The code base of the front-end of nocodefunctions.com
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Updated
Apr 23, 2024 - CSS
The code base of the front-end of nocodefunctions.com
Scripts utilizing Heartex platform to build brand sentiment analysis from the news
Product Rating system using sentiment analysis of text reviews.
#Sentimental Analytics
Open source version of Sarahah but with many addons such as Sentimental Analysis, strict word filteration etc
Image Recognition Web App using the AWS rekognition service API
A research tool for anybody can build, train, test and analysis deep learning models on audio data for the purpose of emotion classification.
Chatbot for detecting (depression) mental state of students.
Decode Social Buzz, Drive Informed Decisions through Crucial Insights using Sentiment Analysis.
Analyzing realtime comments with sentiment analysis
Analysis of tweets from Catalan Referendum
A Demo Twitter Streaming and Sentiment Analysis App to showcase RHT AMQ Streams (Kafka), MongoDB served through Python backend API and Javascript Frontend . This app runs on OpenShift and enjoys persistency using OpenShift Container Storage (rook-ceph)
Built a sentiment analysis of IMDB movie reviews using state-of-art deep learning techniques, achieved an accuracy of 97% of 25k test data. Additionally, I set up a flask server to handle requests from the front end.
Firefox extension that categorize news article according to their humor, by sentiment analysis.
A web application, which will help the user to trace the social media accounts across multiple platforms and the data is stored in the required database using SOCMINT techniques.
EmotionalAI is a machine learning model which classify the given text into positive or negative sentiment.
Twitter Sentiment Analysis https://twittersentimentanalysisfront.herokuapp.com/
SAP HANA Extreme application that analyzes unstructured data (tweets) to retrieve information such as location, people, companies, and also sentiment analysis.
Sentiment Analysis of Tweets concerning the European Election 2019.
This project analyses the sentiment level of any product and plots the total percentage of tweets which were liked and hated,along with various levels between them using social media data
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