Learning Sentiment Based Ranked-Lexicons for Opinion Retrieval

Filipa Peleja, João Magalhães

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


In contrast to classic search where users look for factual information, opinion retrieval aims at finding and ranking subjective information. A major challenge of opinion retrieval is the informal nature of user reviews and the domain specific jargon used to describe the targeted item. In this paper, we present an automatic method to learn a space model for opinion retrieval. Our approach is a generative model that learns sentiment word distributions by embedding multi-level relevance judgments in the estimation of the model parameters. In addition to sentiment word distributions, we also infer domain specific named entities that due to their popularity become a sentiment reference in their domain (e.g. name of a movie, “Batman” or specific hotel items, “carpet”). This contrasts with previous approaches that learn a word’s polarity or aspect-based polarity. Opinion retrieval experiments were done in two large datasets with over 703.000 movie reviews and 189.000 hotel reviews. The proposed method achieved better, or equal, performance than the benchmark baselines.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 37th European Conference on IR Research, ECIR 2015, Vienna, Austria, March 29 - April 2, 2015. Proceedings
Number of pages6
ISBN (Electronic)978-331916353-6
Publication statusPublished - 2015
Event37th European Conference on IR Research, : ECIR 2015 - Vienna, Vienna, Austria
Duration: 29 Mar 20152 Apr 2015

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Verlag
ISSN (Print)0302-9743


Conference37th European Conference on IR Research,
Abbreviated titleECIR 2015


  • Opinion retrieval
  • Sentiment analysis
  • Sentiment space model


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