tweets sentiment analysis to predict whether a person is depressed or not based on their tweets

A Natural Language Processing model to perform Sentiment Analysis of US Airline Customers

Women's' E-commerce product review dataset downloaded from Kaggle will undergo in this code a sentiment analysis process

HexDoc is an AI-powered chatbot that helps users manage stress by offering personalized exercises, motivational quotes, and mental health resources. It uses NLP techniques and TensorFlow-based neural networks to understand and respond to user inputs.

Latent Semantic Analysis of Book Titles

Machine Learning For Texts -- Study Project for Yandex Practicum

Auto Ticket Classification using NLP (Lemmatization & POS tagging) and Supervised Machine Learning models

This project predicts the sentiments of tweets posted on Twitter by different users using Natural Language Processing

In this data cleaning has been done with the help of nltk library and other library which include wordnetlimmitizer , ,and steaming of word has been donw by portersteammer ,

This project showcase the application of LDA Topic Modelling and KMeans Clustering for extracting information from the PDF documents

Predicting which tweets are about real disasters. Using Bag-of-Words, TF-IDF Vectors, Naive Bayes, Linear Discriminant Analysis, Truncated SVD, custom tokenizer, lemmatization, GridSearchCV.

This project is based on the prediction of the sentiments of tweets posted on Twitter by different account users.

Social Media Sentiment Analysis Using Twitter Dataset (Group project by - Anmol Raj, Paritosh Parihar) In this we use a data set containing a collection of tweets to detect the sentiment associated with a particular tweet and detect it as negative or positive accordingly using Machine Learning.

It is a Turkish BERT-based model that will analyze people's bank complaints and classify them according to one of eight categories.

Text Classification using ML algorithms

Crawling news and information website and anticipating the likelihood of its virality.

In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons. An automatic discriminative software with a sensitivity parameter for flame or abusive language detection would be a useful tool.