Identifying subjective statements in news titles using a personal sense annotation framework

This is the accepted version of the following article: Panicheva, P.; Cardiff, J.; Rosso, P. (2013). Identifying subjective statements in news titles using a personal sense annotation framework. Journal of the American Society for Information Science and Technology. 64(7):1411-1422 , which has been published in final form at http://dx.doi.org/10.1002/asi.22841.[EN] Subjective language contains information about private states. The goal of subjective language identification is to determine that a private state is expressed, without considering its polarity or specific emotion. A component of word meaning, "Personal Sense," has clear potential in the field of subjective language identification, as it reflects a meaning of words in terms of unique personal experience and carries personal characteristics. In this paper we investigate how Personal Sense can be harnessed for the purpose of identifying subjectivity in news titles. In the process, we develop a new Personal Sense annotation framework for annotating and classifying subjectivity, polarity, and emotion. The Personal Sense framework yields high performance in a fine-grained subsentence subjectivity classification. Our experiments demonstrate lexico-syntactic features to be useful for the identification of subjectivity indicators and the targets that receive the subjective Personal Sense.The work of Paolo Rosso was done within the EC WIQEI IRSES project (grant no. 269180) FP 7 Marie Curie People Framework, the MICINN Text-Enterprise 2.0 project (TIN2009-13391-C04-03) Plan I+D+I, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. We are grateful to the anonymous reviewers for helpful comments.Panicheva, P.; Cardiff, J.; Rosso, P. (2013). Identifying subjective statements in news titles using a personal sense annotation framework. Journal of the American Society for Information Science and Technology. 64(7):1411-1422. https://doi.org/10.1002/asi.22841S1411142264

Fine-grained Subjectivity and Sentiment Analysis: Recognizing the intensity, polarity, and attitudes of private states

Private states (mental and emotional states) are part of the information that is conveyed in many forms of discourse. News articles often report emotional responses to news stories; editorials, reviews, and weblogs convey opinions and beliefs. This dissertation investigates the manual and automatic identification of linguistic expressions of private states in a corpus of news documents from the world press. A term for the linguistic expression of private states is subjectivity.The conceptual representation of private states used in this dissertation is that of Wiebe et al. (2005). As part of this research, annotators are trained to identify expressions of private states and their properties, such as the source and the intensity of the private state. This dissertation then extends the conceptual representation of private states to better model the attitudes and targets of private states. The inter-annotator agreement studies conducted for this dissertation show that the various concepts in the original and extended representation of private states can be reliably annotated.Exploring the automatic recognition of various types of private states is also a large part of this dissertation. Experiments are conducted that focus on three types of fine-grained subjectivity analysis: recognizing the intensity of clauses and sentences, recognizing the contextual polarity of words and phrases, and recognizing the attribution levels where sentiment and arguing attitudes are expressed. Various supervised machine learning algorithms are used to train automatic systems to perform each of these tasks. These experiments result in automatic systems for performing fine-grained subjectivity analysis that significantly outperform baseline systems

Sentiment Analysis: An Overview from Linguistics

Sentiment analysis is a growing field at the intersection of linguistics and computer science, which attempts to automatically determine the sentiment, or positive/negative opinion, contained in text. Sentiment can be characterized as positive or negative evaluation expressed through language. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative towards the item being reviewed. Sentiment analysis is now a common tool in the repertoire of social media analysis carried out by companies, marketers and political analysts. Research on sentiment analysis extracts information from positive and negative words in text, from the context of those words, and the linguistic structure of the text. This brief survey examines in particular the contributions that linguistic knowledge can make to the problem of automatically determining sentiment

Implicit emotion detection in text

In text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles

A machine-learning approach to negation and speculation detection for sentiment analysis

Recognizing negative and speculative information is highly relevant for sentiment analysis. This paper presents a machine-learning approach to automatically detect this kind of information in the review domain. The resulting system works in two steps: in the first pass, negation/speculation cues are identified, and in the second phase the full scope of these cues is determined. The system is trained and evaluated on the Simon Fraser University Review corpus, which is extensively used in opinion mining. The results show how the proposed method outstrips the baseline by as much as roughly 20% in the negation cue detection and around 13% in the scope recognition, both in terms of F1. In speculation, the performance obtained in the cue prediction phase is close to that obtained by a human rater carrying out the same task. In the scope detection, the results are also promising and represent a substantial improvement on the baseline (up by roughly 10%). A detailed error analysis is also provided. The extrinsic evaluation shows that the correct identification of cues and scopes is vital for the task of sentiment analysis.Maite Taboada from the Natural Sciences and Engineering Research Council of Canada (Discovery Grant 261104- 2008). This work was partly funded by the Spanish Ministry of Education and Science (TIN2009-14057-C03-03 Project) and the Andalusian Ministry of Economy, Innovation and Science (TIC 07629 and TIC 07684 Projects)

Automatic Sentiment Analysis in On-line Text

The growing stream of content placed on the Web provides a huge collection of textual resources. People share their experiences on-line, ventilate their opinions (and frustrations), or simply talk just about anything. The large amount of available data creates opportunities for automatic mining and analysis. The information we are interested in this paper, is how people feel about certain topics. We consider it as a classification task: their feelings can be positive, negative or neutral. A sentiment isn't always stated in a clear way in the text; it is often represented in subtle, complex ways. Besides direct expression of the user's feelings towards a certain topic, he or she can use a diverse range of other techniques to express his or her emotions. On top of that, authors may mix objective and subjective information about a topic, or write down thoughts about other topics than the one we are investigating. Lastly, the data gathered from the World Wide Web often contains a lot of noise. All of this makes the task of automatic recognition of the sentiment in on-line text more difficult. We will give an overview of various techniques used to tackle the problems in the domain of sentiment analysis, and add some of our own results

From humor recognition to Irony detection: The figurative language of social media

[EN] The research described in this paper is focused on analyzing two playful domains of language: humor and irony, in order to identify key values components for their automatic processing. In particular, we are focused on describing a model for recognizing these phenomena in social media, such as "tweets". Our experiments are centered on five data sets retrieved from Twitter taking advantage of user-generated tags, such as "#humor" and "#irony". The model, which is based on textual features, is assessed on two dimensions: representativeness and relevance. The results, apart from providing some valuable insights into the creative and figurative usages of language, are positive regarding humor, and encouraging regarding irony. (C) 2012 Elsevier B.V. All rights reserved.This work has been done in the framework of the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems and it has been partially funded by the European Commission as part of the WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People Framework, and by MICINN as part of the Text-Enterprise 2.0 project (TIN2009-13391-C04-03) within the Plan I + D + I. The National Council for Science and Technology (CONACyT - Mexico) has funded the research work of Antonio Reyes.Reyes Pérez, A.; Rosso, P.; Buscaldi, D. (2012). From humor recognition to Irony detection: The figurative language of social media. Data and Knowledge Engineering. 74:1-12. https://doi.org/10.1016/j.datak.2012.02.005S1127

Detecting subjectivity through lexicon-grammar. strategies databases, rules and apps for the italian language

2014 - 2015The present research handles the detection of linguistic phenomena connected to subjectivity, emotions and opinions from a computational point of view. The necessity to quickly monitor huge quantity of semi-structured and unstructured data from the web, poses several challenges to Natural Language Processing, that must provide strategies and tools to analyze their structures from a lexical, syntactical and semantic point of views. The general aim of the Sentiment Analysis, shared with the broader fields of NLP, Data Mining, Information Extraction, etc., is the automatic extraction of value from chaos; its specific focus instead is on opinions rather than on factual information. This is the aspect that differentiates it from other computational linguistics subfields. The majority of the sentiment lexicons has been manually or automatically created for the English language; therefore, existent Italian lexicons are mostly built through the translation and adaptation of the English lexical databases, e.g. SentiWordNet and WordNet-Affect. Unlike many other Italian and English sentiment lexicons, our database SentIta, made up on the interaction of electronic dictionaries and lexicon dependent local grammars, is able to manage simple and multiword structures, that can take the shape of distributionally free structures, distributionally restricted structures and frozen structures. Moreover, differently from other lexicon-based Sentiment Analysis methods, our approach has been grounded on the solidity of the Lexicon-Grammar resources and classifications, that provides fine-grained semantic but also syntactic descriptions of the lexical entries. According with the major contribution in the Sentiment Analysis literature, we did not consider polar words in isolation. We computed they elementary sentence contexts, with the allowed transformations and, then, their interaction with contextual valence shifters, the linguistic devices that are able to modify the prior polarity of the words from SentIta, when occurring with them in the same sentences. In order to do so, we took advantage of the computational power of the finite-state technology. We formalized a set of rules that work for the intensification, downtoning and negation modeling, the modality detection and the analysis of comparative forms. With regard to the applicative part of the research, we conducted, with satisfactory results, three experiments on the same number of Sentiment Analysis subtasks: the sentiment classification of documents and sentences, the feature-based Sentiment Analysis and the Semantic Role Labeling based on sentiments. [edited by author]XIV n.s

Unsupervised and knowledge-poor approaches to sentiment analysis

Sentiment analysis focuses upon automatic classiffication of a document's sentiment (and more generally extraction of opinion from text). Ways of expressing sentiment have been shown to be dependent on what a document is about (domain-dependency). This complicates supervised methods for sentiment analysis which rely on extensive use of training data or linguistic resources that are usually either domain-specific or generic. Both kinds of resources prevent classiffiers from performing well across a range of domains, as this requires appropriate in-domain (domain-specific) data. This thesis presents a novel unsupervised, knowledge-poor approach to sentiment analysis aimed at creating a domain-independent and multilingual sentiment analysis system. The approach extracts domain-specific resources from documents that are to be processed, and uses them for sentiment analysis. This approach does not require any training corpora, large sets of rules or generic sentiment lexicons, which makes it domain- and languageindependent but at the same time able to utilise domain- and language-specific information. The thesis describes and tests the approach, which is applied to diffeerent data, including customer reviews of various types of products, reviews of films and books, and news items; and to four languages: Chinese, English, Russian and Japanese. The approach is applied not only to binary sentiment classiffication, but also to three-way sentiment classiffication (positive, negative and neutral), subjectivity classifiation of documents and sentences, and to the extraction of opinion holders and opinion targets. Experimental results suggest that the approach is often a viable alternative to supervised systems, especially when applied to large document collections