Alena Neviarouskaya

SentiFul: A Lexicon for Sentiment Analysis

In this paper, we describe methods to automatically generate and score a new sentiment lexicon, called SentiFul, and expand it through direct synonymy and antonymy relations, hyponymy relations, derivation, and compounding with known lexical units. We propose to distinguish four types of affixes (used to derive new words) depending on the role they play with regard to sentiment features: propagating, reversing, intensifying, and weakening. Besides derivation, we considered important process of finding new words such as compounding, which is a highly productive process, especially in the case of nouns and adjectives. We elaborated the algorithm for automatic extraction of new sentiment-related compounds from WordNet using words from SentiFul as seeds for sentiment-carrying base components and applying the patterns of compound formations. In the paper, the importance of considering modifiers, contextual valence shifters, and modal operators, which are integral parts of the SentiFul lexicon for robust sentiment analysis, is also discussed.

Textual Affect Sensing for Sociable and Expressive Online Communication

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.

Affect analysis model: Novel rule-based approach to affect sensing from text

In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).

SentiFul: Generating a reliable lexicon for sentiment analysis

The main drawback of any lexicon-based sentiment analysis system is the lack of scalability. Thus, in this paper, we will describe methods to automatically generate and score a new sentiment lexicon, called SentiFul, and expand it through direct synonymy relations and morphologic modifications with known lexical units. We propose to distinguish four types of affixes (used to derive new words) depending on the role they play with regard to sentiment features: propagating, reversing, intensifying, and weakening.

Analysis of affect expressed through the evolving language of online communication

In this paper, we focus on affect recognition from text in order to facilitate sensitive and expressive communication in computer-mediated environments. Our model for analyzing affect conveyed by text is tailored to handle the style and specifics of informal online conversations. The motivation behind our approach is to improve social interactivity and emotional expressiveness of real-time messaging.In order to estimate affect in text, our model processes symbolic cues, such as emoticons, detects and transforms abbreviations, and employs natural language processing techniques for word/phrase/sentence-level analysis, e.g. by considering relations among words in a sentence. As a result of the analysis, text can be categorized into emotional states and communicative functions. A designed graphical representation of a user (avatar) displays emotions and social behaviour driven by text and performs natural idle movements. The proposed system shows promising results on affect recognition in real examples of online conversation.

Affective haptics in emotional communication

In the paper we are proposing a conceptually novel approach to reinforcing (intensifying) own feelings and reproducing (simulating) the emotions felt by the partner during online communication through specially designed system, iFeel_IM!. The core component, Affect Analysis Model, automatically recognizes nine emotions from text. The detected emotion is stimulated by innovative haptic devices integrated into iFeel_IM!. The implemented system can considerably enhance emotionally immersive experience of real-time messaging.

User study on AffectIM, an avatar-based Instant Messaging system employing rule-based affect sensing from text

報告番号: ; 学位授与年月日: 2008-03-24 ; 学位の種別: 修士 ; 学位の種類: 修士(情報理工学) ; 学位記番号: ; 研究科・専攻: 情報理工学系研究科電子情報学専攻

World's first wearable humanoid robot that augments our emotions

In the paper we are proposing a conceptually novel approach to reinforcing (intensifying) own feelings and reproducing (simulating) the emotions felt by the partner during online communication through wearable humanoid robot. The core component, Affect Analysis Model, automatically recognizes nine emotions from text. The detected emotion is stimulated by innovative haptic devices integrated into the robot. The implemented system can considerably enhance the emotionally immersive experience of real-time messaging. Users can not only exchange messages but also emotionally and physically feel the presence of the communication partner (e.g., family member, friend, or beloved person).

iFeel_IM: innovative real-time communication system with rich emotional and haptic channels

The paper focuses on a novel system iFeel_IM! that integrates 3D virtual world, intelligent component for automatic emotion recognition from text, and innovative affective haptic interfaces providing additional nonverbal communication channels through simulation of emotional feedback and social touch. The motivation behind our work is to enrich social interaction and emotional involvement of the users of communication media. iFeel_IM! users can not only exchange messages but also emotionally and physically feel the presence of the communication partner.

Recognition of affect conveyed by text messaging in online communication

In this paper, we address the task of affect recognition from text messaging. In order to sense and interpret emotional information expressed through written language, rule-based affect analysis system employing natural language processing techniques was created. Since the purpose of our work is to improve social interactivity and affective expressiveness of computer-mediated communication, we decided to tailor the system to handle style and specifics of online conversations. Proposed algorithm for affect analysis covers symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. To realize visual reflection of textual affective information, we have designed an avatar displaying emotions, social behaviour, and natural idle movements.