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sentiment analysis

A series of scripts made for a sentiment analysis project I am playing with, mostly to practice with web scraping, API, and models, for another idea i want to implement.

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.

sentiment analysis using spark ml library. implemented classic ml models: SVM, Logistic Regression, Naive Bayes and Random Forest. implemented embedding: Word2Vec and TF-IDF. also ensemble and hybrid (ml and lexicon based) methods were implemented

sentiment analysis using keras and tensorflow

Komparative Zeitreihenanalyse von lexikalischen Aspekten in österreichischen Korpusdaten: Stabilität und Emotion: Code und Daten

this project build for complete the Technical test

implementing sentiment analysis using svm and lexicon based and hybrid methods. the crawler folder is the code for scraping comments in mobile.ir website. the goal of project is to determining the most popular mobile brand based on user's opinions.

This project shows how to use Tidytext and sentiment analysis to examine the Reddit archive.