text: We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.
  @cite_27: Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive negative classification from 80 up to 85.4 . The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7 , an improvement of 9.7 over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases.
  @cite_11: Preface Acknowledgments Theory and Methods: 1. Introduction and overview of the book 2. The BWS object case 3. The BWS profile case 4. The BWS multi-profile case 5. Basic models 6. Looking forward Applications - Case 1: 7. BWS object case application: attitudes towards end-of-life care Terry N. Flynn, Elisabeth Huynh and Charles Corke 8. How consumers choose wine: using best-worst scaling across countries Larry Lockshin and Eli Cohen 9. Best-worst scaling: an alternative to ratings data Geoffrey N. Soutar, Jillian C. Sweeney and Janet R. McColl-Kennedy Applications - Case 2: 10. When the ayes don't have it: supplementing an accept reject DCE with a case 2 best-worst scaling task Richard T. Carson and Jordan J. Louviere 11. BWS profile case application: preferences for treatment in dentistry Emma McIntosh and Terry N. Flynn 12. BWS profile case application: preferences for quality of life in Australia Terry N. Flynn and Elisabeth Huynh Applications - Case 3: 13. The stability of aggregate-level preferences in longitudinal discrete choice experiments Towhidul Islam and Jordan J. Louviere 14. Case 3 best-worst analysis using delivered pizza and toothpaste examples Bart D. Frischknecht and Jordan J. Louviere 15. Using alternative-specific DCE designs and best and worst choices to model choices Jordan J. Louviere References Subject index Author index.
summary: Fully-supervised approaches based on neural networks have achieved impressive results on fine-grained sentiment classification @cite_11 @cite_27 . More recently, (MIL) models have been proposed that use freely available review ratings to train segment-level predictors. and train sentence-level predictors under a MIL objective, while our previous work introduced , a hierarchical model that is trained end-to-end on document labels and produces polarity-based opinion summaries of single reviews. Here, we use to predict the sentiment polarity of individual opinions.