UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification (original) (raw)
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Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment Analysis
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), 2018
Aspect-based Sentiment Analysis is a finegrained task of sentiment classification for multiple aspects in a sentence. Present neuralbased models exploit aspect and its contextual information in the sentence but largely ignore the inter-aspect dependencies. In this paper, we incorporate this pattern by simultaneous classification of all aspects in a sentence along with temporal dependency processing of their corresponding sentence representations using recurrent networks. Results on the benchmark SemEval 2014 dataset suggest the effectiveness of our proposed approach.
Language-Agnostic Model for Aspect-Based Sentiment Analysis
Proceedings of the 13th International Conference on Computational Semantics - Long Papers
In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis. The proposed approach is based on Bidirectional Long Short-Term Memory (Bi-LSTM) network, which is further assisted with extra hand-crafted features. We define three different architectures for the successful combination of word embeddings and hand-crafted features. We evaluate the proposed approach for six languages (i.e. English, Spanish, French, Dutch, German and Hindi) and two problems (i.e. aspect term extraction and aspect sentiment classification). Experiments show that the proposed model attains state-of-the-art performance in most of the settings.
The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering , as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.
Proceedings of the AAAI Conference on Artificial Intelligence
Aspect-level sentiment classification (ALSC) aims at predicting the sentiment polarity of a specific aspect term occurring in a sentence. This task requires learning a representation by aggregating the relevant contextual features concerning the aspect term. Existing methods cannot sufficiently leverage the syntactic structure of the sentence, and hence are difficult to distinguish different sentiments for multiple aspects in a sentence. We perceive the limitations of the previous methods and propose a hypothesis about finding crucial contextual information with the help of syntactic structure. For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. Empirical studies show that our model outperforms recent models on most of the benchmark datasets for the ALSC task. The results suggest that our method for incorporating syntactic structure enriches the represen...
Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks
Social, Cultural, and Behavioral Modeling, 2018
Aspect-level sentiment classification aims to identify the sentiment expressed towards some aspects given context sentences. In this paper, we introduce an attention-over-attention (AOA) neural network for aspect level sentiment classification. Our approach models aspects and sentences in a joint way and explicitly captures the interaction between aspects and context sentences. With the AOA module, our model jointly learns the representations for aspects and sentences, and automatically focuses on the important parts in sentences. Our experiments on laptop and restaurant datasets demonstrate our approach outperforms previous LSTM-based architectures.
A Deep Analysis on Aspect based Sentiment Text Classification Approaches
International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE) , 2019
Now-a-days, people often express their opinions as reviews, comments, feedback in various social networking sites, business organizations. Feedbacks that are given by the end users have a great impact for the evolution of new version of product or service. For business invested in customers, analyzing each piece of feedback by hand can be overwhelming and similarly for an organization to rate an employee regarding his/her performance based on usual quantitative feedback system is a challenging task. Sentiment analysis, developed within this context can be helpful to solve such issues at early stage and provide guidance in improving their sales and productivity. Moreover, reviews written in natural language are mostly unstructured and needs huge time for processing. As the data is available in large size, it's impossible to process and analyze the information manually. In order to solve this issue, many machine Learning techniques and Deep Learning models are being proposed for automatic learning, extraction and analysis. As the technology advances businesses, organizations, social media and e-commerce sites can benefit from these in-depth insights and customer satisfaction can be analyzed. Sentiment analysis is an excellent source to perform fine-grained analysis like feature-based sentiment analysis and it can be used to identify different aspects expressed at either document or sentence level. This paper highlights the insights of extracting the most important aspects from the opinions expressed in the input text using various machine learning techniques.
SemEval-2015 Task 12: Aspect Based Sentiment Analysis
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015
This paper describes the SemEval 2016 shared task on Aspect Based Sentiment Analysis (ABSA), a continuation of the respective tasks of 2014 and 2015. In its third year, the task provided 19 training and 20 testing datasets for 8 languages and 7 domains, as well as a common evaluation procedure. From these datasets, 25 were for sentence-level and 14 for text-level ABSA; the latter was introduced for the first time as a subtask in SemEval. The task attracted 245 submissions from 29 teams.
A Survey on Aspect-Based Sentiment Classification
ACM Computing Surveys, 2023
With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.
Survey on Aspect Detection for Aspect-Based Sentiment Analysis
Artificial Intelligence Review, 2023
Sentiment analysis is an important tool to automatically understand the user-generated content on the Web. The most fine-grained sentiment analysis is concerned with the extraction and sentiment classification of aspects and has been extensively studied in recent years. In this work, we provide an overview of the first step in aspect-based sentiment analysis that assumes the extraction of opinion targets or aspects. We define a taxonomy for the extraction of aspects and present the most relevant works accordingly, with a focus on the most recent state-of-the-art methods. The three main classes we use to classify the methods designed for the detection of aspects are pattern-based, machine learning, and deep learning methods. Despite their differences, only a small number of works belong to a unique class of methods. All the introduced methods are ranked in terms of effectiveness. In the end, we highlight the main ideas that have led the research on this topic. Regarding future work, we deemed that the most promising research directions are the domain flexibility and the end-to-end approaches.
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), 2016
This paper describes our system used in the Aspect Based Sentiment Analysis (ABSA) task of SemEval 2016. Our system uses Maximum Entropy classifier for the aspect category detection and for the sentiment polarity task. Conditional Random Fields (CRF) are used for opinion target extraction. We achieve state-of-the-art results in 9 experiments among the constrained systems and in 2 experiments among the unconstrained systems.
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