The Sentiment Analysis Web Application is designed to analyze tweets related to two public figures. Here are its key features:
- Sentiment Analysis:
- The application performs sentiment analysis on the top 100 tweets associated with the specified names.
- It categorizes tweets as positive, negative, or neutral based on their content.
- Visualization:
- The sentiment results are visualized using a bar chart, providing an overview of the overall sentiment distribution.
- Users can quickly grasp the sentiment trends related to the given names.
- Wikipedia Image Retrieval:
- The application fetches images from Wikipedia for the specified names.
- These images enhance the user experience and provide context.
- Multilingual Support:
- The application supports analyzing tweets in all languages.
- Users can explore sentiment across diverse linguistic contexts.
Python, Flask, Selenium, Plotly, BeautifulSoup, Requests, TextBlob, HTML/CSS
Since the website is not hosted yet , you need to do it manually by hosting it locally . Clone the Github link and run python app.py on your terminal.
- When encountering a “frame not found” error in Selenium, it typically occurs due to issues with interacting with iframes (inline frames) on a web page.
- To resolve this:
- Run your script in non-headless mode (i.e., with a visible browser window).
- Observe which pages or elements are popping up during the login process.
- Inspect the page source to identify iframes and ensure proper interaction with them.
- If you encounter a timeout error while using Google Translate API:
- Retry the translation request after waiting for a few minutes. Sometimes, temporary server issues can cause timeouts.
- Alternatively, consider obtaining a Google API key. This key can increase your API quota and provide more reliable access to the translation service.