Finding Mutual Benefit between Subjectivity Analysis and Information Extraction

  title={Finding Mutual Benefit between Subjectivity Analysis and Information Extraction},
  author={Janyce Wiebe and Ellen Riloff},
  journal={IEEE Transactions on Affective Computing},
  • J. WiebeE. Riloff
  • Published 1 October 2011
  • Computer Science
  • IEEE Transactions on Affective Computing
"Subjectivity analysis” systems automatically identify and extract information relating to attitudes, opinions, and sentiments from text. As more and more people make their opinions available on the Internet and as people increasingly consult the Internet to ascertain other people's opinions about products, political issues, and so on, the demand for effective subjectivity analysis systems continues to grow. Information extraction systems, which automatically identify and extract factual… 

Subjectivity Analysis In Opinion Mining - A Systematic Literature Review

The SLR has found that majority of the study are using machine learning approach to identify and learn subjective text due to the nature of subjectivity analysis problem that is viewed as classification problem, and the performance of this approach outperformed other approaches though currently it is at satisfactory level.

Sentence Subjectivity Analysis in Social Domains

  • Mostafa KaramibekrA. Ghorbani
  • Computer Science
    2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)
  • 2013
A lexical-syntactical approach is proposed to recognize and classify subjectivity at the sentence level and it has a good accuracy especially on the strong sentences which express explicit opinions.

Lexical-Syntactical Patterns for Subjectivity Analysis of Social Issues

A lexical-syntactical structure for subjective patterns for subjectivity analysis in social domains and its reasonable F-measure implicates its usability in applications like sentiment summarization and opinion question answering.

A study of factuality, objectivity and relevance: three desiderata in large-scale information retrieval?

This is the first study of factuality & objectivity for back-end IR, contributing novel findings about the relation between relevance and factuality/objectivity, and statistically significant gains to retrieval effectiveness in the competitive web search task.

Study on Distinct Approaches for Sentiment Analysis

One direction is make use of Fuzzy logic for sentiment analysis which may improve analysis results, suggest one direction many researchers work on mining a content posted in natural language at different forums, blogs or social networks.

A Framework for Automated Corpus Generation for Semantic Sentiment Analysis

A proposed framework for automated generation of corpus based on analysis of opinions /sentiments and semantics in a user-generate free text based on an analysis of existing corpora - WordNet, SentiWordNet, Domain specific dictionaries, and Parts of Speech (POS) tagging mechanisms for syntactic and linguistic analysis.

Survey on Discrimination Analysis and Sentimental Analysis in Text Mining by using NLP Method

Opinion Mining or Sentiment Analysis is a Natural Language Processing and Information Extraction task that identifies the user’s views or opinions explained in the form of positive, negative or neutral.

A Corpus Approach for Opinion Mining to Improve the Performance Using Averaging

This paper collected opinions of various users from various review sites and constructed a corpus to perform classification and the challenges that face the opinion mining, and tests this approach on social networking reviews such as product reviews, movie reviews and MySpace comments.

Extrapolating subjectivity research to other languages

Techniques of generating tools and resources for subjectivity analysis that do not rely on an existing natural language processing infrastructure in a given language are investigated, where English often acts as the donor language and allows through a relatively minimal amount of effort to establish preliminary subjectivity research in a target language.

Opinion Mining and Sentiment Classification: A Survey

This survey gives an overview of the efficient techniques, recent advancements and the future research directions in the field of Sentiment Analysis.



Exploiting Subjectivity Classification to Improve Information Extraction

An IE system that uses a subjective sentence classifier to filter its extractions is described and it is found that indiscriminately filtering extractions from subjective sentences was overly aggressive, but more selective filtering strategies improved IE precision with minimal recall loss.

Learning Subjective Language

This article shows that the density of subjectivity clues in the surrounding context strongly affects how likely it is that a word is subjective, and it provides the results of an annotation study assessing the subjectivity of sentences with high-density features.

Determining Term Subjectivity and Term Orientation for Opinion Mining

The task of deciding whether a given term has a positive connotations, or a negative connotation, or has no subjective connotation at all is confronted, and it is shown that determining subjectivity and orientation is a much harder problem than determining orientation alone.

Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns

This work adopts a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a), and shows that the combination of these two methods performs better than either one alone.

Relational learning techniques for natural language information extraction

A novel rule representation speci c to natural language and a learning system, Rapier, which learns information extraction rules, and initial results on a small corpus of computer-related job postings with a preliminary version of Rapier are presented.

Toward General-Purpose Learning for Information Extraction

SRV is described, a learning architecture for information extraction which is designed for maximum generality and flexibility and can exploit domain-specific information, including linguistic syntax and lexical information, in the form of features provided to the system explicitly as input for training.

Sentiment analyzer: extracting sentiments about a given topic using natural language processing techniques

This work presents sentiment analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents using natural language processing (NLP) techniques.

Identifying Collocations for Recognizing Opinions

Promising results are shown for a straightforward method of identifying collocational clues of subjectivity, as well as evidence of the usefulness of these clues for recognizing opinionated documents.

Semantic Tag Extraction from WordNet Glosses

A method that uses information from WordNet glosses to assign semantic tags to individual word meanings, rather than to entire words, is proposed and implemented in the Semantic Tag Extraction Program (STEP).

Extracting Opinions, Opinion Holders, and Topics Expressed in Online News Media Text

This method uses semantic role labeling as an intermediate step to label an opinion holder and topic using data from FrameNet, and decomposes the task into three phases: identifying an opinion-bearing word, labeling semantic roles related to the word in the sentence, and then finding the holder and the topic of the opinion word among the labeled semantic roles.