NLP in Action: Network Modeling, Sentiment Analysis, and Topic - Analysing ChatGPT Topics

This project presents a methodology for analyzing the communities, topics, and sentiments of users from diverse backgrounds in social media posts regarding ChatGPT, a new artificial intelligence technology. The methodology involves three steps: exploratory data analysis to understand the dataset’s dimensions, attributes, and distribution of features; network analysis; and text analytics methods, including sentiment analysis, information extraction, and topic modeling. The main objective is to determine the overall sentiment towards ChatGPT and related topics and the most frequently used words and topics in tweets mentioning ChatGPT. The project also explores the most controversial topics related to ChatGPT and how companies can benefit from implementing it in their business strategies. The study results reveal that ChatGPT is a controversial topic among different types of users and is not limited to a particular field of technology. Finally, the project provides detailed findings on the network, communities, sentiments, and topics associated with ChatGPT.

ChatGPT is one of the hottest topics [1] and one of the newest artificial intelligence technologies, not only in the IT industry but all industries as it could potentially impact many jobs – if not eliminate them forever. Therefore, gaining a better understanding of the sentiment of users from diverse backgrounds in social media posts regarding ChatGPT, and analyzing the network of individuals and entities discussing it, such as influencers, large tech companies, news agencies, and other players, could assist us in examining the concept of Artificial Intelligence from a scientific and statistical perspective.

To achieve that goal, we followed a methodology that involves three steps:

1. Begin with preliminary and exploratory data analysis to understand the dataset’s dimensions, attributes, and distribution of features such as reply, retweet, like, quote, and hashtag counts.

2. Perform trivial feature engineering and generate a network of all tweets to conduct fundamental network analysis, including shortest path, centrality analysis, clustering coefficient, modularity analysis, and heavy-tail analysis.

3. Experiment the tweets with text analytics methods including sentiment analysis, information extraction, and topic modeling, to find the best answer for this project purpose.

Our main objective in this project is to conduct sentiment analysis and topic modeling to determine the overall sentiment towards ChatGPT and related topics, as well as the most frequently used words and topics in tweets mentioning ChatGPT. Additionally, we will analyze the types of posts that received the highest number of reactions and the most divisive topics among users and also shedding light on the network and communities more involve in this new technology.

Furthermore, we will explore the most controversial topics related to ChatGPT and how companies can benefit from implementing it in their business strategies. We will also investigate the commonly used hashtags and the most engaged companies in posts, potentially affecting their stock prices or their users' sentiments. Additionally, we aim to analyze the types of posts that received the most engagement, the most divisive topics among users discussing ChatGPT, commonly used hashtags, and the most effective techniques for analyzing tweets.

Despite the considerable number of articles on ChatGPT and its impact on educational performance, our research has revealed that there is currently only one paper that specifically examines ChatGPT using a Twitter dataset [2]. However, it is worth noting that numerous studies focus on network analysis, topic modeling, and sentiment analysis based on Twitter posts and datasets, some of which have objectives similar to our study [3]–[5]. Therefore, we investigated some other articles with regard to Twitter and the methods that were utilized to analyze the tweets around a particular concept other than ChatGPT. In the following lines, we go through the mentioned ChatGPT paper, as well as the other ChatGPT alike research with Twitter data.

1. Exploratory Data Analysis (EDA)

2. Network analysis

3. Sentiment & Text analysis

1. Data Understanding

2. Exploratory Data Analysis

3. Network Analysis

4. Sentiment Analysis

5. Text Analytics

6. POS Tagging

7. Topic Modeling

8. Named Entity Recognition

Future research could focus on analyzing a larger dataset with more posts about ChatGPT or analyzing posts at different intervals to avoid the effect of topics by news at a particular time. As the study found that most tweets were in English, Japanese, and Spanish, future research can explore the impact of language barriers on the spread of the ChatGPT topic and how it can be made more accessible to speakers of other languages. Additionally, studying the impact of ChatGPT on specific industries such as healthcare, finance, and education could provide deeper insights into its implications on society. Understanding the ethical and legal considerations could also be explored further. These research areas could contribute to a better understanding of the potential and challenges of ChatGPT's integration into society.

Overall, the methodology used in this project provided insights into the data features of the dataset, the distribution of the features, and the sentiment towards ChatGPT. Therefore, the findings in this project could be interpreted as far as society's mainstream opinion on ChatGPT and its consequences in the modern world. Of course, our dataset was limited to a specific period, and our methodology covered only some aspects. For example, Cascading (propagation model) is one of the experiments that was beyond the scope of our project and could