Text Sentiment Analysis in Python using Natural Language Processing (NLP) for Negative/Positive Content Detection. Deployed on the Cloud using Streamlit on the Heroku Platform.

Performing text sentiment analysis on numerous websites and determining their text sentiment scores using NLP and python programming.

This is a demo repo to demonstrate how to use Python GoogleNews library to search and scrape result data. And save scraping results into Pandas dataframe.

Video Analytics in Python using face-emotion-detection, speech-to-text and text-sentiment analysis pre-trained DEEP LEARNING models

This is the advanced form of the sentiment analysis from text. In this method we have to find out the sentiment regarding the aspect word. The data set contains the reviews and the aspect word/phrase. We have to find out the sentiment regarding that aspect/phrase. We have used simple method using TfIdfVectorizer and ml algorithms. Got the nearly…

This project leverages the power of transformer models to perform sentiment analysis on both text and images. It uses BERT for text sentiment analysis and a pre-trained vision transformer (ViT) for image sentiment analysis.

In this Project we will train our RNN model by giving it different tweets and then predict the sentiments of the tweets.

Moodify is a web application that suggests songs based on your mood. The app allows users to detect their mood via text input,or by selecting from a dropdown menu, and then recommends region-specific songs using the Spotify API.

This work is a text analysis in R based on the NASA data found in: https://data.nasa.gov/data.json . The text analysis is based in different steps starting with lexicons. Further investigating positive and negative sentiments on a world cloud . Also, correlations between words are analysed.

This project configured the Caikit runtime to load and run a Hugging Face text sentiment analysis model. Then deployed a client application on the runtime that used the Caikit API to query the Hugging Face model for sentiment analysis on text strings. The model response included the sentiment analysis and a confidence score for each sample.

This project extracts text from a list of URLs and performs a detailed textual analysis to compute various linguistic and sentiment metrics. The extracted data is saved to text files, and the analysis results are compiled into an Excel file.

A collection of various utility REST api

Text sentiment analisys