This repository focuses on sentiment analysis of Twitter data using Python, Natural Language Processing (NLP), and the Natural Language Toolkit (NLTK). The goal is to extract valuable insights from social media discussions, such as word frequency, hashtag trends, and sentiment patterns.
Social media, particularly Twitter, serves as a rich source of public opinion and sentiment. This project utilizes NLP techniques and the NLTK library to analyze tweets, providing a comprehensive understanding of the sentiment conveyed in the data. The analysis includes:
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Sentiment Analysis: Determining the sentiment (positive, negative, or neutral) of each tweet.
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Word Frequency Analysis: Identifying the most frequently used words in the dataset to understand common themes.
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Hashtag Trends: Extracting and analyzing hashtags to identify popular trends and topics.
The project is implemented in Python, leveraging popular libraries such as NLTK for NLP tasks. The code is organized into segments, making it easy to understand and extend. The sentiment analysis model is trained on a labeled dataset that has been taken from Kaggle, and NLTK's functionalities are employed for preprocessing and analysis.
To get started with the project, follow these steps:
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Clone the repository to your local machine:
git clone https://github.com/your-username/tweets_analysis.git