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New BRICS Member Sentiment Analysis issue-518
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tanuj437 committed Jun 10, 2024
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572 changes: 572 additions & 0 deletions New BRICS Member Sentiment Analysis/Dataset/BRICS.csv

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34 changes: 34 additions & 0 deletions New BRICS Member Sentiment Analysis/Dataset/README.md
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**Dataset Description**

Explore the global sentiment surrounding the expansion of the BRICS alliance with our comprehensive sentiment analysis dataset. This dataset captures the public's reactions and opinions on the inclusion of six new member nations into the BRICS coalition. By analyzing this dataset, you will gain valuable insights into how people perceive and respond to the changing dynamics within this influential group of nations.

**Key Features:**

Diverse Opinions: Understand the wide range of public sentiments, from positive endorsements to critical viewpoints, reflecting the multifaceted nature of international relations.
Emotional Reactions: Delve into the emotions expressed by individuals in their comments, providing a deeper understanding of the public mood.
Public Discourse: Analyze the impact of the BRICS expansion on public conversations, revealing how international events shape public opinion.
Data Collection:
The comments have been labeled based on the number of likes they received, offering a community-driven perspective on sentiment. This approach highlights the most engaging and impactful comments, ensuring that the analysis reflects the collective voice of the global community.

**About This File**

This dataset contains the following columns:

textDisplay: The actual text of the comment.

likeCount: The number of likes the comment received.

label: The sentiment label, derived from the like count.

**Sample Comments:**

Here are a few sample comments from the dataset:

"hurray for Brics nation, I hope this changes the World Economic Order!" - 0 likes
"BRICS are modern Axis Powers😂" - 0 likes


This dataset is an invaluable resource for researchers, analysts, and anyone interested in the public's perception of international alliances and their evolving dynamics. By exploring this dataset, you'll uncover the diverse range of opinions, emotions, and reactions that define the public discourse on this significant global event.


For more details and to access the dataset, visit the [Kaggle page](https://www.kaggle.com/datasets/syedali110/6-new-brics-members-sentiment-analysis).
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52 changes: 52 additions & 0 deletions New BRICS Member Sentiment Analysis/Images/README.md
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**Images Folder**

The images folder contains various visualizations generated during the exploratory data analysis (EDA) and model evaluation phases. These visualizations provide valuable insights into the dataset and the performance of the sentiment analysis models. Below is a detailed description of the contents:

**1. Label Distribution**
Label Distribution:
This graph illustrates the distribution of sentiment labels in the dataset. It provides insights into the balance of the dataset and whether there are any imbalances that need to be addressed during model training.
<img width="551" alt="distribution" src="https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/668790d5-2e8e-4137-84df-c160c3e0d014">


**4. Like Count Graph**
Like Count Distribution:
This graph shows the distribution of like counts across comments. It helps in understanding the engagement level of the comments and identifying any skewness in the data.
<img width="515" alt="likecount" src="https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/f15f116d-e44e-4eb1-ada0-a6b11db14d25">


**3. Word Cloud**
Word Cloud:
The word cloud is a visual representation of the most frequent words in the dataset. It highlights the key terms used in the comments, giving a quick overview of the prominent themes and topics discussed.
<img width="605" alt="wordcloud" src="https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/854460be-7fe0-4379-b5ac-9c692eb41b11">

**1. Model Outputs**

Stacking Classifier Confusion Matric

![stacking_classifier_confusion](https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/b1248a61-8287-4e88-8cde-7a28540ab9f4)

Simple Neural Network

![simple_neural_network_output](https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/8f8c82b6-bb62-41b1-87d1-6be04fb49db1)

MLP

![mlp_output](https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/3224f2e4-6644-4477-8c73-c0d00bec9d05)

LSTM

![lstm_output](https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/c04c1790-0a4d-4781-818c-e4906dfd71a1)

LightGBM

![LightGBM_output](https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/214e57e6-a4ec-4be4-b724-4b4b8c5fe44a)

Advance Stacking Output

![advance_stacking_output](https://github.com/tanuj437/BRICS-Sentiment-Analysis/assets/128210429/30ad821d-a155-4e2f-aafc-1d550e21cc84)






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