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Articles 1 - 5 of 5
Full-Text Articles in Computational Linguistics
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
MSU Graduate Theses
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 …
Cell Phone Ethnography: Mixed Methods And The Brand Consumer Relationship, Robert Nathaniel Dove
Cell Phone Ethnography: Mixed Methods And The Brand Consumer Relationship, Robert Nathaniel Dove
Masters Theses
Overall, the goal of this study is to identify and differentiate the various motivations and cultural influences that can be used to explain consumer behavior. In doing so, this study hopes to facilitate the development of new and innovative marketing strategies, providing a new research design for the ethnographer’s toolkit. More importantly, this model can give shape to new constructs and new variables for further empirical testing in the field through quantitative and qualitative methods. By blending the two approaches, using qualitative interpretive anthropological analysis by field study with quantitative sentiment analysis adapted from market researcher Jeffery Breen’s (2012) methodology, …
Using Textual Features To Predict Popular Content On Digg, Paul H. Miller
Using Textual Features To Predict Popular Content On Digg, Paul H. Miller
Paul H Miller
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 …
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 …