large unstructured/structured data and then analysing polarity of those opinions. Opinion mining and sentiment analysis have found vast application in analysing online ratings, analysing product based reviews, e-governance, and managing hostile content over the internet. This paper proposes an algorithm to implement aspect level sentiment analysis. The algorithm takes input from the remarks submitted by various teachers of a student. An aspect tree is formed which has various levels and weights are assigned to each b…
Sentiment analysis and opinion mining on E-commerce site
本文针对电子商务场景下的情感极性分类问题，提出一种广泛的情感对立分类技术，并同时开展句子级和评论级分类实验，为后续情感分析研究提供方向。
A Long Short-Term Memory (LSTM) Model for Business Sentiment Analysis Based on Recurrent Neural Network
本研究针对商业情感分析任务，提出基于改进循环神经网络的LSTM模型以解决梯度消失问题，在产品评论数据集上取得91.33%的准确率，优于传统RNN模型，可助力企业分析客户反馈、评估营销策略。
Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated content such as online reviews. In this work, we introduce GEN-SCL-NAT, which consists of two techniques for improved structured generation for ACOS quadruple extraction. First, we propose GEN-SCL, a supervised contrastive learning objective that aids quadruple prediction by encoura…
With the multitude of companies and organizations abound today, ranking them and choosing one out of the many is a difficult and cumbersome task. Although there are many available metrics that rank companies, there is an inherent need for a generalized metric that takes into account the different aspects that constitute employee opinions of the companies. In this work, we aim to overcome the aforementioned problem by generating aspect-sentiment based embedding for the companies by looking into reliable employee reviews of them. We created a comprehensive dataset of company reviews from the fam…
The amount of opinionated data on the internet is rapidly increasing. More and more people are sharing their ideas and opinions in reviews, discussion forums, microblogs and general social media. As opinions are central in all human activities, sentiment analysis has been applied to gain insights in this type of data. There are proposed several approaches for sentiment classification. The major drawback is the lack of standardized solutions for classification and high-level visualization. In this study, a sentiment analyzer dashboard for online social networking analysis is proposed. This, to …
Relationship extraction and named entity recognition have always been considered as two distinct tasks that require different input data, labels, and models. However, both are essential for structured sentiment analysis. We believe that both tasks can be combined into a single stacked model with the same input data. We performed different experiments to find the best model to extract multiple opinion tuples from a single sentence. The opinion tuples will consist of holders, targets, and expressions. With the opinion tuples, we will be able to extract the relationship we need.
With the rapid growth of social media on the web, emotional polarity computation has become a flourishing frontier in the text mining community. However, it is challenging to understand the latest trends and summarize the state or general opinions about products due to the big diversity and size of social media data and this creates the need of automated and real time opinion extraction and mining. On the other hand, the bulk of current research has been devoted to study the subjective sentences which contain opinion keywords and limited work has been reported for objective statements that imp…
This paper fills a gap in aspect-based sentiment analysis and aims to present a new method for preparing and analysing texts concerning opinion and generating user-friendly descriptive reports in natural language. We present a comprehensive set of techniques derived from Rhetorical Structure Theory and sentiment analysis to extract aspects from textual opinions and then build an abstractive summary of a set of opinions. Moreover, we propose aspect-aspect graphs to evaluate the importance of aspects and to filter out unimportant ones from the summary. Additionally, the paper presents a prototyp…
Unveiling Public Opinion: A Study of Sentiment Analysis Using LSTM and Traditional Models
研究社交媒体时代Twitter的情感分析，评估逻辑回归、随机森林等多种算法，用预处理的Kaggle Twitter数据集确定最优模型，结果显示LSTM方法性能更佳，在捕捉文本方面优于传统机器学习技术。
Analyzing the sentiments of legal opinions available in Legal Opinion Texts can facilitate several use cases such as legal judgement prediction, contradictory statements identification and party-based sentiment analysis. However, the task of developing a legal domain specific sentiment annotator is challenging due to resource constraints such as lack of domain specific labelled data and domain expertise. In this study, we propose novel techniques that can be used to develop a sentiment annotator for the legal domain while minimizing the need for manual annotations of data.
In this paper, we present a process of building a social listening system based on aspect-based sentiment analysis in Vietnamese from creating a dataset to building a real application. Firstly, we create UIT-ViSFD, a Vietnamese Smartphone Feedback Dataset as a new benchmark corpus built based on a strict annotation schemes for evaluating aspect-based sentiment analysis, consisting of 11,122 human-annotated comments for mobile e-commerce, which is freely available for research purposes. We also present a proposed approach based on the Bi-LSTM architecture with the fastText word embeddings for t…
A document which elaborates opinions and arguments related to the previous court cases is known as a legal opinion text. Lawyers and legal officials have to spend considerable effort and time to obtain the required information manually from those documents when dealing with new legal cases. Hence, it provides much convenience to those individuals if there is a way to automate the process of extracting information from legal opinion texts. Party-based sentiment analysis will play a key role in the automation system by identifying opinion values with respect to each legal parties in legal texts.
We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time, we merge extractions from multiple reviews and select the most popular ones. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries…
Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying LDA-based opinion model for product review mining. Tweets are often informal, unstructured and lacking labeled data such as categories and ratings, making it challenging for product opinion mining. In this paper, we propose an LDA-based opinion…
Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad s…
Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight. We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a convolutional neural network supplemented with some heuristic rules.
A Novel Counterfactual Data Augmentation Method for Aspect-Based Sentiment Analysis
针对ABSA任务中观点表达多样性影响模型性能的问题，提出一种基于积分梯度定位掩码、结合极性提示用T5生成反转极性观点的反事实数据增强方法，在三个数据集上效果优于现有增强方法。
As the type and the number of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. In this paper, we investigate the problem of mining opinions on the collection of informal short texts. Both positive and negative sentiment strength of texts are detected. We focus on a non-English language that has few resources for text mining. This approach would help enhance the sentiment analysis in languages where a list of opinionated words does not exist. We propose a new method projects the text into dense and low dimensional feature vecto…
Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Mining opinions expressed in the user generated content is a challenging yet practically very useful problem. This survey would cover various approaches and methodology used in Sentiment Analysis and Opinion Mining in general. The focus would be on Internet text like, Product review, tweets and other social media.
Inconsistent Affective Reaction: Sentiment of Perception and Opinion in Urban Environments
本研究利用街景图像和微博文本数据，通过反应指数和多种分析方法，揭示城市环境中感知与观点情感的不一致性及其影响因素，为城市更新提供策略方向。
Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method
本研究利用LSTM方法对YouTube评论进行情感分类，发现LSTM在处理印尼文文本情感分析时有效，但因数据不平衡导致正向情感识别效果较弱。
The application and usage of opinion mining, especially for business intelligence, product recommendation, targeted marketing etc. have fascinated many research attentions around the globe. Various research efforts attempted to mine opinions from customer reviews at different levels of granularity, including word-, sentence-, and document-level. However, development of a fully automatic opinion mining and sentiment analysis system is still elusive. Though the development of opinion mining and sentiment analysis systems are getting momentum, most of them attempt to perform document-level sentim…
Aspect-oriented Opinion Alignment Network for Aspect-Based Sentiment Classification
针对基于方面的情感分类中观点词与方面语义不匹配问题，提出含邻接跨度增强模块和多视角注意力机制的AOAN模型，在三个基准数据集上取得最优结果。
Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning
本文针对加密货币领域Twitter情感分析多聚焦英语的现状，研究伊朗用户相关观点，采用多种NLP与机器学习/深度学习方法构建分类模型，最终BERT模型以83.50%准确率取得最优效果，可为经济管理提供参考。
In the last couple decades, social network services like Twitter have generated large volumes of data about users and their interests, providing meaningful business intelligence so organizations can better understand and engage their customers. All businesses want to know who is promoting their products, who is complaining about them, and how are these opinions bringing or diminishing value to a company. Companies want to be able to identify their high-value customers and quantify the value each user brings. Many businesses use social media metrics to calculate the user contribution score, whi…
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction
针对现有ASTE模型依赖单词单角色假设和词级交互导致在复杂场景下表现不佳的问题，提出跨度标记与贪心推理方案STAGE，将其转化为多类跨度分类问题，在四个数据集上大幅超越现有最优方法且易于泛化。
Given a target name, which can be a product aspect or entity, identifying its aspect words and opinion words in a given corpus is a fine-grained task in target-based sentiment analysis (TSA). This task is challenging, especially when we have no labeled data and we want to perform it for any given domain. To address it, we propose a general two-stage approach. Stage one extracts/groups the target-related words (call t-words) for a given target. This is relatively easy as we can apply an existing semantics-based learning technique. Stage two separates the aspect and opinion words from the groupe…
An Integrated NPL Approach to Sentiment Analysis in Satisfaction Surveys
本研究提出一种集成 NLP 方法用于满意度调查的情感分析，通过情感极性分类与意见挖掘提取受访者观点及词汇模式，以识别改进领域并支持提升满意度的战略决策。
Sentiment analysis or opinion mining has become an open research domain after proliferation of Internet and Web 2.0 social media. People express their attitudes and opinions on social media including blogs, discussion forums, tweets, etc. and, sentiment analysis concerns about detecting and extracting sentiment or opinion from online text. Sentiment based text classification is different from topical text classification since it involves discrimination based on expressed opinion on a topic. Feature selection is significant for sentiment analysis as the opinionated text may have high dimensions…
Public acceptance and support for renewable energy are important determinants of renewable energy policies and market conditions. This paper examines public sentiment toward solar energy in the United States using data from Twitter, a micro-blogging platform in which people post messages, known as tweets. We filtered tweets specific to solar energy and performed a classification task using Robustly optimized Bidirectional Encoder Representations from Transformers (RoBERTa). Analyzing 71,262 tweets during the period of late January to early July 2020, we find public sentiment varies significant…
Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further…
With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentime…
Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and resear…
Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of prod…
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