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Twitter-Sentiment-Analysis-about-ChatGPT

A quantitative study on over 1.25 million tweets about ChatGPT, employed data scrapping, data cleaning, EDA, topic modeling, and sentiment analysis.

TABLE OF CONTENT

BACKGROUND

ChatGPT is an artificial intelligence chatbot developed by OpenAI and launched in November 2022. It is built on top of OpenAI's GPT-3 family of large language models and has been fine-tuned (an approach to transfer learning) using both supervised and reinforcement learning techniques. Given the advantages of ChatGPT over traditional chatbots, ChatGPT has attracted more than 1 million users in 5 days and 100 million users in 2 months after it was launched, leaving behind other popular online platforms such as Netflix, Facebook, and Instagram in terms of adoption rates. Some early adopters of ChatGPT believe that it will eventually obsolete several professions related to content creation. it has been demonstrated that ChatGPT is capable of producing high-quality responses to a variety of challenges, including solving coding challenges and generating accurate responses to exam queries.

OBJECTIVE

Using a mixed-method approach, analyze tweets from December 2022 to January 2023 that mention ChatGPT and express diverse and unstructures opinions. Identify the main topics and sentiments of the conversations and examine perception of early ChatGPT users. We assert this identification will allow us to understand and assess ChatGPT's capability, effectiveness, and facing challenges.

Research Questions

  • RQ1 What are the profile characterisitics of ChatGPT early users?
  • RQ2 What are the dominant topics that emerge from the tweets about ChatGPT?
  • RQ3 What are the sentiments that are associated with the tweets about ChatGPT?

METHODOLOGY

TOOLS

Task Technique Description Tools/Packages Used
Data Collection Scraping tweets from Twitter snscrape
Data Preprocessing Duplication removal, lowercasing, noise removal (punctuation, stopwords, URLs, @users), lemmatization re, NLTK, pandas, numpy
Feature Engineering Retrieving geographical info from a user's profile location; retrieving datetime info from tweet timestamps geopy, datetime
Topic Modeling Identifying topics using the Latent Dirichlet Allocation (LDA) modelling pyLDAvis, gensim
Sentiment Analysis Quantitative sentiment analysis of each topic via rule-based and deep learning based model VADER, roBERTa, scipy, torch
Data Visualization Multi-attribute plots matplotlib, seaborn, wordcloud, PowerBI
Environments & Platforms Google Colab, Databricks, Pyspark, Jupyter Notebook, Twitter

DATA-COLLECTION

Method Notes
Tweepy 3200 tweets; no historical data
GetOldTweets3 Twitter has removed the endpoint the GetOldTweets3 uses
Twint Twitter throws a more strict device + IP-ban after a certain amount of queries
snscrape Scrapped 1.25M tweets - 832,924 English tweets

Data Collection: Identifying ChatGPT Content

  • Package used: snscrape
  • Language: English
  • Keywords: ChatGPT
  • Timeframe: December 1, 2022 to January 31, 2023
  • Features: User ID, User Name, User Verification, User Location, User Followers, Tweet Text, Posted Timestamp, and Posed Language
  • Number of tweets collected = 1,255,518
  • December - 474,572 tweets | January - 780,946 tweets

    • DATA-PREPROCESSING

    Data Cleaning

    • Merged collected datasets into a single dataframe and removed duplicate tweets.
    • Dropped 17 tweets that were missing timestamp values.
    • Filled missing values in 'Location' column with the term "Unknown".

    Feature Engineering

    • Utilized the geopy package to obtain the geographic information (specifically, the country) from the profile location associated with each user..
    • Extracted both the date posted and the week from the timestamp by means of the datetime package, subsequently discarding the timestamp column.

    English Tweet Text Preprocessing

    • Filtered English-tweets and saved them to a new dataframe.
    • Converted all tweets that represented the same word in different cases (e.g. ChatGPT and CHATGPT) to the same lowercase form (e.g.chatgpt).
    • Removed noise such as punctuation, URLs, Twitter handles using the "re" library.
    • Removed stopwords using the NLTK English stopwords list, and eliminated tokens that were too short (less than three characters) or too long (over 50 characters).
    • Exracted frequently occurring continuous sequence of 2 words (bigram) and 3 words (trigram) within the corpus.
    • Performed WordNet-based lemmatization using the NLTK pacakge to transform each word into its base or dictionary form.

    DATA-MODELING

    Unsupervised LDA

    The unsupervised Latent Dirichlet Allocation (LDA) modelling technique was applied to extract a set of key ChatGPT topics from the collected tweets.

    • Generated a dictionary and corpus containing all the tweet texts, filtering out extreme words with low/high appearing frequency of occurence (occuring in less than 10 tweets or more than 50% of tweets).
    • Inplemented LDA using the LdaMulticore module in Gensim library.
    • Conducted a series of experiment by varing the number of topics (N) from 2 to 40, and obtained a relatively high coherence score for the range of 10 to 18 topics.
    • Executed LDA with N=10 and identified 10 topics based on the highly relevant tweets for each topic.

    Sentiment Analysis

    Sentiment analysis is an approach to identifying the emotional tone behind textual data. Various algorithms (models) are available for sentiment analysis tasks, and each has its pros and cons, such as:

    • Rule-based (lexicon-based): Such kinds of models have their own dictionaries (lexicons) of words or emojis with positive or negative weights. These algorithms count the number of positive and negative words in the given text. If the number of positives is more than the negatives, they return a positive sentiment. If both are equal, they return a neutral sentiment. Rules or dictionaries of words can be customized. And these kinds of algorithms do not require any model training.
    • Supervised Machine learning: These algorithms are fed with many labeled text data until they can learn patterns or the essence of the statement instead of clearly defined rules. However, for this approach labeled data is required, which is not available in our study.
    • Unsupervised Deep Learning: Such kinds of algorithms are able to learn patterns through multiple layers from unstructured and unlabeled data to perform sentiment analysis using various learning mechanisms, e.g. self-attention.

    In this study, we used both VADER (rule-based model) from the NLTK library and Twitter-roBERTa (deep learning based)from the TRANSFORMERS package to examine the early users' attitude towards ChatGPT.

    RESULTS

    EDA

    Tweets about ChatGPT over time

    • When ChatGPT was first released, there were approximately 250,000 tweets discussing it in the first two weeks leading up to December 11, 2022.
    • The number of tweets mentioning ChatGPT in January 2023 was 1.65 times higher than the number of tweets in December 2022.
    • In week 9 (1/21/2023-1/29/2023), the number of tweets related to ChatGPT reached its highest point in the past two monts (246,657).

    Users' Features

    • Verification Status
      • ChatGPT has been adopted by both verified and non-verified users on Twitter: Only 3.1% of ChatGPT users on Twitter are verified.

    • Followers
      • Some high-profile Twitter users with millions of followers have expressed interest in ChatGPT, including Elon Musk, CNN, NYTimes, and others.

    • Tweeting Frequency
      • From December 2022 to January 2023, a total of 550,655 Twitter users tweeted about ChatGPT.
      • Among these users, 63.42% tweeted only once, while 36.58% tweeted more than once.

    • Countries
      • 31.7% users tweeting about ChatGPT were from the United Stats.
      • The top 5 countries where ChatGPT was discussed the most were United States, the United Kingdom, India, Japan, and France.
      • ChatGPT has grabbed attention from users in over 150 countries worldwide.

    • Languages
      • Over 70% of users discussing ChatGPT were English speakers.
      • Around 30% of tweets about ChatGPT were written in other languages, i.e. Japanese, Spanish, French, etc., indicating that ChatGPT is also functional in languages other than Engligh.

    TOPIC-MODELING

    Optimal Number of Topics and Iterations

    After evaluating coherence score, comprehensibility of top keywords, and computational cost, the study determined the optimal number of topics (10) and iterations (60) for LDA analysis.

    Topics

    Through a meticulous analysis of the top 20 keywords and hundreds of highly correlated tweets for each topic within the LDA topic modeling results, the study determined a descriptive and meaningful name for each topic. The top 3 most discussed topics are:

    • T8 Impacts on Work and Efficiency
    • T5 ChatGTP's Issues and Reliability
    • T1 ChatGPT's Cost and Access

    SENTIMENT-ANALYSIS

    • Comparison of VADER and roBERTa
      • When comparing VADER and roBERTa, it was found that VADER struggled to identify many positive or negative words in tweerts, often resulting in a polarity score of 0. roBERTa was better at capturing the deeper meaning of a text, allowing it to distinguish between negative and neutral tweets with greater sensitivity.
      • Based on the prediction results, VADER identified positive and neutral tweets as more common, while negative tweets were relatively infrequent (only 12.4%). However, roBERTa's results showed that the number of negative tweets almost doubled (24.1%).
      • In terms of computational efficiency, VADER was significantly faster than roBERTa, taking only 0.49 seconds to analyze 5000 tweets, while roBERTa took 243.42 seconds on a personal desktop.

    Overall, VADER is a faster option, but may not capture the nuances of natural language as well as roBERTa. The choice between VADER and roBERTa will depend on the specific task requirements and available computational resources.

    • Sentiment Over Time (based on roBERTa results)

    • Sentiment of Each Topics

      • Topic T8 Impacts on Work and Efficiency and T7 Impacts on Future Business and Industry received the most positive sentiment of approximate 50%, indicating people's optimism about the postive effects of ChatGPT on furture work and life.
      • Topic T5 ChatGPT's Issues and Reliability and T9 Impacts on Education and Academy received relatively more negative sentiment, approximately 46% and 30%, resperctively. This indicates that there is room for improvement in terms of user experience, system stability, watermarking for preventing plagiarism, etc.

      Overall, early users of ChatGPT expressed mainly positive or neutral sentiments regarding its performance in assisting human tasks in various domains, such as business analysis, software development, and NLP. However, a limited percentage of users expressed concerns about potential misuse of ChatGPT and its reliability.

    CONCLUSION

    This study focused on showcasing the discussions about ChatGPT on Twitter, utilizing a dataset comprising 1.25 million tweets by leveraging machine learning and text analytics. Exploratory data analysis was conducted first on the collected dataset to understand the characteristics of early ChatGPT users. Further, topic modeling was performed to identify the main topics, followed by quantitative sentiment analysis on each topic. The study provides valuable insights into the sentiments of early ChatGPT users and emphasizes the importance of continued research and conversation to develop best practises for the responsible use of large language models.

    REFERENCES