How can NLP Tasks Mutually Benefit Sentiment Analysis? A Holistic Approach to Sentiment Analysis

Existing opinion analysis techniques rely on the clues within the sentence that focus on the sentiment analysis task itself. However, the sentiment analysis task is not isolated from other NLP tasks (co-reference resolution, entity linking, etc) but they can benefit each other. In this paper, we define dependencies between sentiment analysis and other tasks, and express the dependencies in first order logic rules regardless of the representations of different tasks. The conceptual framework proposed in this paper using such dependency rules as constraints aims at exploiting information outside the sentence and outside the document to improve sentiment analysis. Further, the framework allows exception to the rules.

[1]  Michael Strube,et al.  Annotating Anaphoric and Bridging Relations with MMAX , 2001, SIGDIAL Workshop.

[2]  Lei Zhang,et al.  Sentiment Analysis and Opinion Mining , 2017, Encyclopedia of Machine Learning and Data Mining.

[3]  Parminder Bhatia,et al.  Better Document-level Sentiment Analysis from RST Discourse Parsing , 2015, EMNLP.

[4]  Dragomir R. Radev,et al.  Detecting Subgroups in Online Discussions by Modeling Positive and Negative Relations among Participants , 2012, EMNLP.

[5]  Dragomir R. Radev,et al.  Subgroup Detection in Ideological Discussions , 2012, ACL.

[6]  Amita Misra,et al.  Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue , 2013, SIGDIAL Conference.

[7]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[8]  Noah A. Smith,et al.  Measuring Ideological Proportions in Political Speeches , 2013, EMNLP.

[9]  Janyce Wiebe,et al.  An Account of Opinion Implicatures , 2014, ArXiv.

[10]  Janyce Wiebe,et al.  Joint Inference and Disambiguation of Implicit Sentiments via Implicature Constraints , 2014, COLING.

[11]  Janyce Wiebe,et al.  A Conceptual Framework for Inferring Implicatures , 2014, WASSA@ACL.

[12]  Heng Ji,et al.  Knowledge Base Population: Successful Approaches and Challenges , 2011, ACL.

[13]  Junehwa Song,et al.  Contrasting Opposing Views of News Articles on Contentious Issues , 2011, ACL.

[14]  Kristin Precoda,et al.  Detection of Agreement and Disagreement in Broadcast Conversations , 2011, ACL.

[15]  J. Wiebe,et al.  Discourse-level relations for opinion analysis , 2010 .

[16]  Jacob Eisenstein,et al.  Discourse Connectors for Latent Subjectivity in Sentiment Analysis , 2013, NAACL.

[17]  Claire Cardie,et al.  Joint Inference for Fine-grained Opinion Extraction , 2013, ACL.

[18]  M. Walker,et al.  How can you say such things?!?: Recognizing Disagreement in Informal Political Argument , 2011 .

[19]  Janyce Wiebe,et al.  Joint Prediction for Entity/Event-Level Sentiment Analysis using Probabilistic Soft Logic Models , 2015, EMNLP.

[20]  Julia Hirschberg,et al.  Identifying Agreement and Disagreement in Conversational Speech: Use of Bayesian Networks to Model Pragmatic Dependencies , 2004, ACL.

[21]  Philip Resnik,et al.  Political Ideology Detection Using Recursive Neural Networks , 2014, ACL.

[22]  Janyce Wiebe,et al.  Benefactive/Malefactive Event and Writer Attitude Annotation , 2013, ACL.

[23]  Thierry Poibeau,et al.  Multi-source, Multilingual Information Extraction and Summarization , 2012, Theory and Applications of Natural Language Processing.

[24]  Philipp Koehn,et al.  Synthesis Lectures on Human Language Technologies , 2016 .

[25]  Maria Leonor Pacheco,et al.  of the Association for Computational Linguistics: , 2001 .

[26]  Philip Resnik,et al.  More than Words: Syntactic Packaging and Implicit Sentiment , 2009, NAACL.

[27]  Mark Dredze,et al.  Entity Linking: Finding Extracted Entities in a Knowledge Base , 2013, Multi-source, Multilingual Information Extraction and Summarization.

[28]  Renata Vieira,et al.  An Empirically-based System for Processing Definite Descriptions , 2000, CL.

[29]  Claire Cardie,et al.  Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon , 2014, WASSA@ACL.

[30]  Sean Gerrish,et al.  Predicting Legislative Roll Calls from Text , 2011, ICML.

[31]  Chris Callison-Burch,et al.  PPDB: The Paraphrase Database , 2013, NAACL.

[32]  Herbert H. Clark,et al.  Bridging , 1975, TINLAP.