Results 1 - 10
of
49
Opinion Mining and Sentiment Analysis
, 2008
"... An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, active ..."
Abstract
-
Cited by 749 (3 self)
- Add to MetaCart
An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include materialon summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
Sentiment Polarity Identification in Financial News: A Cohesion-based Approach
- Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics
, 2007
"... Text is not unadulterated fact. A text can make you laugh or cry but can it also make you short sell your stocks in company A and buy up options in company B? Research in the domain of finance strongly suggests that it can. Studies have shown that both the informational and affective aspects of news ..."
Abstract
-
Cited by 59 (1 self)
- Add to MetaCart
(Show Context)
Text is not unadulterated fact. A text can make you laugh or cry but can it also make you short sell your stocks in company A and buy up options in company B? Research in the domain of finance strongly suggests that it can. Studies have shown that both the informational and affective aspects of news text affect the markets in profound ways, impacting on volumes of trades, stock prices, volatility and even future firm earnings. This paper aims to explore a computable metric of positive or negative polarity in financial news text which is consistent with human judgments and can be used in a quantitative analysis of news sentiment impact on financial markets. Results from a preliminary evaluation are presented and discussed. 1
Automatic identification of pro and con reasons in online reviews
- In Proceedings of COLING/ACL Poster Sessions
, 2006
"... In this paper, we present a system that automatically extracts the pros and cons from online reviews. Although many approaches have been developed for extracting opinions from text, our focus here is on extracting the reasons of the opinions, which may themselves be in the form of either fact or opi ..."
Abstract
-
Cited by 47 (0 self)
- Add to MetaCart
(Show Context)
In this paper, we present a system that automatically extracts the pros and cons from online reviews. Although many approaches have been developed for extracting opinions from text, our focus here is on extracting the reasons of the opinions, which may themselves be in the form of either fact or opinion. Leveraging online review sites with author-generated pros and cons, we propose a system for aligning the pros and cons to their sentences in review texts. A maximum entropy model is then trained on the resulting labeled set to subsequently extract pros and cons from online review sites that do not explicitly provide them. Our experimental results show that our resulting system identifies pros and cons with 66 % precision and 76 % recall. 1
When specialists and generalists work together: overcoming domain dependence in sentiment tagging
- In Proceedings of ACL-08: HLT
, 2008
"... This study presents a novel approach to the problem of system portability across different domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on Word-Net. The paper explores the challe ..."
Abstract
-
Cited by 34 (1 self)
- Add to MetaCart
(Show Context)
This study presents a novel approach to the problem of system portability across different domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on Word-Net. The paper explores the challenges of system portability across domains and text genres (movie reviews, news, blogs, and product reviews), highlights the factors affecting system performance on out-of-domain and smallset in-domain data, and presents a new system consisting of the ensemble of two classifiers with precision-based vote weighting, that provides significant gains in accuracy and recall over the corpus-based classifier and the lexicon-based system taken individually. 1
Summarizing emails with conversational cohesion and subjectivity
- In ACL-08: HLT: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
, 2008
"... In this paper, we study the problem of summarizing email conversations. We first build a sentence quotation graph that captures the conversation structure among emails. We adopt three cohesion measures: clue words, semantic similarity and cosine similarity as the weight of the edges. Second, we use ..."
Abstract
-
Cited by 30 (2 self)
- Add to MetaCart
(Show Context)
In this paper, we study the problem of summarizing email conversations. We first build a sentence quotation graph that captures the conversation structure among emails. We adopt three cohesion measures: clue words, semantic similarity and cosine similarity as the weight of the edges. Second, we use two graph-based summarization approaches, Generalized ClueWordSummarizer and Page-Rank, to extract sentences as summaries. Third, we propose a summarization approach based on subjective opinions and integrate it with the graph-based ones. The empirical evaluation shows that the basic clue words have the highest accuracy among the three cohesion measures. Moreover, subjective words can significantly improve accuracy. 1
M.: Textual Affect Sensing for Sociable and Expressive Online Communication
"... Abstract. In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging. The evolving nature of language in online conversations is a main issue in affect sensing from this media type, since sentence parsing might fail while syntactical structure ..."
Abstract
-
Cited by 25 (9 self)
- Add to MetaCart
(Show Context)
Abstract. In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging. The evolving nature of language in online conversations is a main issue in affect sensing from this media type, since sentence parsing might fail while syntactical structure analysis. The developed Affect Analysis Model was designed to handle not only correctly written text, but also informal messages written in abbreviated or expressive manner. The proposed rule-based approach processes each sentence in sequential stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. In a study based on 160 sentences, the system result agrees with at least two out of three human annotators in 70 % of the cases. In order to reflect the detected affective information and social behaviour, an avatar was created.
Adapting a polarity lexicon using integer linear programming for domainspecific sentiment classification
- In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing
"... Polarity lexicons have been a valuable resource for sentiment analysis and opinion mining. There are a number of such lexical resources available, but it is often suboptimal to use them as is, because general purpose lexical resources do not reflect domain-specific lexical usage. In this paper, we p ..."
Abstract
-
Cited by 25 (2 self)
- Add to MetaCart
Polarity lexicons have been a valuable resource for sentiment analysis and opinion mining. There are a number of such lexical resources available, but it is often suboptimal to use them as is, because general purpose lexical resources do not reflect domain-specific lexical usage. In this paper, we propose a novel method based on integer linear programming that can adapt an existing lexicon into a new one to reflect the characteristics of the data more directly. In particular, our method collectively considers the relations among words and opinion expressions to derive the most likely polarity of each lexical item (positive, neutral, negative, or negator) for the given domain. Experimental results show that our lexicon adaptation technique improves the performance of fine-grained polarity classification. 1
Multiple ranking strategies for opinion retrieval in blogs
- In Proceesings of the 15th Text Retrieval Conference
, 2006
"... We describe our participation in the Opinion Retrieval task at TREC 2006. Our 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 l ..."
Abstract
-
Cited by 23 (1 self)
- Add to MetaCart
We describe our participation in the Opinion Retrieval task at TREC 2006. Our 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. The separate approaches were combined into a single ranking, yielding significant improvement over a content-only baseline.
From Words to Senses: A Case Study of Subjectivity Recognition
"... We determine the subjectivity of word senses. To avoid costly annotation, we evaluate how useful existing resources established in opinion mining are for this task. We show that results achieved with existing resources that are not tailored towards word sense subjectivity classification can rival re ..."
Abstract
-
Cited by 16 (1 self)
- Add to MetaCart
(Show Context)
We determine the subjectivity of word senses. To avoid costly annotation, we evaluate how useful existing resources established in opinion mining are for this task. We show that results achieved with existing resources that are not tailored towards word sense subjectivity classification can rival results achieved with supervision on a manually annotated training set. However, results with different resources vary substantially and are dependent on the different definitions of subjectivity used in the establishment of the resources. 1
CLaC and CLaC-NB: Knowledge-based and corpus-based approaches to sentiment tagging
"... For the Affective Text task at Semeval-1/Senseval-4, the CLaC team compared a knowledge-based, domain-independent approach and a standard, statistical machine learning approach to ternary sentiment annotation of news headlines. In this paper we describe the two systems submitted to the competition a ..."
Abstract
-
Cited by 14 (2 self)
- Add to MetaCart
(Show Context)
For the Affective Text task at Semeval-1/Senseval-4, the CLaC team compared a knowledge-based, domain-independent approach and a standard, statistical machine learning approach to ternary sentiment annotation of news headlines. In this paper we describe the two systems submitted to the competition and evaluate their results. We show that the knowledge-based unsupervised method achieves high accuracy and precision but low recall, while supervised statistical approach trained on small amount of in-domain data provides relatively high recall at the cost of low precision. 1
