Generating Subjective Responses to Opinionated Articles in Social Media: An Agenda-Driven Architecture and a Turing-Like Test

@inproceedings{Cagan2014GeneratingSR,
  title={Generating Subjective Responses to Opinionated Articles in Social Media: An Agenda-Driven Architecture and a Turing-Like Test},
  author={Tomer Cagan and S. Frank and Reut Tsarfaty},
  booktitle={ACL 2014},
  year={2014}
}
Natural language traffic in social media (blogs, microblogs, talkbacks) enjoys vast monitoring and analysis efforts. However, the question whether computer systems can generate such content in order to effectively interact with humans has been only sparsely attended to. This paper presents an architecture for generating subjective responses to opinionated articles based on users’ agenda, documents’ topics, sentiments and a knowledge graph. We present an empirical evaluation method for… 

Figures and Tables from this paper

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
TLDR
An up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised is given, to highlight a number of recent research topics that have arisen partly as a result of growing synergies betweenNLG and other areas of artifical intelligence.
Predicting Psychological Health from Childhood Essays. The UGent-IDLab CLPsych 2018 Shared Task System.
TLDR
The IDLab system submitted to Task A of the CLPsych 2018 shared task is predicting psychological health of children based on language used in hand-written essays and socio-demographic control variables using linear models, gradient boosting as well as neural-network based regressors to predict scores.
Data-Driven Broad-Coverage Grammars for Opinionated Natural Language Generation (ONLG)
TLDR
This work presents a data-driven architecture for ONLG that generates subjective responses triggered by users’ agendas, consisting of topics and sentiments, and based on wide-coverage automatically-acquired generative grammars.

References

SHOWING 1-10 OF 50 REFERENCES
Data-Driven Response Generation in Social Media
TLDR
It is found that mapping conversational stimuli onto responses is more difficult than translating between languages, due to the wider range of possible responses, the larger fraction of unaligned words/phrases, and the presence of large phrase pairs whose alignment cannot be further decomposed.
Multiple Ranking Strategies for Opinion Retrieval in Blogs - The University of Amsterdam at the 2006 TREC Blog Track
TLDR
The approach to identifying opinions in blog post consisted of scoring the posts separately on various aspects associated with an expression of opinion about a topic, including shallow sentiment analysis, spam detection, and link-based authority estimation, yielding significant improvement over a content-only baseline.
How opinions are received by online communities: a case study on amazon.com helpfulness votes
TLDR
It is found that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product.
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
TLDR
A supervised sentiment classification framework which is based on data from Twitter, a popular microblogging service, is proposed, utilizing 50 Twitter tags and 15 smileys as sentiment labels, allowing identification and classification of diverse sentiment types of short texts.
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and
Finding high-quality content in social media
TLDR
This paper introduces a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition, and shows that its system is able to separate high-quality items from the rest with an accuracy close to that of humans.
An intelligent discussion-bot for answering student queries in threaded discussions
TLDR
The results show that the discussion-bot can begin to meet students' learning requests and discuss directions that might be taken to increase the effectiveness of the question matching and answer extraction algorithms.
Learning to Extract International Relations from Political Context
TLDR
A new probabilistic model for extracting events between major political actors from news corpora by bringing together familiar components in natural language processing with contextual political information— temporal and dyad dependence—to infer latent event classes is described.
Software Framework for Topic Modelling with Large Corpora
TLDR
This work describes a Natural Language Processing software framework which is based on the idea of document streaming, i.e. processing corpora document after document, in a memory independent fashion, and implements several popular algorithms for topical inference, including Latent Semantic Analysis and Latent Dirichlet Allocation in a way that makes them completely independent of the training corpus size.
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
TLDR
This work discusses SENTIWORDNET 3.0, a lexical resource explicitly devised for supporting sentiment classification and opinion mining applications, and reports on the improvements concerning aspect (b) that it embodies with respect to version 1.0.
...
...