sentiment analysis

The Real-Time Insights from Twitter project offers a comprehensive platform for monitoring and analyzing Twitter's dynamic conversations. By leveraging the Twitter API, this project facilitates the real-time collection, analysis, and visualization of tweets, providing valuable insights into public sentiment, trending topics, and other significant metrics.

Objectives:

Data Collection: The project uses the Tweepy library to stream tweets based on specific keywords, hashtags, or other criteria, capturing data that includes tweet content, user information, and metadata. Sentiment Analysis: By employing Natural Language Processing (NLP) tools like TextBlob, the project assesses the sentiment expressed in tweets, classifying them as positive, negative, or neutral. This helps in understanding public mood and opinion trends. Trend Identification: The project identifies and analyzes trending hashtags and topics, providing insights into what subjects are gaining traction or popularity over time. Visualization: Data analysis is further enhanced through visualizations using Matplotlib and Seaborn. These visual tools include time series plots, word clouds, and sentiment distribution charts, offering a clear view of trends and public discourse.

A Telegram bot for sentiment analysis, i.e., a sentiment analysis bot making use of the Telegram Bot API. Just add the SensAI Expanse bot to a Telegram group and start writing messages. The bot will analyse the messages, eventually it may ask you explicitly how do you feel, and use all that data to access your emotional valence, i.e., if you feel positive, neutral, or negative.

applying NLP tools on IMDB dataset and classifying reviews

A virtual space with your virtual twin. Your twin can interact with other twins and other people.