Objective

The objective of this project is to perform sentiment analysis on tweets related to Apple using the R programming language. The sentiment analysis helps us understand the overall sentiment and public opinion surrounding Apple based on the tweets.

Development Time

The project took approximately 3 days to complete. This duration included data collection, data preprocessing, sentiment analysis implementation, and result visualization.

Pricing

For creating a similar sentiment analysis project, the cost would depend on various factors such as the complexity of the analysis, the volume of data, and the specific requirements of the client. Please contact me mohammedkayser143@gmail.com directly to discuss the project details and obtain a personalized quote.

Tools Used

The following tools were used in the development of this project:

• R programming language
• RStudio (optional, but recommended)
• R packages: tidyverse, tm, SnowballC, syuzhet, ggplot2, wordcloud, stringr, lubridate, textdata, ggpubr, reshape2

Project Ownership

This project was developed independently by [Your Name]. It was not done for any specific organization but rather as a personal project to demonstrate the capabilities of sentiment analysis using the R programming language.

Dataset

The dataset used for this analysis is a collection of tweets obtained from kaggle The tweets were collected using relevant keywords and hashtags related to Apple, ensuring a diverse range of opinions and discussions.

Usage

To use this project, follow the steps below:

1. Clone this repository to your local machine or download the source code files.
2. Open the R script "sentiment_analysis.R" in RStudio or any preferred R environment..
3. Run the script to perform sentiment analysis on the Apple tweets.
4. The script will generate visualizations and summary statistics of the sentiment analysis results.

Results

The sentiment analysis produces the following outcomes:

• Sentiment polarity: Positive, negative, or neutral sentiment for each tweet.
• Sentiment distribution: Visual representation of the sentiment distribution using bar charts.
• Word cloud: Visualization of the most frequent words in the tweets.
• Sentiment over time: Analysis of sentiment trends over a specified time period.

Contributing

Contributions to this project are welcome. If you have any suggestions, improvements, or feature additions, please feel free to submit a pull request or open an issue. Your contributions will help enhance the functionality and accuracy of the sentiment analysis project.