Replication issues in syntax-based aspect extraction for opinion mining

Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.Comment: Accepted in the EACL 2017 SR

Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from "Waiters are very friendly and the pasta is simply average" could be ('Waiters', positive, 'friendly'). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.Comment: This paper is accepted in AAAI 202

Entitásorientált véleménykinyerés magyar nyelven

Napjainkban a digitális formában fellelhető, strukturálatlan adatok mennyisége folyamatosan növekszik, ezáltal a bennük említett entitásokra vonatkozó vélemények polaritásának automatizált elemzése is egyre fontosabbá válik. Cikkünkben bemutatunk egy olyan alkalmazást, mely segítségével magyar nyelvű szövegekből lehetséges a tulajdon-, földrajzi- és cégnevekre vonatkozó, részletes szerzői attitűd kinyerése. A forráskódot és a megoldást virtualizált formában is nyilvánosságra hoztuk

Multitask Aspect_Based Sentiment Analysis with Integrated Bidirectional LSTM & CNN Model

International audienceSentiment analysis or opinion mining used to understand the community's opinions on a particular product. Sentiment analysis involves building the opinion collection and classification system. Aspect-based sentiment analysis focuses on the ability to extract and summarize opinions on specific aspects of entities within sentiment document. In this paper, we propose a novel supervised learning approach using deep learning techniques for multitask aspect-based opinion mining system that support four main subtasks: extract opinion target, classify aspect-entity (category), and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of entity. Using extra POS layer to identify morphological features of words combines with stacking architecture of BiLSTM and CNN with word embeddings achieved by training GloVe on Restaurant domain reviews of the SemEval 2016 benchmark dataset in our proposed method is aimed at increasing the accuracy of the model. Experimental results showed that our multitask aspect-based sentiment analysis model has extracted and classified main above subtasks concurrently and achieved significantly better accuracy than the state-of-the-art methods

Toward a multitask aspect-based sentiment analysis model using deep learning

International audienceSentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches

Journalistic transparency using CRFs to identify the reporter of newspaper articles in Spanish

Journalistic transparency rises as a key issue against the lack of credibility to which journalists are exposed, as well as the media manipulators and fake news providers. With the use of Natural Language Processing (NLP) and Machine Learning (ML), it is possible to automate the extraction of information from newspaper articles to know what the sources of information are to verify their veracity. Along with this article, we present the application of Conditional Random Fields (CRFs) for a specific type of Entity Recognition (ER) task, namely, to identify what we have called the “reporter” in newspaper articles, i.e., who or what is the provider of the information. Thus, we have created a labelled corpus for the Spanish language and trained and analysed several CRFs models with a set of specific features. The obtained results suppose a solid baseline for our goal.This research work has been co-funded by Display Connectors S.L. through the project entitled \Identi- fying relevant entities in newspaper articles"(in Spanish \Identi caci on de entidades relevantes en noticias period sticas"), and by the Madrid Regional Government through the project e-Madrid-CM (P2018/TCS- 4307). The e-Madrid-CM project is also co- nanced by the Structural Funds (FSE and FEDER). Also, we give special thanks to the people from the P ublico online newspaper for their work and support