Tracking public attitudes toward ChatGPT on Twitter using sentiment analysis and topic modeling

ChatGPT sets a new record with the fastest-growing user base, as a chatbot powered by a large language model (LLM). While it demonstrates state-of-the-art capabilities in a variety of language-generating tasks, it also raises widespread public concerns regarding its societal impact. In this paper, we utilize natural language processing approaches to investigate the public attitudes towards ChatGPT by applying sentiment analysis and topic modeling techniques to Twitter data. Our result shows that the overall sentiment is largely neutral to positive, which also holds true across different occupation groups. Among a wide range of topics mentioned in tweets, the most popular topics are Artificial Intelligence, Search Engines, Education, Writing, and Question Answering

Computational Modeling of Metaphor in Discourse

 Metaphor is used as a language resource/tool to better represent one’s point in communication. It can help achieving social goals such as illustrating attitudes indirectly. This thesis aims to understand metaphor from this social perspective in order to capture how metaphor is used in a discourse and identify a broad spectrum of predictors from the discourse context that contribute towards its detection. We build computational models for metaphor detection that adopt the notion of framing in discourse, a well-known approach for conceptualizing discourse processes. I claim that developing computational models based on this view paves the way for metaphor processing at the discourse level such as extended metaphor detection, and ultimately contribute to modeling people’s use of metaphor in interaction.  In order to model metaphor from this social perspective, we begin with corpus studies to observe people’s use of metaphor in three distinct domains where people use different metaphors for different purposes. This foundational work reveals how the layperson conception of metaphor differs from the technical operationalization of linguists from past work. The focus of our subsequent work is on metaphorical language that is recognizable as such by laypersons.  Next, we perform two case studies, which illuminate the value of metaphor detection in discourse, to explore situational factors that affect people’s use of metaphor. The first study investigates inner situational factors. We build logistic regression models to discover whether metaphor usage is influenced by three psychological distress conditions including PTSD, depression, and anxiety. Our annotation scheme allows separating effects on language choices of the three factors: contextual expectations, content of the message, and framing. Separating these factors gives us deeper insight into understanding people’s metaphor choice, and necessitates consideration of these factors in our next studies. The second study examines external situational factors. We investigate the influence of stressful cancer events on people’s use of metaphor. This study verifies the association between the cancer events and metaphor usage, and the effectiveness of the situational factor as a new type of predictor for metaphor detection.   Then, we build computational models for detecting metaphors that can be around related metaphors, not restricted in their syntactic positions. These models find topical patterns by leveraging lexical context, to explore how a metaphorical frame switch is distinguished from a literal one. We design, implement, and evaluate computational models of three kinds: (1) features of frame contrast, which capture lexical contrast around metaphorical frames; (2) features of frame transition, which capture topic transition patterns occurring around metaphorical frames; and (3) features of frame facets, which capture frame facet patterns occurring around metaphorical frames. We demonstrate that these three features in a nonlinear machine learning model are effective in metaphor detection, and discuss the mechanism through which the frame information enables more accurate metaphor detection in discourse.</p

Effects of the Curing Conditions on the Carbonation Curing Efficiency of Ordinary Portland Cement and a Belite-Rich Cement Mortar

In the present study, the efficiency of five different carbonation and/or water curing conditions on the properties of belite-rich cement mortar and ordinary Portland cement mortar was investigated. The hybrid curing of samples was carried out by submerging samples at different levels in water or in a lime-saturated solution kept under carbonation curing conditions. The compressive strength was measured to compare the physical properties of the cement mortars, and X-ray diffraction and thermogravimetric analysis results were analyzed to compare the physicochemical properties. The results revealed that the supply of additional moisture during carbonation curing tends to decrease carbonation curing efficiency and that the hydration products of cement paste are predominantly affected by the depth at which the specimen was immersed in the liquid rather than the type of liquid used

Characteristics of Preplaced Aggregate Concrete Fabricated with Alkali-Activated Slag/Fly Ash Cements

This study assesses the characteristics of preplaced aggregate concrete prepared with alkali-activated cement grout as an adhesive binder. Various binary blends of slag and fly ash without fine aggregate as a filler material were considered along with different solution-to-solid ratios. The properties of fresh and hardened grout along with the properties of hardened preplaced concrete were investigated, as were the compressive strength, ultrasonic pulse velocity, density, water absorption and total voids of the preplaced concrete. The results indicated that alkali-activated cement grout has better flowability characteristics and compressive strength than conventional cement grout. As a result, the mechanical performance of the preplaced aggregate concrete was significantly improved. The results pertaining to the water absorption and porosity revealed that the alkali-activated preplaced aggregate concrete is more resistant to water permeation. The filling capacity based on the ultrasonic pulse velocity value is discussed to comment on the wrapping ability of alkali-activated cement grout

T³-Vis : a visual analytic framework for Training and fine-Tuning Transformers in NLP

Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model’s intrinsic properties and behaviours. Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence. Case studies and feedback from a user focus group indicate that the framework is useful, and suggest several improvements. Our framework is available at: https: //github.com/raymondzmc/T3-Vis.Science, Faculty ofNon UBCComputer Science, Department ofReviewedFacultyResearcherGraduat

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has impacted people's lives, it is essential to capture how people react to public health interventions and understand their concerns. We aim to investigate people's reactions and concerns about COVID-19 in North America, especially in Canada. We analyzed COVID-19-related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people's sentiment about COVID-19-related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, "vaccines," "economy," and "masks") and 60 opinion terms such as "infectious" (negative) and "professional" (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19-related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions