Sentiment Analysis on Social Media
The topic of the Nkrumah project is sentiment analysis on social media using the Endsars datasets as a use case
Write a brief summary about the project here (what problem are you solving , whats your solution) including goals, and key features
Sentiment analysis on social media is significant due to its potential applications across diverse domains. It offers insights into public perceptions, sentiments, and trends, aiding businesses in brand management, product development, and customer service. Politicians and policymakers can leverage sentiment analysis to gauge public opinion, while healthcare organizations can monitor sentiments about healthcare services and policies. Furthermore, sentiment analysis is valuable for understanding societal trends and aiding crisis management.
We propose a machine learning-based sentiment analysis approach utilizing natural language processing (NLP) techniques. This approach involves preprocessing the social media text data, extracting relevant features, and employing a supervised learning technique for sentiment classification.
The solution of sentiment analysis on social media data provides valuable insights that can influence decision-making, strategy formulation, and overall public engagement across a wide range of sectors and industries.
- Key Features
A machine learning-based sentiment analysis approach utilizing natural language processing (NLP) techniques. This feature involves preprocessing the social media text data, extracting relevant features, and employing a supervised learning technique for sentiment classification.
Sentiment analysis, also known as opinion mining, is a computational technique used to determine the sentiment expressed in a piece of text. It involves analyzing the subjective information present in the text to categorize it into positive, negative, or neutral sentiments. Sentiment analysis can be performed at various levels, such as document-level, sentence-level, or aspect-level, to capture different nuances of sentiment.
Sentiment analysis is highly relevant in the context of social media due to the immense volume of user-generated content being shared daily. Social media platforms are a hub of diverse opinions, emotions, and attitudes expressed by individuals and communities. Understanding public sentiment on social media is crucial for several reasons:
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Brand Perception and Marketing Companies can gauge how their brand or products are perceived by analyzing social media sentiments. Positive sentiment can guide marketing strategies, while negative sentiment can indicate areas for improvement.
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Customer Feedback and Support Monitoring sentiment allows businesses to address customer concerns promptly, improve products/services, and enhance customer satisfaction by responding to feedback effectively.
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Political and Social Trends Public sentiment on social media can reflect political opinions, societal issues, and trends, providing insights for policymakers, campaign strategies, and social movements.
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Crisis Management During crises or emergencies, monitoring social media sentiment helps organizations assess public perception, enabling them to respond appropriately and manage reputational damage.
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Product Development and Innovation Analyzing sentiment regarding existing products or services can guide innovation by identifying features that resonate positively with users and areas for enhancement.
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Market Research and Competitor Analysis Sentiment analysis helps in understanding market dynamics, consumer preferences, and competitive landscapes by analyzing public sentiment towards various products, services, or competitors.
In essence, sentiment analysis on social media data provides valuable insights that can influence decision-making, strategy formulation, and overall public engagement across a wide range of sectors and industries.
The copy of the project can be gotten from the repository and setup using the installation guide mentioned below.
- Clone the repository from GIT
- Run the pip install command from from the command line on the path of the project
- Review the copy of the Endsars data
- Run the project to see the sentiment analysis on the Tweets.
- Worthy of mention is the contribution of the Team mentor
- Notable Libraries used in the projects: Pandas, NumPy, and Scikit-learn
- The use of Machine Learning, NLP Techniques, and Sentiment Analysis Algorithms.
- Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. https://nlp.stanford.edu/IR-book/information-retrieval-book.html
- Scikit-learn Documentation: Official documentation for Scikit-learn, a widely used Python library for machine learning.
- NLTK Documentation: Official documentation for the Natural Language Toolkit (NLTK) in Python
- Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends® in Information Retrieval, 2(1-2), 1-135.
- Socher, R., et al. (2013). Recursive deep models for semantic compositionality over a sentiment treebank. Conference on Empirical Methods in Natural Language Processing (EMNLP).
Listed below is the Team mentor and members who have contributed to the project with link to their Linkedin profile
- Inconsistency in meeting attendance by members
The project can be explored and used for further analysis.
Your project should involve the following components:
- Data Sourcing: Web scraping or any other data sourcing method.
- Data Cleaning and Prep: Data Cleaning, preparation and basic statistics reporting
- Modeling: Base Model, Model Comparison, Hyper-parameter Tuning and monitoring with experiment management
- Model Deployment : Deploy on the web or mobile. You can leverage Google Colab/Streamlit/Huggyface where possible.
- Requirements.txt: A file for all dependecies required
- Project Submission Deadline: December 10, 2023
- Presentation Day: December 16, 2023