Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.
A comprehensive review on argumentation corpora is beyond the scope of this paper; good overviews can be found in e.g. @cite_18 @cite_15 @cite_5 . Here we only review argumentation corpora constructed from customer reviews. @cite_2 built the corpus, consisting of 2.1k hotel reviews posted on Tripadvisor.com. Instead of directly labelling arguments, they annotate and sentiments polarities. A statement is ''. They designed a rule-based tool to segment statements, and employed crowdsourcing to annotate the sentiment and (e.g. location, services, facilities) in each statement. Results suggested that crowdsourcing workers can reliably identify the sentiments of statements (approval rate 72.8 (rejection rate 43.3 view ArguAna as an intermediate resource for building argumentation corpora, because each argument usually contains several statements (e.g. one positive negative statement serving as the claim, and several neutral statements serving as premises). Thus, ArguAna cannot be directly used for training argumentation mining techniques.
@cite_5 @cite_18 @cite_2 @cite_15
1911240979 774598698 2019207508 2395678543
The analysis of user reviews has become critical in research and industry, as user reviews increasingly impact the reputation of products and services. Many review texts comprise an involved argumentation with facts and opinions on different product features or aspects. Therefore, classifying sentiment polarity does not suffice to capture a review's impact. We claim that an argumentation analysis is needed, including opinion summarization, sentiment score prediction, and others. Since existing language resources to drive such research are missing, we have designed the ArguAna TripAdvisor corpus, which compiles 2,100 manually annotated hotel reviews balanced with respect to the reviews' sentiment scores. Each review text is segmented into facts, positive, and negative opinions, while all hotel aspects and amenities are marked. In this paper, we present the design and a first study of the corpus. We reveal patterns of local sentiment that correlate with sentiment scores, thereby defining a promising starting point for an effective argumentation analysis. Scarcity of annotated corpora for many languages is a bottleneck for training finegrained sentiment analysis models that can tag aspects and subjective phrases. We propose to exploit statistical machine translation to alleviate the need for training data by projecting annotated data in a source language to a target language such that a supervised fine-grained sentiment analysis system can be trained. To avoid a negative influence of poor-quality translations, we propose a filtering approach based on machine translation quality estimation measures to select only high-quality sentence pairs for projection. We evaluate on the language pair German English on a corpus of product reviews annotated for both languages and compare to in-target-language training. Projection without any filtering leads to 23 F1 in the task of detecting aspect phrases, compared to 41 F1 for in-target-language training. Our approach obtains up to 47 F1. Further, we show that the detection of subjective phrases is competitive to in-target-language training without filtering. Mining detailed opinions buried in the vast amount of review text data is an important, yet quite challenging task with widespread applications in multiple domains. Latent Aspect Rating Analysis (LARA) refers to the task of inferring both opinion ratings on topical aspects (e.g., location, service of a hotel) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings. A major limitation of previous work on LARA is the assumption of pre-specified aspects by keywords. However, the aspect information is not always available, and it may be difficult to pre-define appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews. In this paper, we propose a unified generative model for LARA, which does not need pre-specified aspect keywords and simultaneously mines 1) latent topical aspects, 2) ratings on each identified aspect, and 3) weights placed on different aspects by a reviewer. Experiment results on two different review data sets demonstrate that the proposed model can effectively perform the Latent Aspect Rating Analysis task without the supervision of aspect keywords. Because of its generality, the proposed model can be applied to explore all kinds of opinionated text data containing overall sentiment judgments and support a wide range of interesting application tasks, such as aspect-based opinion summarization, personalized entity ranking and recommendation, and reviewer behavior analysis. In this paper, we argue that an annotation scheme for argumentation mining is a function of the task requirements and the corpus properties. There is no one-size-fits-all argumentation theory to be applied to realistic data on the Web. In two annotation studies, we experiment with 80 German newspaper editorials from the Web and about one thousand English documents from forums, comments, and blogs. Our example topics are taken from the educational domain. To formalize the problem of annotating arguments, in the first case, we apply a Claim-Premise scheme, and in the second case, we modify Toulmin's scheme. We find that the choice of the argument components to be annotated strongly depends on the register, the length of the document, and inherently on the literary devices and structures used for expressing argumentation. We hope that these findings will facilitate the creation of reliably annotated argumentation corpora for a wide range of tasks and corpus types and will help to bridge the gap between argumentation theories and actual application needs.