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Articles 1 - 5 of 5
Full-Text Articles in Computational Linguistics
Understanding Perceptions Of A Peruvian Local Market Program: A Reflexive Thematic Analysis, Rosmery Ramos-Sandoval Dr., Jano Ramos-Diaz
Understanding Perceptions Of A Peruvian Local Market Program: A Reflexive Thematic Analysis, Rosmery Ramos-Sandoval Dr., Jano Ramos-Diaz
The Qualitative Report
Despite growing global interest in short food supply chains (SFSCs), little is known about how consumers in developing countries perceive these models, especially through digital platforms like social media. This study investigates how Twitter users represent and perceive SFSCs in the context of the Peruvian government´s “De la Chacra a la Olla” program. The study analyzed 1,167 tweets from Peruvian Twitter users referencing the hashtag #DeLaChacraALaOlla between 2014 and 2020, using reflexive thematic analysis within an exploratory case study framework to examine consumer perceptions of SFSCs. The analysis revealed three key themes in consumer perceptions of SFSCs on Twitter: direct …
Characterizing Language Use In Online Accessibility Discussion Forums, Nithiya Venkatraman, Anand Ravi Aiyer, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok
Characterizing Language Use In Online Accessibility Discussion Forums, Nithiya Venkatraman, Anand Ravi Aiyer, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok
Computer Science Faculty Publications
Discussion forums are one of the favored platforms for knowledge sharing. Given their popularity, copious research exists on understanding the linguistic and behavioral characteristics of forum conversations, so as to inform the design of many downstream applications including discourse visualization, sentiment analysis, and question answering. However, prior investigations have mainly focused on general forums designed primarily for sighted users, and as such the applicability of their findings to dedicated accessibility discussion forums frequented by blind screen reader users remains unanswered. To bridge this knowledge gap and facilitate the development of better-informed assistive technologies for blind people, we investigated language use …
A Sentiment Analysis Of "Filipinx" On Twitter Using A Multinomial Naïve Bayes Classification Model, Clarisse Taboy
A Sentiment Analysis Of "Filipinx" On Twitter Using A Multinomial Naïve Bayes Classification Model, Clarisse Taboy
Dissertations, Theses, and Capstone Projects
On social media, the use of “Filipinx” as a gender neutral, inclusive term for “Filipino” tends to generate high user engagement, at times without regard for the original context in which the word appears. This project applies computational methods to collect a large dataset in English/Filipino from Twitter containing “Filipinx”, and to train a Naïve Bayes model to classify tweets into three sentiments: positive, neutral, and negative. My methodology takes inspiration from that of four related studies that similarly conducted sentiment analysis on English/Filipino tweets involving various topics, and whose resulting accuracy scores were compared side-by-side. Conducting sentiment analysis on …
Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat
Evaluation Of Different Machine Learning, Deep Learning And Text Processing Techniques For Hate Speech Detection, Nabil Shawkat
Graduate Theses/Dissertations
Social media has become a domain that involves a lot of hate speech. Some users feel entitled to engage in abusive conversations by sending abusive messages, tweets, or photos to other users. It is critical to detect hate speech and prevent innocent users from becoming victims. In this study, I explore the effectiveness and performance of various machine learning methods employing text processing techniques to create a robust system for hate speech identification. I assess the performance of Naïve Bayes, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and K Nearest Neighbors using three distinct datasets sourced from social …
Using Textual Features To Predict Popular Content On Digg, Paul H. Miller
Using Textual Features To Predict Popular Content On Digg, Paul H. Miller
Department of English: Dissertations, Theses, and Student Research
Over the past few years, collaborative rating sites, such as Netflix, Digg and Stumble, have become increasingly prevalent sites for users to find trending content. I used various data mining techniques to study Digg, a social news site, to examine the influence of content on popularity. What influence does content have on popularity, and what influence does content have on users’ decisions? Overwhelmingly, prior studies have consistently shown that predicting popularity based on content is difficult and maybe even inherently impossible. The same submission can have multiple outcomes and content neither determines popularity, nor individual user decisions. My results show …