Stress Wave Propagation through Cohesive Soil

Generally, there exist an inelastic deformation and an energy dissipation during the stress wave propagation through cohesive soil. In order to describe these characteristics, the authors proposed the constitutive equation of normally consolidated clay. The phenomenological nature of the parameters involved in the stress-strain relation was investigated in detail by using the triaxial test results and the stress wave propagation test results. In these test the pore water pressure was measured and its value was compared with the calculated result by using the constitutive equation of clay. As a result, the proposed stress-strain relation was very effective for interpreting the behavior of cohesive soil

Analysis of Information Spreading by Social Media Based on Emotion and Empathy

The number of social media users has increased exponentially in recent times, and various types of social media platforms are being introduced. While social media has become a convenient communication tool, its use has caused various social problems. Some users who cannot imagine the emotions their posts may induce in readers cause what is termed as “the flaming phenomenon.” In some cases, users intentionally repeat strong remarks for self-advertisement. To identify the cause of this phenomenon, it is necessary to analyze the posted contents or the personalities of the users who cause the flaming. However, it is difficult to reach a generalized conclusion because each case varies depending on the circumstances and individual. In this chapter, we study the phenomenon of information spreading via communication on social media by conducting a detailed analysis of replies and number of retweets in Japanese, and we reveal the relation between the feedback on such posts and the emotions or empathy they result in

Time-Series Analysis of Video Comments on Social Media

In this study, we propose a method to detect unfair rating cheat caused by multiple comment postings focusing on time-series analysis of the number of comments. We defined the videos that obtained a lot of comments by unfair cheat as ‘unfair video’ and defined the videos which obtained without unfair cheat as ‘popular video’. Specifically, our proposed method focused on the difference of chronological distributions of the comments between the popular videos and the unfair videos. As the evaluation result, our proposed method could obtain higher accuracy than that of the baseline method

Relations between Sleep Time and SNS Texts

Sleeping habits are one of the major issues in today’s healthcare. In this paper, we consider the problem of analyzing sleeping habits of people using social networking service (SNS) texts. As the first step toward predicting user’s sleeping time using SNS texts, we assume that the time span between the user’s last post in one day and the first post the next day can be used as a pseudo-indicator for the user’s sleeping time if the user posts the text sufficiently frequently. We call such tweet time spans “pseudo-sleeping time” if the first tweet of the next day include “Good morning” or similar words. We try to predict such pseudo-sleeping time using the text (tweet) of the preceding tweet (i.e., the last tweet of the day). Preliminary experiments show that the tweet text contains some useful information to predict the user’s pseudo-sleeping time

Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts

This paper propose a method to predict the stage of buzz-trend generation by analyzing the emotional information posted on social networking services for multimodal information, such as posted text and attached images, based on the content of the posts. The proposed method can analyze the diffusion scale from various angles, using only the information at the time of posting, when predicting in advance and the information of time error, when used for posterior analysis. Specifically, tweets and reply tweets were converted into vectors using the BERT general-purpose language model that was trained in advance, and the attached images were converted into feature vectors using a trained neural network model for image recognition. In addition, to analyze the emotional information of the posted content, we used a proprietary emotional analysis model to estimate emotions from tweets, reply tweets, and image features, which were then added to the input as emotional features. The results of the evaluation experiments showed that the proposed method, which added linguistic features (BERT vectors) and image features to tweets, achieved higher performance than the method using only a single feature. Although we could not observe the effectiveness of the emotional features, the more emotions a tweet and its reply match had, the more empathy action occurred and the larger the like and RT values tended to be, which could ultimately increase the likelihood of a tweet going viral

Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only

Emotional Similarity Word Embedding Model for Sentiment Analysis

We propose a method for constructing a dictionary of emotional expressions, which is an indispensable language resource for sentiment analysis in the Japanese. Furthermore, we propose a method for constructing a language model that reproduces emotional similarity between words, which to date has yet not been considered in conventional dictionaries and language models. In the proposed method, we pre-trained sentiment labels for the distributed representations of words. An intermediate feature vector was obtained from the pre-trained model. By learning an additional semantic label on this feature vector, we can construct an emotional semantic language model that embeds both emotion and semantics. To confirm the effectiveness of the proposed method, we conducted a simple experiment to retrieve similar emotional words using the constructed model. The results of this experiment showed that the proposed method can retrieve similar emotional words with higher accuracy than the conventional word-embedding model

Construction and Expansion of Dictionary of Idiomatic Emotional Expressions and Idiomatic Emotional Expression Corpus

Objective: In the study of sentiment estimation from language, methods focusing on words, phrases, sentence patterns, and sentence-final expressions have been proposed. However, it is difficult to deal with a wide variety of emotional expressions by only assigning emotions to words and phrases. In particular, it is difficult to analyze metaphorical expressions and idiomatic expressions on a word-by-word basis, and it is impossible to register all expressions in a dictionary because new expressions can be created by flexibly replacing words. However, it is difficult to determine the constraints on the words to be replaced, and not all expressions can be registered in the dictionary as sentence patterns. Methods: In this paper, we construct a dictionary of idiomatic sentiment expressions, which contains idioms expressing emotions. In this paper, we construct a pseudo-emotional corpus by collecting utterances containing emotional idioms from social media and automatically assigning emotions expressed by the idioms. Results: This corpus includes expressions other than idioms, and can be an effective resource for estimating emotions in sentences that do not contain idioms. In this study, we create an emotion estimation model for utterances based on the constructed corpus, and conduct evaluation experiments to explore the problems of the idiomatic emotion corpus. In addition, using the constructed sentiment corpus, we investigate how to expand the dictionary of sentiment expressions in idiomatic phrases by using deep learning methods. Conclusion: Using the corpus of idiomatic sentiments constructed by the proposed method as training data, models with and without idioms were constructed by machine learning models. The results show that the F-values of all emotions with idioms exceed 0.8. On the other hand, when idioms were not included, the F-values tended to decrease overall. However, the F-values of emotions such as "shame" and "excitement" were around 0.7, indicating that the characteristics of emotional expressions other than idioms were expressed