A musical system for emotional expression

The automatic control of emotional expression in music is a challenge that is far from being solved. This paper describes research conducted with the aim of developing a system with such capabilities. The system works with standard MIDI files and develops in two stages: the first offline, the second online. In the first stage, MIDI files are partitioned in segments with uniform emotional content. These are subjected to a process of features extraction, then classified according to emotional values of valence and arousal and stored in a music base. In the second stage, segments are selected and transformed according to the desired emotion and then arranged in song-like structures.The system is using a knowledge base, grounded on empirical results of works of Music Psychology that was refined with data obtained with questionnaires; we also plan to use data obtained with other methods of emotional recognition in a near future. For the experimental setups, we prepared web-based questionnaires with musical segments of different emotional content. Each subject classified each segment after listening to it, with values for valence and arousal. The modularity, adaptability and flexibility of our system’s architecture make it applicable in various contexts like video-games, theater, films and healthcare contexts.     

A tag-topic model for blog mining

Blog mining addresses the problem of mining information from blog data. Although mining blogs may share many similarities to Web and text documents, existing techniques need to be reevaluated and adapted for the multidimensional representation of blog data, which exhibit dimensions not present in traditional documents, such as tags. Blog tags are semantic annotations in blogs which can be valuable sources of additional labels for the myriad of blog documents. In this paper, we present a tag-topic model for blog mining, which is based on the Author-Topic model and Latent Dirichlet Allocation. The tag-topic model determines the most likely tags and words for a given topic in a collection of blog posts. The model has been successfully implemented and evaluated on real-world blog data.     

Using learning automata in brain emotional learning for speech emotion recognition

We propose an improved version of brain emotional learning (BEL) model trained via learning automata (LA) for speech emotion recognition. Inspiring from the limbic system in mammalian brain, the original BEL model is composed of two neural network components, namely amygdala and orbitofrontal cortex. In this modified BEL model, named brain emotional learning based on learning automata (BELBLA), we have employed the theory of the stochastic LA in error back-propagation to train the BEL model in decreasing the high computational complexity of the traditional gradient method. Hence, the performance of the model can be enhanced. For a speech emotion recognition task, we extract the usual features, such as energy, pitch, formants, amplitude, zero crossing rate and MFCC, from average short-term signals of the emotional Berlin dataset. The experimental results show that the BELBLA outperforms some opponents, like hidden Markov model, Gaussian mixture model, k-nearest neighbor, support vector machines and artificial neural networks, for this application.     

结构特征和内容分析融合的博客文章分类

针对博客文章内容上,包含多个主题,类别归属不明显,多为作者自己主观意见且结构上,包括不同于文本的标签,普通文本分类方法直接应用于博客文章效果不理想的问题,提出一种结构特征和内容分析融合的博客文章分类方法。内容上,通过迭代两种不同特征选择方法,提高特征集代表性的前提下,利用正文,标题两个方面分类.结构上,利用博客文章特有的标签分类,并将三个方面融合。实验结果表明,改进的分类方法有效地提高了博客文章分类的性能。     

Cues in computer-mediated communication: A corpus analysis

An analysis of five contemporary corpora examines the use of several different cues in four channels of computer-mediated communication. With an in-depth corpus analysis, we show that a wealth of cues is available in online communication, and that these cues are often matched with words that have particular functions and/or semantic meanings. Using the Linguistic Inquiry and Word Count text analysis software (Pennebaker et al., 2007), we found the two largest categories represented by cue-laden words involved affect and cognitive mechanisms, suggesting that cues are largely used to indicate emotion or to disambiguate a message. We argue that learning the meaning of these cues is central to learning how people communicate nonverbally while online.     

A synthetical approach for blog recommendation: Combining trust,social relation,and semantic analysis

Weblog is a good paradigm of online social network which constitutes web-based regularly updated journals with reverse chronological sequences of dated entries, usually with blogrolls on the sidebars, allowing bloggers link to favorite site which they are frequently visited. In this study we propose a blog recommendation mechanism that combines trust model, social relation and semantic analysis and illustrates how it can be applied to a prestigious online blogging system – wretch in Taiwan. By the results of experimental study, we found a number of implications from the Weblog network and several important theories in domain of social networking were empirically justified. The experimental evaluation reveals that the proposed recommendation mechanism is quite feasible and promising.     

Evaluation criteria for blog design and analysis of causal relationships using factor analysis and DEMATEL

The purpose of this study is to find the key factors influencing blog design, and explore the causal relationships between the criteria for each factor. Since design is a multiple criteria decision-making (MCDM) problem, this study adopts a model which is a hybrid of factor analysis and the Decision Making Trial and Evaluation Laboratory method (DEMATEL). The DEMATEL method is used to simplify and visualize the interrelationships between criteria in making a decision. This study found five core factors that influence blog design: visual clarity, interface and usability, content and searchability, programming, and sociability. In addition, the key criteria for each factor were identified and the impact-relation maps obtained. The results of this study can provide useful guidance to blog designers for developing better blog platforms.     

EOSentiMiner: an opinion-aware system based on emotion ontology for sentiment analysis of Chinese online reviews

With the growing availability and popularity of online reviews, consumers' opinions towards certain products or services are generated and spread over the Internet; sentiment analysis thus arises in response to the requirement of opinion seekers. Most prior studies are concerned with statistics-based methods for sentiment classification. These methods, however, suffer from weak comprehension of text-based messages at semantic level, thus resulting in low accuracy. We propose an ontology-based opinion-aware framework – EOSentiMiner – to conduct sentiment analysis for Chinese online reviews from a semantic perspective. The emotion space model is employed to express emotions of reviews in the EOSentiMiner, where sentiment words are classified into two types: emotional words and evaluation words. Furthermore, the former contains eight emotional classes, and the latter is divided into two opinion evaluation classes. An emotion ontology model is then built based on HowNet to express emotion in a fuzzy way. Based on emotion ontology, we evaluate some factors possibly affecting sentiment classification including features of products (services), emotion polarity and intensity, degree words, negative words, rhetoric and punctuation. Finally, sentiment calculation based on emotion ontology is proposed from sentence level to document level. We conduct experiments by using the data from online reviews of cellphone and wedding photography. The result shows the EOSentiMiner outperforms baseline methods in term of accuracy. We also find that emotion expression forms and connection relationship vary across different domains of review corpora.     

A taxonomy generation tool for semantic visual analysis of large corpus of documents

Multimedia Tools and Applications - Taxonomies are semantic resources that help to categorize and add meaning to data. In a hyperconnected world where information is generated at a rate that...     

Soft-computing-based emotional expression mechanism for game of computer Go

The game of Go is considered one of the most complicated games in the world. One Go game is divided into three stages: the opening, the middle, and the ending stages. Millions of people regularly play Go in countries around the world. The game is played by two players. One is White and another is Black. The players alternate placing one of their stones on an empty intersection of a square grid-patterned game board. The player with more territory wins the game. This paper proposes a soft-computing-based emotional expression mechanism and applies it to the game of computer Go to make Go beginners enjoy watching Go game and keep their tension on the game. First, the knowledge base and rule base of the proposed mechanism are defined by following the standards of the fuzzy markup language. The soft-computing mechanism for Go regional alarm level is responsible for showing the inferred regional alarm level to Go beginners. Based on the inferred board situation, the fuzzy inference mechanisms for emotional pleasure and arousal are responsible for inferring the pleasure degree and arousal degree, respectively. An emotional expression mapping mechanism maps the inferred degree of pleasure and degree of arousal into the emotional expression of the eye robot. The protocol transmission mechanism finally sends the pre-defined protocol to the eye robot via universal serial bus interface to make the eye robot express its emotional motion. From the experimental results, it shows that the eye robot can support Go beginners to have fun and retain their tension while watching or playing a game of Go.     

An annotated corpus for the analysis of VP ellipsis

Verb Phrase Ellipsis (VPE) has been studied in great depth in theoretical linguistics, but empirical studies of VPE are rare. We extend the few previous corpus studies with an annotated corpus of VPE in all 25 sections of the Wall Street Journal corpus (WSJ) distributed with the Penn Treebank. We annotated the raw files using a stand-off annotation scheme that codes the auxiliary verb triggering the elided verb phrase, the start and end of the antecedent, the syntactic type of antecedent (VP, TV, NP, PP or AP), and the type of syntactic pattern between the source and target clauses of the VPE and its antecedent. We found 487 instances of VPE (including predicative ellipsis, antecedent-contained deletion, comparative constructions, and pseudo-gapping) plus 67 cases of related phenomena such as do so anaphora. Inter-annotator agreement was high, with a 0.97 average F-score for three annotators for one section of the WSJ. Our annotation is theory neutral, and has better coverage than earlier efforts that relied on automatic methods, e.g. simply searching the parsed version of the Penn Treebank for empty VP’s achieves a high precision (0.95) but low recall (0.58) when compared with our manual annotation. The distribution of VPE source–target patterns deviates highly from the standard examples found in the theoretical linguistics literature on VPE, once more underlining the value of corpus studies. The resulting corpus will be useful for studying VPE phenomena as well as for evaluating natural language processing systems equipped with ellipsis resolution algorithms, and we propose evaluation measures for VPE detection and VPE antecedent selection. The stand-off annotation is freely available for research purposes.     

A multi-modal dialogue analysis method for medical interviews based on design of interaction corpus

We propose a multi-modal dialogue analysis method for medical interviews that hierarchically interprets nonverbal interaction patterns in a bottom-up manner and simultaneously visualizes the topic structure. Our method aims to provide physicians with the clues generally overlooked by conventional dialogue analysis to form a cycle of dialogue practice and analysis. We introduce a motif and a pattern cluster in the designs of the hierarchical indices of interaction and exploit the Jensen–Shannon divergence (JSD) metric to reduce the number of usable indices. We applied the proposed interpretation method of interaction patterns to develop a corpus of interviews. The results of a summary reading experiment confirmed the validity of the developed indices. Finally, we discussed the integrated analysis of the topic structure and a nonverbal summary.     

A product configuration analysis method for emotional design using a personal construct theory

Identifying emotion-related product attributes (perceived by consumers) is no easy task in the realm of emotional design. Conventionally, this process relies heavily on the researchers who conduct the Kansei experiments selecting product attributes such as color, form, and texture for Kansei studies. However, in so doing, other product attributes that also play a vital role in product-emotion associations might be neglected by the researchers. More importantly, the identification of product attributes should be based on consumer's point of view (and feelings). Accordingly, a personal construct theory based product configuration analysis method is proposed in this work. The method develops the customer's mind map for each Kansei tag in order to capture replications of candidate products. A means-value chain is used to generate targets which are later compared with candidate products by consumers. The comparison results could suggest product attributes that are relevant to the desired Kansei. The proposed approach is presented and illustrated using a case study of Graffiti designs on notebooks. Results obtained are discussed. It appears that the proposed method is promising in identifying product attributes with desired Kansei impacts.     

Sound design for emotion and intention expression of socially interactive robots

The current concept of robots has been greatly influenced by the image of robots from science fiction. Since robots were introduced into human society as partners with them, the importance of human–robot interaction has grown. In this paper, we have designed seven musical sounds, five of which express intention and two that express emotion for the English teacher robot, Silbot. To identify the sound design considerations, we analyzed the sounds of robots, R2-D2 and Wall-E, from two popular movies, Star Wars and Wall-E, respectively. From the analysis, we found that intonation, pitch, and timbre are dominant musical parameters to express intention and emotion. To check the validity of these designed sounds for intention and emotion, we performed a recognition rate experiment. The experiment showed that the five designed sounds for intentions and the two for emotions are sufficient to deliver the intended emotions.     

Employing both gender and emotion cues to enhance speaker identification performance in emotional talking environments

Speaker recognition performance in emotional talking environments is not as high as it is in neutral talking environments. This work focuses on proposing, implementing, and evaluating a new approach to enhance the performance in emotional talking environments. The new proposed approach is based on identifying the unknown speaker using both his/her gender and emotion cues. Both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) have been used as classifiers in this work. This approach has been tested on our collected emotional speech database which is composed of six emotions. The results of this work show that speaker identification performance based on using both gender and emotion cues is higher than that based on using gender cues only, emotion cues only, and neither gender nor emotion cues by 7.22 %, 4.45 %, and 19.56 %, respectively. This work also shows that the optimum speaker identification performance takes place when the classifiers are completely biased towards suprasegmental models and no impact of acoustic models in the emotional talking environments. The achieved average speaker identification performance based on the new proposed approach falls within 2.35 % of that obtained in subjective evaluation by human judges.     

A corpus for studying addressing behaviour in multi-party dialogues

This paper describes a multi-modal corpus of hand-annotated meeting dialogues that was designed for studying addressing behaviour in face-to-face conversations. The corpus contains annotated dialogue acts, addressees, adjacency pairs and gaze direction. First, we describe the corpus design where we present the meetings collection, annotation scheme and annotation tools. Then, we present the analysis of the reproducibility and stability of the annotation scheme.

Development of simulated emotion speech database for excitation source analysis

The work presented in this paper is focused on the development of a simulated emotion database particularly for the excitation source analysis. The presence of simultaneous electroglottogram (EGG) recordings for each emotion utterance helps to accurately analyze the variations in the source parameters according to different emotions. The work presented in this paper describes the development of comparatively large simulated emotion database for three emotions (Anger, Happy and Sad) along with neutrally spoken utterances in three languages (Tamil, Malayalam and Indian English). Emotion utterances in each language are recorded from 10 speakers in multiple sessions (Tamil and Malayalam). Unlike the existing simulated emotion databases, instead of emotionally neutral utterances, emotionally biased utterances are used for recording. Based on the emotion recognition experiments, the emotions elicited from emotionally biased utterances are found to show more emotion discrimination as compared to emotionally neutral utterances. Also, based on the comparative experimental analysis, the speech and EGG utterances of the proposed simulated emotion database are found to preserve the general trend in the excitation source characteristics (instantaneous F0 and strength of excitation parameters) for different emotions as that of the classical German emotion speech-EGG database (EmoDb). Finally, the emotion recognition rates obtained for the proposed speech-EGG emotion database using the conventional mel frequency cepstral coefficients and Gaussian mixture model based emotion recognition system, are found to be comparable with that of the existing German (EmoDb) and IITKGP-SESC Telugu speech emotion databases.