Distributed Agent-Based Retrieval Tools: Proceedings of the 1st International Workshop

element, and the proper content within a

element. Post in Spanish instead are enclosed in a

element. Comments are enclosed in a

to enclose comments. For these most used content generators, we created a list of elements to be included. We exploited the features of the customizable HTML reader in IXE [6], which allows providing a list of elements, element classes or element ids to skip or to include during indexing. For instance, using these parameters in IXE conﬁguration ﬁle: IncludeElement Blogger div.post div.comments IncludeElement WordPress* div.post ol#commentlist we direct IXE to limit indexing to div elements with class name post or comments for pages generated by Blogger or div elements with class name post or ol elements with id commentlist generated from any version of WordPress. The third strategy is used to handle the remaining cases, excluding elements which are considered not part of the post. For example: ExcludeElement div.*link* div.side* div.*bar ExcludeElement div#header div#nav* excludes from indexing any div whose class name contains ‘link’, starts with ‘side’ or ends with ‘bar’ as well as any div whose id name is ‘header’ or starts with ‘nav’. Fortunately enough, many content generator do indeed use markup of this kind, so that with a list of about 50 element to exclude, we avoid most of the irrelevant parts. Another problem is the presence of spam blogs, also called splogs, i.e. fake blog pages which contain advertising or other irrelevant content used just to promote aﬃliated sites, which are often disreputable. We detected for instance in the collection a large number of splogs from the domain blogspot.com, which hosts a free blog posting service by Google. To avoid splogs, we used a black list of URLs from A. Soro, G. Armano and G. Paddeu (eds.) Distributed Agent-Based Retrieval Tools c 2006 Polimetrica International Scientiﬁc Publisher Monza/Italy

Extracting Dependency Relations for Opinion Mining

177

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http://www.splogspot.com/. Any blog from that list is assigned a page rank of 0 during indexing, so that it will not normally appear in search results. Finally, pages are written in several languages, but only the English blogs are considered relevant according to TREC blog track guidelines. RSS includes a metadata ﬁeld for language. However it is not used consistently and hence it is quite inaccurate. One could apply a language detector to identify the language, but in the case of blog posts, which are often quite short, also this method is not suﬃciently accurate. For helping ﬁnding opinionated blogs, we enriched the index with tags for words, i.e. the index does not contain only words but also an overlay of tags for each word. One tag is the OPINIONATED tag, which is associated to subjective words considered to be carrying an opinion bias. We tagged as opinionated a subset of the list of words SentiWordNet [9]. SentiWordNet was created from WordNet, starting from two seed sets of positive and negative terms, expanded by means of synonyms, antonyms and other semantic relations. Subjective terms were then represented as feature vectors consisting of terms in their description and glosses and used to train a statistical classiﬁer. All words in WordNet were classiﬁed, producing the list SentiWordNet, consisting in 115,341 words marked with positive and negative orientation scores ranging from 0 to 1. We extracted from SentiWordNet a subset of 8427 opinionated words, by selecting those whose orientation strength is above a threshold of 0.4. The overlay allows performing proximity searches of the type: content matches proximity 6 [OPINIONATED:* ’George Bush’] which will return all documents where any (i.e. *) opinionated word occurs within 6 terms from the phrase George Bush. We plan to reﬁne the approach by exploiting an English parser, in order to detect whether the opinionated term refers indeed to Bush, rather than to another entity in the same sentence.

6.2

Results

We performed a few experiments using the TREC 2006 Blog topics number 851 to 900. These topic range from controversial or beloved political ﬁgures (e.g. Abramoﬀ, Bush, Ann Coulter), to performers (Jon A. Soro, G. Armano and G. Paddeu (eds.) Distributed Agent-Based Retrieval Tools c 2006 Polimetrica International Scientiﬁc Publisher Monza/Italy

178

G. Attardi, M. Simi

The electronic edition of this book is not sold and is made available in free access. Every contribution is published according to the terms of “Polimetrica License B”. “Polimetrica License B” gives anyone the possibility to distribute the contents of the work, provided that the authors of the work and the publisher are always recognised and mentioned. It does not allow use of the contents of the work for commercial purposes or for profit. Polimetrica Publisher has the exclusive right to publish and sell the contents of the work in paper and electronic format and by any other means of publication. Additional rights on the contents of the work are the author’s property.

Steward), to movies or tv shows (March of the Penguins, Arrested Development, Life on Mars, Oprah Winfrey), to products (MacBook pro, Blackberry, Shimano) to political subjects (nuclear power, jihad). Each topic consists in a title, plus a description and a narrative. For example, topic 951 is as follows:

"March of the Penguins" <desc>Provide opinion of the film documentary "March of the Penguins". <narr>Relevant documents should include opinions concerning the film documentary "March of the Penguins". Articles or comments about penguins outside the context of this film documentary are not relevant. We performed a baseline run with queries made just from title words joined in AND. A second run used the same words but added a proximity operator with distance 6 to an opinionated word. The third run used an AND combination of title words plus an OR of description words. For the fourth run we used queries made from title words within proximity 6 from opinionated words plus an OR of description words. We performed an informal judgment of the results of these runs and obtained the following estimates for average precision@5, i.e. the precision of the ﬁrst ﬁve results averaged on all topics:<br /> <br /> run title title + proximity title + description title + proximity + description<br /> <br /> relevant results 573 483 537 450<br /> <br /> precision@5 0.100 0.275 0.250 0.637<br /> <br /> Results are quite preliminary, but they seem to indicate that the opinionated words analysis provides a signiﬁcant improvement on both title and title+description queries. A. Soro, G. Armano and G. Paddeu (eds.) Distributed Agent-Based Retrieval Tools c 2006 Polimetrica International Scientiﬁc Publisher Monza/Italy<br /> <br /> Extracting Dependency Relations for Opinion Mining<br /> <br /> 7<br /> <br /> 179<br /> <br /> Intent Retrieval<br /> <br /> The electronic edition of this book is not sold and is made available in free access. Every contribution is published according to the terms of “Polimetrica License B”. “Polimetrica License B” gives anyone the possibility to distribute the contents of the work, provided that the authors of the work and the publisher are always recognised and mentioned. It does not allow use of the contents of the work for commercial purposes or for profit. Polimetrica Publisher has the exclusive right to publish and sell the contents of the work in paper and electronic format and by any other means of publication. Additional rights on the contents of the work are the author’s property.<br /> <br /> The Intent Classiﬁer will be used as part of an intent retrieval engine, which uses a specialized Passage Retrieval system in order to retrieve candidate sentences about a target. The Passage Retrieval system [5] supports keyword searches based on a traditional inverted word-document index as well as searches for opinionated words as described above, but returns passages rather than documents where the keywords occur. The Passage Retrieval index also contains annotations about Named Entitites and can be queried specifying the search for a term representing a named entity. This allows distinguishing for instance the term ‘Apple’ used to mean an organization rather than a fruit. Each returned candidate result sentence is given a rank computed by a combination of a classical IR similarity metric (PL2, a variant of the well known BM25) and a distance metric on the target proximity. Since at the moment the system does not perform anaphora resolution, the target may not occur in a candidate sentence, but be just referred through a pronoun or implicitly. We handle anaphora in a crude way by using a score on the distance between the candidate sentence and one where the target occurs. The opinion mining task is performed by retrieving sentences from the Passage Retrieval that contain the given target tagged as an entity in proximity to an opinionated word. Each retrieved sentence is parsed and a set of sub-patterns extracted as features and used to classify the sentence. The result consists in a list of sentences with associated notes about the presence of opinions about the given target and their polarity.<br /> <br /> 8<br /> <br /> Conclusions<br /> <br /> We have presented the design of an opinion retrieval engine which extracts candidate sentences containing an opinion using a specialized search index which maintains annotations on words denoting whether the word is opinionated or whether it is part of a Named Entity. Each candidate sentence is then passed to an Intent Classiﬁer to assess whether it indeed contains an opinion on the requested subject. We propose to build an Intent Classiﬁer which uses relations extracted from a dependency parse tree of a sentence to compute features for classiﬁcation. We sketched the technology of dependency parsing A. Soro, G. Armano and G. Paddeu (eds.) Distributed Agent-Based Retrieval Tools c 2006 Polimetrica International Scientiﬁc Publisher Monza/Italy<br /> <br /> 180<br /> <br /> G. Attardi, M. Simi<br /> <br /> The electronic edition of this book is not sold and is made available in free access. Every contribution is published according to the terms of “Polimetrica License B”. “Polimetrica License B” gives anyone the possibility to distribute the contents of the work, provided that the authors of the work and the publisher are always recognised and mentioned. It does not allow use of the contents of the work for commercial purposes or for profit. Polimetrica Publisher has the exclusive right to publish and sell the contents of the work in paper and electronic format and by any other means of publication. Additional rights on the contents of the work are the author’s property.<br /> <br /> and a multi-language parser whose performance is suitable for large scale deployment. We presented the opinion search engine which we built for the TREC 2006 Blog Task and which can be used as searching component of the opinion retrieval system. Preliminary results from experiments are promising, showing improvements in precision when exploiting annotations on opinionated words. Possible enhancements to the system include incorporating an anaphora resolution mechanism, so that each sentence will be indexed also with the entity referred by the anaphora. Also it will be worthwhile to use multiple classiﬁers, one for each intent, so that the retrieval system could mark each result with a list of the expressed intents, or alternatively one could retrieve documents with a given intent. It will also be worth considering moving the intent classiﬁcation at indexing time and recording the outcome as metadata associated to each sentence. The beneﬁts would a reduction of the search time, while the diﬃculty would be to identify all possible targets present or referred in each sentence.<br /> <br /> Acknowledgements We thank Andrea Esuli and Fabrizio Sebastiani for making available to us the list of opinionated words they produced from WordNet.<br /> <br /> Bibliography [1] K. Abe, S. Kawasoe, T. Asai, H. Arimura, S. Arikawa. [2002] Optimized substructure discovery for semi-structured data. Proc. of 6th PKDD, pp.1-14. [2] A. Aue, M. Gamon. [2005] Customizing Sentiment Classiﬁers to New Domains: a Case Study. In RANLP-05, the International Conference on Recent Advances in Natural Language Processing. [3] G. Attardi [2006] Experiments with a Multilanguage non-projective dependency parser. In Proc. of the Tenth CoNLL. [4] G. Attardi, S. Di Marco, D. Salvi [1998] Categorisation by context. Journal of Universal Computer Science , 4(9),719-736. A. Soro, G. Armano and G. Paddeu (eds.) Distributed Agent-Based Retrieval Tools c 2006 Polimetrica International Scientiﬁc Publisher Monza/Italy<br /> <br /> Extracting Dependency Relations for Opinion Mining<br /> <br /> 181<br /> <br /> The electronic edition of this book is not sold and is made available in free access. Every contribution is published according to the terms of “Polimetrica License B”. “Polimetrica License B” gives anyone the possibility to distribute the contents of the work, provided that the authors of the work and the publisher are always recognised and mentioned. It does not allow use of the contents of the work for commercial purposes or for profit. Polimetrica Publisher has the exclusive right to publish and sell the contents of the work in paper and electronic format and by any other means of publication. Additional rights on the contents of the work are the author’s property.<br /> <br /> [5] G. Attardi, A. Cisternino, F. Formica, M. Simi, A. Tommasi, C. Zavattari [2001] PIQASso: PIsa Question Answering System Proceedings of Text Retrieval Conference (Trec-10), 599-607, NIST, Gaithersburg (MD), November 13-16. [6] G. Attardi, A. Cisternino [2001] Template Metaprogramming an Object Interface to Relational Tables, Reﬂection 2001, LNCS 2192, 266-267, Springer-Verlag, Berlin. [7] S. Buchholz, et al. [2006] CoNLL-X Shared Task on Multilingual Dependency Parsing. In Proc. of the Tenth CoNLL. [8] S. Dumais, J. Platt, D. Heckerman and M. Sahami [1998] Inductive learning algorithms and representations for text categorization. In Proceedings of CIKM, 148–155. [9] A. Esuli, F. Sebastiani [2005] Determining the semantic orientation of terms through gloss classiﬁcation. CIKM 2005: 617-624. [10] S. Matsumoto, H. Takamura, M. Okumura [2005] Sentiment Classiﬁcation Using Word Sub-sequences and Dependency Sub-trees. In: Ho, Tu Bao, Cheung, David, & Li, Huan (eds), Proceeding of PAKDD’05, the 9th Paciﬁc-Asia Conference on Advances in Knowledge Discovery and Data Mining. LNCS, vol. 3518. [11] H. Yamada, Y. Matsumoto [2003] Statistical Dependency Analysis with Support Vector Machines. In Proc. IWPT.<br /> <br /> A. Soro, G. Armano and G. Paddeu (eds.) 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