'Girlfriends and Strawberry Jam’: Tagging Memories, Experiences, and Events for Future Retrieval

In this short paper we have some preliminary thoughts about tagging everyday life events in order to allow future retrieval of events or experiences related to events. Elaboration of these thoughts will be done in the context of the recently started Network of Excellence PetaMedia (Peer-to-Peer Tagged Media) and the Network of Excellence SSPNet (Social Signal Processing), to start in 2009, both funded by the European Commission's Seventh Framework Programme. Descriptions of these networks will be given later in this paper

Machine Learning Based Twitter Sentiment Analysis and User Influence

The use of social media platforms, such as Twitter, has grown exponentially over the years, and it has become a valuable source of information for various fields, including marketing, politics, and finance. Sentiment analysis is particularly relevant  in social media analysis. Sentiment analysis involves the use of natural language processing (NLP) techniques to automatically determine the sentiment expressed in a given text, such as positive, negative, or neutral. In this research paper, we focus on Twitter sentiment analysis and identify the most influential users in a given topic. We propose a methodology based on machine learning techniques to perform sentiment analysis and identify the most influential users on Twitter based on popularity. Specifically, we utilize a combination of NLP techniques, sentiment lexicons, and machine learning algorithms to classify tweets as positive, negative, or neutral. We then employ popularity calculations for each user to identify the top 10 most influential users on a given topic. The proposed methodology was tested on a large dataset of US airlines tweets which is related to a specific topic i.e. airlines, and the results show that the approach can effectively classify tweets according to sentiment and identify the most influential users. We evaluated the performance of several machine learning algorithms, including Multinomial Naive Bayes, Support Vector Machines (SVM), Decision Trees, Gradient Boosting, logistic regression, AdaBoost, KNN and Random Forest, and found that the logistic regression algorithm has achieved the highest accuracy. The proposed methodology has several implications for various fields, such as marketing, where sentiment analysis can help companies understand consumer behavior and tailor their marketing strategies accordingly. Moreover, identifying the most influential users can provide insights into opinion leaders in a given topic and help companies and policymakers target their messages more effectively

深層学習に基づく感情会話分析に関する研究

Owning the capability to express specific emotions by a chatbot during a conversation is one of the key parts of artificial intelligence, which has an intuitive and quantifiable impact on the improvement of chatbot’s usability and user satisfaction. Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. Recently, many studies on neural emotional conversational models have been conducted. However, enabling the chatbot to control what kind of emotion to respond to upon its own characters in conversation is still underexplored. At this stage, people are no longer satisfied with using a dialogue system to solve specific tasks, and are more eager to achieve spiritual communication. In the chat process, if the robot can perceive the user's emotions and can accurately process them, it can greatly enrich the content of the dialogue and make the user empathize. In the process of emotional dialogue, our ultimate goal is to make the machine understand human emotions and give matching responses. Based on these two points, this thesis explores and in-depth emotion recognition in conversation task and emotional dialogue generation task. In the past few years, although considerable progress has been made in emotional research in dialogue, there are still some difficulties and challenges due to the complex nature of human emotions. The key contributions in this thesis are summarized as below: (1) Researchers have paid more attention to enhancing natural language models with knowledge graphs these days, since knowledge graph has gained a lot of systematic knowledge. A large number of studies had shown that the introduction of external commonsense knowledge is very helpful to improve the characteristic information. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. In this work, we employ an external knowledge graph ATOMIC to extract the knowledge sources. We proposed KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. The conversation is a sequence of coherent and orderly discourses. For neural networks, the capture of long-range context information is a weakness. We adopt Transformer a structure composed of self-attention and feed forward neural network, instead of the traditional RNN model, aiming at capturing remote context information. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets. (2) We proposed an emotional dialogue model based on Seq2Seq, which is improved from three aspects: model input, encoder structure, and decoder structure, so that the model can generate responses with rich emotions, diversity, and context. In terms of model input, emotional information and location information are added based on word vectors. In terms of the encoder, the proposed model first encodes the current input and sentence sentiment to generate a semantic vector, and additionally encodes the context and sentence sentiment to generate a context vector, adding contextual information while ensuring the independence of the current input. On the decoder side, attention is used to calculate the weights of the two semantic vectors separately and then decode, to fully integrate the local emotional semantic information and the global emotional semantic information. We used seven objective evaluation indicators to evaluate the model's generation results, context similarity, response diversity, and emotional response. Experimental results show that the model can generate diverse responses with rich sentiment, contextual associations

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

Regulating Mobile Mental Health Apps

Mobile medical apps (MMAs) are a fast‐growing category of software typically installed on personal smartphones and wearable devices. A subset of MMAs are aimed at helping consumers identify mental states and/or mental illnesses. Although this is a fledgling domain, there are already enough extant mental health MMAs both to suggest a typology and to detail some of the regulatory issues they pose. As to the former, the current generation of apps includes those that facilitate self‐assessment or self‐help, connect patients with online support groups, connect patients with therapists, or predict mental health issues. Regulatory concerns with these apps include their quality, safety, and data protection. Unfortunately, the regulatory frameworks that apply have failed to provide coherent risk‐assessment models. As a result, prudent providers will need to progress with caution when it comes to recommending apps to patients or relying on app‐generated data to guide treatment

Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education

This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA), for personalized and adaptive learning in higher education. The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform. This platform is engineered to reduce cognitive load on learners by providing easy access to information, facilitating knowledge assessment, and delivering personalized learning support tailored to individual needs and learning styles. The AIIA's capabilities include understanding and responding to student inquiries, generating quizzes and flashcards, and offering personalized learning pathways. The research findings have the potential to significantly impact the design, implementation, and evaluation of AI-enabled Virtual Teaching Assistants (VTAs) in higher education, informing the development of innovative educational tools that can enhance student learning outcomes, engagement, and satisfaction. The paper presents the methodology, system architecture, intelligent services, and integration with Learning Management Systems (LMSs) while discussing the challenges, limitations, and future directions for the development of AI-enabled intelligent assistants in education.Comment: 29 pages, 10 figures, 9659 word