Compositional Approach for Automatic Recognition of Fine-Grained Affect, Judgment, and Appreciation in Text

TitleCompositional Approach for Automatic Recognition of Fine-Grained Affect, Judgment, and Appreciation in Text
Publication TypeThesis
Year of Publication2011
AuthorsNeviarouskaya, A
UniversityUniversity of Tokyo
CityTokyo, Japan
Thesis TypePhD

Sharing feelings, pleasant or painful impressions, showing sincere empathy or indifference, exchanging tastes and points of view, advancing moral values, expressing praise or reprehension are indispensable for full-value and effective social interplay between people. With rapidly growing online sources (news, blogs, discussion forums, product or service reviews, social networks etc.) aimed at encouraging and stimulating people's discussions concerning personal, public, or social issues, there is a great need in development of robust computational tools for the analysis of people's preferences and attitudes. Sentiment or subjectivity analysis is nowadays a rapidly developing field with a variety of emerging approaches targeting the recognition of sentiment reflected in written language. Automatic recognition of positive and negative opinions and classification of text using emotion labels have been gaining increased attention of researchers. However, the topic of recognition of fine-grained attitudes expressed in text has been ignored. Attitude types (namely, affect, judgment, and appreciation) define the specifics of appraisal being expressed: distinct types of personal emotional states; positive and negative appraisal of person's character, behavior, skills; and aesthetic evaluation of semiotic and natural phenomena (events, artifacts etc.), correspondingly. In this thesis, first we describe the developed Affect Analysis Model (AAM) that is based on rule-based linguistic approach for classification of sentences using nine emotion labels (anger, disgust, fear, guilt, interest, joy, sadness, shame, and surprise) or neutral. We demonstrate the results of AAM evaluation on two data sets represented by sentences from diary-like blog posts. Averaged accuracy of our system is up to 81.5 percent in fine-grained emotion classification (nine emotion labels and neutral) and up to 89.0 percent in polarity-based classification. As lexicon-based systems strongly depend on the availability of sentiment-conveying terms in their databases, in order to overcome the problem of lexicon coverage, we introduce original methods for building and expanding sentiment lexicon (SentiFul) represented by sentiment-conveying words that are annotated by sentiment polarity, polarity scores and weights. The main features of the SentiFul are as follows: (1) it is built using not only methods exploring direct synonymy, antonymy, and hyponymy relations, but also innovative methods based on derivation and compounding with known lexical units (the originality and valuable contribution lie in the elaborate patterns/rules for the derivation and compounding processes that have not been considered before); (2) it is larger than the existing lists of sentiment words; (3) it includes polarity scores, in contrast to most existing sentiment dictionaries that lack assignments of degree or strength of sentiment. Our AttitudeFul database contains lexicon necessary for fine-grained attitude analysis; it includes attitude-conveying terms, extensive sets of modifiers, contextual valence shifters, and modal operators, which contribute to robust analysis of contextual attitude and its strength. In this thesis, we introduce novel compositional linguistic approach for attitude recognition in text. There are several aspects that distinguish our Attitude Analysis Model (@AM) from other systems. First, our method classifies individual sentences using fine-grained attitude labels (nine for different affective states, two for positive and negative judgment, and two for positive and negative appreciation), as against other methods that mainly focus on two sentiment categories (positive and negative) or six basic emotions. Next, our Attitude Analysis Model is based on the analysis of syntactic and dependency relations between words in a sentence; the compositionality principle (the rules of polarity reversal, aggregation, propagation, domination, neutralization, and intensification, at various grammatical levels); a novel linguistic approach based on the rules elaborated for semantically distinct verb classes; and a method considering the hierarchy of concepts. As distinct from the state-of-the-art approaches, the proposed compositional linguistic approach for automatic recognition of fine-grained affect, judgment, and appreciation in text (1) is domain-independent; (2) extensively deals with the semantics of terms, which allows accurate and robust automatic analysis of attitude type, and broadens the coverage of sentences with complex contextual attitude; (3) processes sentences of different complexity, including simple, compound, complex (with complement and relative clauses), and complex-compound sentences; (4) handles not only correctly written text, but also informal messages written in an abbreviated or expressive manner; and (5) encodes the strength of the attitude and the level of confidence, with which the attitude is expressed, through numerical values in the interval [0.0, 1.0]. The performance of our Attitude Analysis Model was evaluated on data sets represented by sentences from different domains. @AM achieved high level of accuracy on sentences from personal stories about life experiences, fairy tales, and news headlines, outperforming other methods on several measures. In fine-grained attitude classification (14 labels) our system achieved averaged accuracy of 62.1 percent, and in coarse-grained classification (3 labels)-87.9 percent. Using Affect Analysis Model and Attitude Analysis Model, we have developed several applications: AffectIM (Instant Messaging application integrated with AAM), EmoHeart (application of AAM in 3D world Second Life), iFeel_IM! (innovative real-time communication system with rich emotional and haptic channels), and web-based @AM interface. We believe that the output of our systems can contribute to the robustness of the following society-beneficial and analytical applications: public opinion mining, deep understanding of a market and trends in consumers'subjective feedback, attitude-based recommendation system, economic and political forecasting, affect-sensitive and empathic dialogue agent, emotionally expressive storytelling, integration into online communication media and social networks.