Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Emotion and Adaptation

Nowadays, with the development of internet technology and electronic commerce, the Web storages huge number of product reviews comment by customers. Product reviews tend to be more objective in reflecting the real situation of the product, more and more customers post product reviews at merchant websites in order to make an informed choice. However, a large number of reviews made it difficult to track the comments and suggestions that customers made. In this paper, a fine-grained emotional element detection and emotional tendency judgment method based on conditional random fields (CRFs) and support vector machine (SVM) was proposed. This model introduces semantics and word meaning in CRF model to improve the robustness. In SVM model, deep semantic information imported based on neural network to improve the traditional bag of word model. Experimental results show that the proposed model with deep features efficiently improved the F-Measure.

/pdf/fine-grained-emotion-elements-extraction-and-tendency-z9q29lh635.pdf

Fine-Grained Emotion Elements Extraction and Tendency Judgment Based on Mixed Model

This paper proposes an novel approach to annotate function tags for unparsed text. What distinguishes our work from other attempts in such task is that we assign function tags directly basing on lexical information other than on parsed trees. In order to demonstrate the effectiveness and versatility of our method, we investigate two statistical models for automatic annotation, one is log-linear maximum entropy model and the other is margin maximum based support vector machine model, which achieve the best F-score of 82.8 and 86.4 respectively when tested on the text from Penn Chinese Treebank. We also quantity the effect of POS tagger accuracy on system performance. Our results indicate that the function tag types could be determined via flexible and powerful feature representations from words, POS tags and word position indicators, and that, similarly to syntactic parsing, the main difficulty lies in complex constituents with long-distance dependency.

Exploiting lexical information for function tag labeling

， †† † Summary A pragmatic emotional model is one of obligatory subjects for building a humanoid computer This paper presents a conception of a Mental State Transition Network Model using psychological research for reference [1] The ongoing work proposes a novel approach to detect human emotions According to Pluchik’s basic emotional classification, we defined nine basic emotional states and carried out a series of psychological investigations [2] By the results of experiments we provide a new way to predict coming human’s emotions depending on the various currents emotional states under various reinforcements By means of statistic data derived from 227 questionnaires, the transitions in distribution among the emotions and relationships between internal mental situations and external stimuli are concluded The high precision achieved in our evaluating experiments shows the model is helping in recognition and synthesis of human emotion

/pdf/a-mind-model-for-an-affective-computer-w6msuewiwv.pdf

A Mind Model for an Affective Computer

With development of the recent globalization, the chance to exchange in English increased in the business field. In particular, it's necessary to write a thesis and a charter handwriting in English. Because many Japanese are not used to making English sentence, it is a great burden to write appropriate sentence in English without any support for creating English sentence. In this study we have developed an English composition support system. By this system, it's to search for the interlinear translation example to refer to by database and generate a new sentence by replacing a noun in the example sentence. In this paper, based on the technique of Super-Function, we propose a method to convert an affirmative sentence into negative sentence and vice versa to realize more flexible and extensive text conversion.

Flexible English writing support based on negative-positive conversion method

In Supervised-learning of emotions from human language, to keep the emotion labels balanced in the training set is a challenging task since emotion labels are highly biased in raw data of human language. In this paper, we propose a novel method based on active learning to partially inhibit the polarization of text samples with more frequently observed emotion labels for constructing the training set, and to encourage the selection of samples with less frequently observed emotion labels. For each batch of unlabeled samples, the selected samples by our approach are given the ground truth emotion labels from human experts before they are merged to the training data. Our experiment of multi-label emotion classification on Chinese Weibo messages suggests that the proposed method is effective in constructing the label-balanced training set for text emotion classification, and the supervised text emotion classification results have been steadily improved with such training set.

Fuji Ren

Papers

Fine-Grained Emotion Elements Extraction and Tendency Judgment Based on Mixed Model

Exploiting lexical information for function tag labeling

A Mind Model for an Affective Computer

Flexible English writing support based on negative-positive conversion method

Building Label-Balanced Emotion Corpus Based on Active Learning for Text Emotion Classification