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Sentiment Analysis Project

This repository contains a Jupyter Notebook that performs sentiment analysis on a dataset of tweets related to Nvidia. The project aims to analyze the sentiment (positive, negative, or neutral) associated with tweets and to explore how people express their opinions about Nvidia on social media.

Project Overview

The sentiment analysis in this project is focused on text data, specifically tweets, which are categorized into different sentiments. This notebook demonstrates how to load, preprocess, and analyze a dataset of tweets to extract valuable insights regarding public opinion.

Features

  • Data Loading: The dataset of tweets is loaded for analysis.
  • Data Preprocessing: Various data cleaning and preprocessing steps are applied to make the text suitable for sentiment analysis.
  • Exploratory Data Analysis (EDA): The notebook includes visualizations and statistical analyses of the dataset.
  • Modeling: Sentiment analysis is performed using various natural language processing (NLP) techniques.
  • Results: The final sentiment classifications are displayed, and insights are provided based on the results.

Requirements

To run the notebook, the following Python libraries are required:

  • pandas
  • numpy
  • matplotlib
  • seaborn
  • scikit-learn
  • nltk
  • tensorflow (for advanced models, if used)

You can install these dependencies using pip:

pip install pandas numpy matplotlib seaborn scikit-learn nltk tensorflow

Usage

  1. Clone the repository:
git clone https://github.com/zshafique25/Sentiment-Analysis.git
  1. Install the required dependencies (see the Requirements section).
  2. Open the notebook Sentiment_Analysis.ipynb in Jupyter Notebook or JupyterLab.
  3. Run the cells sequentially to load the data, preprocess it, and perform sentiment analysis.

Dataset

The dataset used in this notebook contains the following columns:

  • ID: A unique identifier for each tweet.
  • Topic: The topic of the tweet (in this case, there are different companies Like Amazon, Nividia, Microsoft , Verizon etc).
  • Sentiment: The sentiment label (Positive, Negative, Neutral).
  • Tweet: The actual text of the tweet.

Results

The sentiment analysis results include predictions for each tweet's sentiment, which can be used to understand the general perception of those companies on social media.