Advanced Machine Learning Techniques for Squid Game Sentiment Analysis: A Comprehensive Approach to Understand Public Opinion, Social Impact, and Viewer Engagement

Project Abstract:

Background: Squid Game, a popular television series, has captured the attention of audiences worldwide and generated significant discussions on social media platforms. By analyzing user-generated content related to Squid Game, it is possible to understand public opinion, social impact, and viewer engagement. This project aims to develop a robust and reliable sentiment analysis model using advanced machine learning techniques, leveraging a dataset containing social media posts related to Squid Game to enhance the understanding of the show’s influence on public sentiment and social discourse.

Objectives:

1. To collect, preprocess, and analyze a dataset of social media posts (e.g., tweets) related to Squid Game, including text content, user information, and sentiment labels.
2. To implement natural language processing (NLP) techniques for effective feature extraction and representation from social media post texts.
3. To implement various machine learning algorithms, including classification, ensemble methods, and deep learning, to create a high-performance sentiment analysis model.
4. To evaluate the performance of the sentiment analysis model using appropriate metrics and validate its effectiveness in classifying sentiment in Squid Game-related posts.
5. To provide insights and recommendations for content creators, marketers, and researchers based on the Squid Game sentiment analysis.

Methods:

1. Data collection and preprocessing: Data collection will involve gathering social media posts related to Squid Game using platform-specific APIs (e.g., Twitter API). Data preprocessing steps, such as data cleaning, handling missing values, and text preprocessing (e.g., tokenization, stopword removal, and stemming), will be performed to ensure the dataset is suitable for ML model training.
2. Feature extraction: NLP techniques, such as Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), and word embeddings (e.g., Word2Vec, GloVe), will be used for effective feature extraction and representation from social media post texts.
3. Model development: ML algorithms, including Logistic Regression, Naive Bayes, Decision Trees, Random Forest, Support Vector Machines, and deep learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, will be applied to develop the sentiment analysis model. Hyperparameter tuning and model selection will be conducted through cross-validation and grid search techniques.
4. Model evaluation: The performance of the ML models will be assessed using metrics such as accuracy, precision, recall, F1-score, and area under the Receiver Operating Characteristic (ROC) curve.
5. Insights and recommendations: The Squid Game sentiment analysis will be used to derive insights and recommendations for content creators, marketers, and researchers to understand the show’s impact on public sentiment and social discourse.

Expected Outcomes: The project will result in a comprehensive sentiment analysis model capable of accurately classifying sentiment in Squid Game-related social media posts. The implementation of this model will enable a better understanding of public opinion, social impact, and viewer engagement related to the show, providing valuable insights for content creators, marketers, and researchers.

Keywords: Squid Game, sentiment analysis, machine learning, natural language processing, feature extraction, data preprocessing, classification, ensemble methods, deep learning, public opinion, social impact, viewer engagement.