The project researches sentiment analysis on Twitter, with the goal of evaluating the positivity, negativity or neutrality of comments. Using Word Embeddings, an advanced method in natural language processing, our model achieved a high accuracy of 96.61%. The model was trained on Twitter data and tested on a data comment dataset from Binance.

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

This GitHub project focuses on sentiment analysis for Twitter data using RoBERTa and traditional algorithms. We preprocess the datasets with text cleaning and tokenization. RoBERTa's contextualized word embeddings and fine-tuned architecture excel in sentiment classification, outperforming traditional algorithms, especially with larger datasets.

M2015-DWDM Major Project - Sentiment analysis for twitter data using 'Sentiment Specific Word Embeddings'

This project uses the Sentiment140 dataset for Twitter sentiment analysis. It involves text preprocessing, vectorization (TF-IDF or Word Embeddings), and classification (Naive Bayes, Logistic Regression, SVM), utilizing pandas, scikit-learn, nltk, and visualization tools.

Using the word embeddings: fastText, Skip-gram and GloVe as basis for a one-dimensional and a two-dimensional Convolutional Neural Network. Performing Sentiment analysis on a "Twitter" dataset.

This repository is dedicated to Twitter Sentiment Analysis, focusing on classifying tweet sentiments using machine learning. It includes text-processing techniques for data enhancement and utilizes Word Embedding methods and an LSTM neural network for accurate sentiment classification

This repository contains multiple deep learning models for sentiment analysis on Twitter datasets using different word embeddings and transformer techniques. It demonstrates how embedding choices affect classification performance while providing end-to-end workflows including preprocessing, modeling, evaluation, and visualizations.

Project leveraging advanced text data analysis from data Extraction using twitter API, using advanced NLP techniques and state of the art word embedding to hybrid models for feature extraction and further analyze sentiment and detect depression from tweets.

This project explores word embedding techniques on a Twitter dataset using Word2Vec, Doc2Vec, and CNN-based methods. It transforms text into dense vector representations, enabling semantic understanding for NLP tasks. Applications include sentiment analysis, text classification, and tweet clustering.

This project uses LSTM neural networks for sentiment analysis on social media posts. It involves cleaning text, using word embeddings, and training an LSTM model to classify posts as positive, negative, or neutral. It helps understand public opinion and trends from platforms like Twitter or Facebook. Ask ChatGPT