Summary of Deep Learning-based Sentiment Analysis Of Olympics Tweets, by Indranil Bandyopadhyay et al.
Deep Learning-based Sentiment Analysis of Olympics Tweets
by Indranil Bandyopadhyay, Rahul Karmakar
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study develops an advanced deep learning (DL) model for sentiment analysis (SA) to understand global audience emotions through tweets in the context of the Olympic Games. The goal is to create a reliable and accurate SA model that can analyze subjective information such as views, feelings, and attitudes toward specific topics, products, services, events, or experiences. To achieve this, the researchers used natural language processing (NLP) for tweet pre-processing and sophisticated DL models for sentiment classification. The study focuses on data selection, preprocessing, visualization, feature extraction, and model building, featuring a baseline Naïve Bayes (NB) model and three advanced DL models: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results show that the BERT model can efficiently classify sentiments related to the Olympics, achieving the highest accuracy of 99.23%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how people feel about the Olympic Games by analyzing tweets. It uses special computer models to look at what people are saying and how they’re feeling. The goal is to make a tool that can accurately figure out how people feel based on their tweets. The researchers used different types of computer models, including one called BERT, which was able to correctly guess how people felt about the Olympics most of the time. |
Keywords
» Artificial intelligence » Bert » Classification » Cnn » Deep learning » Encoder » Feature extraction » Natural language processing » Neural network » Nlp
