The present is marked by the influence of the Social Web on societies and people worldwide. In this context, users generate large amounts of data, especially containing opinion, which has been proven useful for many real-world applications. In order to extract knowledge from user-generated content, automatic methods must be developed. In this paper, we present different approaches to multi-document summarization of opinion from blogs and reviews. We apply these approaches to: (a) identify positive and negative opinions in blog threads in order to produce a list of arguments in favor and against a given topic and (b) summarize the opinion expressed in reviews. Subsequently, we evaluate the proposed methods on two distinct datasets and analyze the quality of the obtained results, as well as discuss the errors produced. Although much remains to be done, the approaches we propose obtain encouraging results and point to clear directions in which further improvements can be made.
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The degree of importance of each ‘latent’ topic is given by the singular values and the optimal number of latent topics (i.e., dimensions) r can be fine-tuned on training data.
The annotation described was done by two Computer Science graduates. The task they were given was to annotate the sentences that were found in the summaries obtained and that were not found in the Gold Standard, in terms of whether they are relevant to the topic of the post and in terms of whether they express a positive, negative or no opinion.The results presented are for the cases in which they agreed. In case of disagreement, the sentence was simply considered as incorrectly included in the final summary.
The motivation for not considering negation in our system is based on two aspects: 1) The paper “A Survey on the Role of Negation in Sentiment Analysis” by Wiegand et al. (2010) describes the various manners in which negation has been considered in sentiment analysis. One of the important conclusions is that negation, unless used within a method for sentiment analysis that uses syntactic analysis (i.e. which is able to precisely detect the scope of the negation), brings negligible or no improvement over the more simple approach, which does not consider negation. 2) We are using this simple approach in the view of multilinguality. In this context, given the positioning of negation in the different languages and the manner in which it should be considered for each of the languages involved, we must omit to consider negation for the time being (until a thorough analysis is done on how negation should be considered correctly, for various languages).
As far as the word sense disambiguation (WSD) is concerned, work by Akkaya et al. (2009) has shown that WSD, in the sense in which it is understood by the research community (i.e. to assign a WordNet synset to each word) is not useful for subjectivity analysis. Instead, the authors propose “Subjectivity WSD”, by which they aim solely at discriminating among subjective and objective usage of the word synsets. Even so, performing WSD (in the traditional sense) has not been a priority to the sentiment analysis community. The only step that has been proven useful is the shallow disambiguation using part of speech information (Wiegand and Klakow 2010).
Although there are many shortcomings to ROUGE, it remains the only automatic method to measure the performance of summarization systems. While perhaps a more linguistic-quality based evaluation would be more informative, it is more costly and subjective and at this time there is no common agreement on alternative evaluation metrics.
We used F1 score instead of recall used at TAC, because the lengths of our model summaries and system summaries are different, that is, model summaries can be longer than the system produced summaries.
Certainly, in order to use gold polarity alongside the score produced by the sentiment analysis tool as we do, we had to firstly automatically align all the automatically identified sentences with the annotated comments.
We note, however, that the results on our corpus are not directly comparable with those of TAC08, since the data sets are different and the tasks involved are significantly distinct.
Blog posts in our corpus were annotated as important with respect to the main topic of the respective blog threads.
The Medical Subject Headings (MeSH) thesaurus is prepared by the US National Library of Medicine for indexing, cataloguing, and searching for biomedical and health-related information and documents. Although it was initially meant for biomedical and health-related documents, since it represent a large IS-A taxonomy, it can be used in more general tasks (http://www.nlm.nih.gov/mesh/meshhome.html).
Europe Media Monitor (EMM) (Steinberger et al. 2009c) and the related text mining tools have been entirely developed at the European Commission’s Joint Research Centre (JRC). Unfortunately, the tools cannot be currently made publicly available, but the end product (the news analysis output) is freely accessible at http://emm.newsbrief.eu/overview.html. The tools used for the experiments described here i.e., named entity recognition (NER) (Steinberger and Pouliquen 2009) and disambiguation (Pouliquen et al. 2006) can in principle be replaced by any other tools. For a detailed description of how these tools were built and the manner in which they function, please see Pouliquen et al. (2006) and Pouliquen and Steinberger (2009).
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Balahur, A., Kabadjov, M., Steinberger, J. et al. Challenges and solutions in the opinion summarization of user-generated content. J Intell Inf Syst 39, 375–398 (2012). https://doi.org/10.1007/s10844-011-0194-z