Spring 2024
My vision is a model which could reference the major (at least American, but potentially other English) style guides (the AP Stylebook, Chicago, MLA, etc.) and, given a sentence with an point of ambiguous grammar/style, could give the solution according the different major style guides. I'm not married to this idea per se, but I like the idea of working with style guides in some way.
I'd like to try and build a sentiment analysis tool, capable of classifying emotions into positive, neutral, or negative (or perhaps give a numerical rating). If possible, the model can be expanded to include other emotions like happiness, anger, disappointment, etc.
Looking at words used on social media and comparing sentiments across different social platforms.
For the team project, I want to use NLP skills to catch keywords in some math papers. From my own experience, math research papers are hard to understand due to their nouns and abstract ideas. If we can get some key things out from the math paper, this will be helpful.
N/A
For the team project at the end of the semester, I would like to build a Sentiment Analysis model for Cryptocurrency Trading. In the world of Crypto, many of the price movements are caused by sudden changes in the sentiment of investors. This often can be found on social media sites like Reddit and Twitter, where users often post their feelings about the currencies. A few years ago, a subreddit called WallStreetBets blew up for causing an insane price increase in Gamestop stock as well as some others. From this, I discovered the power of using Sentiment Analysis modeling on various social media sites to attempt to predict price movements of online currencies and stocks. For this project, I would like to analyze user posts on these social media sites to calculate the overall sentiment for specific Cryptocurrencies. I will then use this data to predict the incoming price changes that may occur.
My project idea involves training a large language model on countless recipes, found online, which must be preprocessed accordingly to be usable. Once the chatbot is trained, a simple web platform would be used for accessibility and testing. This platform would be comprised of chats, and an area to enter text. The goal is to make a chatbot capable of creating usable recipes based on user inputs, dietary restrictions, and ingredient restrictions. I thought of this project idea with Dylan Parker.
Concept and Idea: Spam Detection Bot for Email Systems The potential concept for our team project is to develop an advanced Spam Detection Bot that is specifically designed for email systems.
Intelligent Linguistic Analysis: The bot will utilize natural language processing (NLP) techniques to analyze the content of emails. It will focus on identifying linguistic patterns, keywords, and phrase structures commonly associated with spam.
Machine Learning Integration: By employing machine learning algorithms, the bot will be able to learn and adapt to evolving spam tactics. This continuous learning approach ensures that the bot remains effective even as spammers change their strategies.
Feedback Mechanism: The bot allows the users to manually mark certain email as spam or not, which allows the bot to update itself to increase the accuracy of detection and reduce the possibility of false detection.
Real-time Processing and Efficiency: The bot is designed to process emails in real-time, ensuring that users' inboxes are promptly cleared of spam.
Security and Privacy Focus: In addition to spam detection, the bot will prioritize user security and privacy, ensuring that the user’s privacy is always the top priority.
I would like to make a model that can read influential people's tweets in the financial world to get some sort of sentiment, and determine if someoen should invest in that stock or not.
ex. If Warren Buffett were to tweet out "Apple is a terrible company", the model would be able to detect the sentiment of Apple as negative, and therefore not invest in the stock.
I want to create a language identifier that can output the name of a language based on a text of a certain language as the input. To make this work I believe that we would first identify the types of characters used and narrow down the languages based on that. Secondly, we would tokenize the text into words and compare it to the dictionaries of each language we are still considering. Lastly, we would look at how many matches there are between the text and the dictionaries and output the most similar one. Maybe if it's below a certain threshold, the output could say that there's not enough data to suggest that the language is one we have access to.
For the project, I'm thinking about an AI diary app using GPT. This app will let students write about their day, and the AI will offer encouraging words and advice. It could also detect if the student is stressed and help them as a friend. The goal is to create a comforting space for students to reflect and relax.
Project Idea I would like to study the difference between the usage of “(disability-adj) person” and “person with (disability)” in the context of academic papers. For example, there is lots of discourse on the difference between saying autistic person or person with autism, and from my own experience on reading research papers about autism, both are used frequently. The project could involve gathering data about its usage frequency in scientific papers between academic fields (medical, psychology, sociology, anthropology, etc.), within the same papers (do research papers always use the same term, or do some use both?), by date (is there a difference in pre- and post- 2015, or some other relevant date?), or some other metric. Further work could be done with sentiment analysis technology to see if the papers use language that is favorable / amiable towards disability or disparaging towards disability, and could be correlated with their chosen phrase to describe disability (disabled person vs person with disability) to see if there is a significant difference.
I just switched into this class and haven't attended any lectures yet so I don't have a good concept of what a good project is, but an idea is to analyze tik toks to gauge that population's opinions on the 2024 election.
I have an plan to do a text analyze on the answer in QUORA to distinguish answer from experts and other participants.
One idea for a project which I might like to pursue would be an AI coach for video games. The idea would be to have the coach look at in game performance, potentially in real time, and give coaching about how to improve ala "pay more attention to objectives".
Something that may be challenging to do as a team project is to look at computational linguistics based on languages other than English. An alternative that would fall within the scope of the class while also looking at nonstandard English would be analyzing various English dialects, with projects such as dialect identification, translation between dialects, or even a chatbot that understands multiple English dialects.
For the group project, I am interested in sentiment analysis, specifically concerning evaluations of business products, or social media posts. Additionally, I am also inclined towards topics associated with the detection of spam emails or messages.
I am fairly open to the type of team project I would like to pursue this semester. Things such as sentiment analysis, story generation, and chatbots would all be interesting to me. I am not sure about the feasability, but the project I would be most interested in working on would be a text to speech tool. I would like to learn how speech generation works on such a level, and it is a tool I could imagine using once created. I know that this type of tool already exists, so I would ideally like to work on a less prevalent aspect such as adding emotionally expressive speech so that it doesn't sound as flat. I am also interested in the ways that characters or celebrities voices can be emulated, but I am not sure if this would have any legal hurdles.
N/A
I have thought of working on a sentiment analysis model which would classify customer review we see on e-commerce platform(whether they are positive, negative, or neutral). However, I also want to listen to other ideas so I am also open to new ideas.
Project Idea: Using AI for recipe recommendation, modification, and generation. Opening the fridge often reveals leftover ingredients from previous meals, which can make deciding what to cook a challenge. Typically, we end up spending considerable time looking for recipes that match our tastes and the ingredients we currently have. It’s common to find that we’re missing a few items for the recipes we’re interested in, which can be quite inconvenient. However, by incorporating artificial intelligence, we could have a tool that acts like a consultative chef. This AI system would allow us to tailor our recipe searches more precisely, adjusting recipes to fit our personal requirements, like creating a low-calorie version of a certain dish or suggesting substitutions for missing ingredients.
I am very unsure of what kind of project I would like to pursue. I think combining linguistics with visual data could be interesting though. Maybe some kind of coding that sorts or filters certain types of words or phrases, and then those are represented in some visually pleasing way. I think some kind of code that analyzes literature at a highly specific linguistic level could be very interesting. I am extremely flexible and would be willing to work on almost any project.
A possible project could be to use NLP techniques on unconventional data. Personally, I am interested in cross-cultural linguistics / multilingualism such as heritage language usage in diasporic settings. Some possible datasets could be English loan word usage in a non-English speaking countries (ie. in everyday life, music lyrics, social media, websites). For example, in South Korea and many other countries, popular music integrates English into various text such as music lyrics, advertisements and marketing, and everyday speech (based on age group). Another possible topic could be topics such as correcting gendered language to non-gendered language. Lastly, academic related topics: I think a fun project could be something like predicting final course grade based on a student writing sample, predicting / generating potential test questions based on a text, or predict / generate weak areas of students based on their code sample.
A text summarization tool for simplifying complex readings for classes.
I have a group that I believe we will be working together, yet we have not yet decided on a topic for a project. An idea I have been thinking about is incorporating a character into game that will take the language input that the player puts in. After analyzing the type of writing that the players uses, the character will respond in the same way the player wrote. For example, if someone is using Shakespearean language, then the character will respond back the same way.
Project Idea: Explain Attention Is All You Need to children: Design a system that explains and summarizes academic papers in a more comprehensive way, especially for those who do not have much background knowledge. It can reliably lay out the fundamental information from abstracts, introductions, methods, and findings from any academic paper without missing vital information, allowing readers to process the main ideas efficiently.
Though I still don't know much about NLP, for my team project, I think it would be interesting to try to work on a language model that is trained and works solely on inputs with perfect grammar in an attempt to see the effect of input "sanitization" on performance and model size.
I'd like to apply NLP algorithms and some machine learning algorithms on a public available dataset to perform supervised classification task. For example, applying MLP on product review to distinguish helpful reviews against unhelpful ones. I would like to further compare and evaluate the performance of some large language models, such as Bert and GPT4 API.
My idea is to make an "PolyGlotBot" which is a multilingual virtual assistant that helps Chinese learners practice and improve their skills. The model will give real-time feedback on grammar, pronunciation, and vocabulary when having the conversation with it. It will also have the interior function which adapts the user's proficiency level and personalizes learning content. This can help leaners learn better on his own rythm and on his own level.
I'm not sure but I am open to exploring
Project Idea: Create a tool that can figure out how people feel when they post on social media, no matter what language they're using. The idea is to make a system that helps us see the emotions behind online conversations in different languages, so users and businesses can get a sense of what people are expressing on a global scale.
I would like to learn how could NLP techniques be applied to alternative data in finance and business.
Utilization of sentiment analysis would be something I'd be interested in working on; a project like conducting sentiment analysis on a dataset (such as poems) and using it to generate new data (such as new poems based on a key emotion) is something that I would be interested in. In addition, I would also be interested in leveraging LLMs to create something, such as recreating the personality of characters.
The team project that I have in mind is an LLM that recommends personalized recipes based on user requests, dietary restrictions, and ingredient availability. The system could also assist users in meal planning by suggesting balanced meal combinations and creating shopping lists if they do not have the necessary ingredients. My team would ideally collect many recipes online for our database to serve as the foundation for our recommendations. An algorithm would need to be developed for the meal recommendation system. We would then create a web-based application for user interactions with our system. If possible, it would be great to have a user "account" feature for further user personalization in the future. I thought of this project idea with Marcus Cheema.
Text Writing Editor: Use models like OpenAI's Davinci API for generating creative writing, including poetry, stories, or even scripts. The focus would be on fine-tuning the model's parameters and prompts to allow certain styles or themes.
GitHub repository link: Team project: I'd like to know more about sentiment/emotion/opinion analysis of text. This semester I'd probably do something related to analysis techniques regarding sentiment lexicons and sentiment classification models.
N/A
N/A
The team project that we would like to pursue is to use ai (chatGPT) to train specifically for a task, such as writing, research, programming. For example, there is a plugin function in Canva supported by ChatGPT that is able to generate infographics and visualizations automatically through prompts.
N/A
I am interested in doing a project about sentiment analysis, where a model would be able to decide what the tone of a piece of text is. This is intriguing to me because even as people, tone is difficult to convey accurately through text, and even when we are able to determine the sentiment of a piece of text, it is not always clear exactly how that tone was communicated. I am curious to see how accurate a machine can be in determining something so emotion-based and not clear-cut. With that said, I am open to other project ideas as well!
N/A
Two idea possibilities: 1)Sentiment Analysis Chatbot: A chatbot that detects the emotional and state of mind-being from a person, the idea behind this we are LARPing as Walmart or Target or another retailer, and we want to detect how the customer feels without having them directly state it (because that tends to anger them). Put it like this, if you're disappointed with either customer service or a product you just purchased, and you write to a bot wanting to express disappointment, but is instead asked how you feel about the service or product, that will just annoy you into stating you are angry or upset. The key to this tool is being able to interpret word choices and convert them into state of emotional well-being and satisfaction from a state of 1-5, 1 being extremely unsatisfactory, and 5 being extremely satisfactory, and utilizing this metric across 5 rubrics (Customer Service, Product Quality, Cleanliness of Stores, Location Convenience, Feeling of Safety) from a short conversation with the consumer. Along the way, the bot can also make recommendations, including for products and advices, after the conversation. 2)Spam Detection Bot: Email filter bot that would combine ANN with corpus of common spam emails, including ones I've fallen for (I've failed 100% of the Emory phishing emails, I'm sad to say, not an exaggerated stat, I've never not clicked on those baits). We'd create a distinction between actual legitimate bot emails (i.e. Job offers, important notifications, reminders) from both harmless and harmful spam. Differs from email spam on multi-lingual factor: my emails still have large amounts of non-english spam that filter through, but because the spam bot isn't as well-trained in that factor, I'm receiving garbage on fake chinese job referrals and german job offers (I know they're fake, because like the genius that I am, clicked on them and inquired, which resulted in me getting more spam emails). The spam detection bot would also create warnings on non-spam emails that border on spam (i.e. promotional emails), and offer the end user the opportunity to enable options to filter them out.
Team Project Idea: Examine presidential inaugural addresses for in word tokens, types, diversity, etc. More points of analysis will definitely come up, but in general hope to look for different trends over time, individual differences between presidents, and other interesting observations.
Our team would like to develop a system that can analyze and classify the sentiment of social media posts. We will choose one social media platform and focus on posts from a certain period or around a specific topic. Our goal is that the system can help businesses, organizations, and governments understand the public reaction and adjust policies/improve products.
N/A
Project Idea: I aim to analyze the linguistic nuances and sentiment differences in academic papers when referring to "people with disabilities" vs. "disabled people". I hope to understand if the choice of terminology correlates with varying sentiments and contextual frameworks in disability discourse within academic literature.
N/A
Our team would like to develop a system that can analyze and classify the sentiment of social media posts. We will choose one social media platform and focus on posts from a certain period or around a specific topic. Our goal is that the system can help businesses, organizations, and governments understand the public reaction and adjust policies/improve products.