A real-time interactive web app based on data pipelines using streaming Twitter data, automated sentiment analysis, and MySQL&PostgreSQL database (Deployed on Heroku)

Best free, open-source datasets for data science and machine learning projects. Top government data including census, economic, financial, agricultural, image datasets, labeled and unlabeled, autonomous car datasets, and much more. Data.gov NOAA - https://www.ncdc.noaa.gov/cdo-web/ atmospheric, ocean Bureau of Labor Statistics - https://www.bls.gov/data/ employment, inflation US Census Data - https://www.census.gov/data.html demographics, income, geo, time series Bureau of Economic Analysis - http://www.bea.gov/data/gdp/gross-dom... GDP, corporate profits, savings rates Federal Reserve - https://fred.stlouisfed.org/ curency, interest rates, payroll Quandl - https://www.quandl.com/ financial and economic Data.gov.uk UK Dataservice - https://www.ukdataservice.ac.uk Census data and much more WorldBank - https://datacatalog.worldbank.org census, demographics, geographic, health, income, GDP IMF - https://www.imf.org/en/Data economic, currency, finance, commodities, time series OpenData.go.ke Kenya govt data on agriculture, education, water, health, finance, … https://data.world/ Open Data for Africa - http://dataportal.opendataforafrica.org/ agriculture, energy, environment, industry, … Kaggle - https://www.kaggle.com/datasets A huge variety of different datasets Amazon Reviews - https://snap.stanford.edu/data/web-Am... 35M product reviews from 6.6M users GroupLens - https://grouplens.org/datasets/moviel... 20M movie ratings Yelp Reviews - https://www.yelp.com/dataset 6.7M reviews, pictures, businesses IMDB Reviews - http://ai.stanford.edu/~amaas/data/se... 25k Movie reviews Twitter Sentiment 140 - http://help.sentiment140.com/for-stud... 160k Tweets Airbnb - http://insideairbnb.com/get-the-data.... A TON of data by geo UCI ML Datasets - http://mlr.cs.umass.edu/ml/ iris, wine, abalone, heart disease, poker hands, …. Enron Email dataset - http://www.cs.cmu.edu/~enron/ 500k emails from 150 people From 2001 energy scandal. See the movie: The Smartest Guys in the Room. Spambase - https://archive.ics.uci.edu/ml/datase... Emails Jeopardy Questions - https://www.reddit.com/r/datasets/com... 200k Questions and answers in json Gutenberg Ebooks - http://www.gutenberg.org/wiki/Gutenbe... Large collection of books

Twitter Sentiment Analysis using #tag, words and username

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.

Twitter Sentiment Analysis using Textblob and Tweepy, wrapped with Flask as a web app.

A Simple Approach to Twitter Sentiment Analysis in R Programming Language

A web front end for R Twitter sentiment analysis

R shiny web application to scrape tweets based on user-defined search keyword and perform sentiment analysis of the tweets. Sentiment analysis of tweets consists of classifying tweets into emotion classes (i.e., anger, disgust, fear, joy, sadness and surprise) and also polarity classes (i.e., negative, neutral and positive) using naïve Bayes classifier. The tweets are scraped, classified into sentiment classes and visualized in R using twitteR, sentiment and ggplot2 packages, respectively.

Twitter Sentiment Analysis Web Application (NodeJS)

In this repo i created a twitter sentiment analysis on flask app (web base).

To Scrape IMDB for Celebrity Data and Analyze Sentiment on Twitter | Edureka Course Work

A sentiment analysis and web-sockets application with twitter's streaming api

This is a web app that runs Sentiment Analysis on tweets from a Twitter Handle

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[Web Mining] Vietnamese sentiment analysis from Twitter. Using TwitterAPI, Python

Abstract Depression brings significant challenges to the overall global public health. Each day, millions of people suffered from depression and only a small fraction of them undergo proper treatments. In the past, doctors will diagnose a patient via a face to face session using the diagnostic criteria that determine depression such as the Depression DSM-5 Diagnostic Criteria. However, past research revealed that most patients would not seek help from doctors at the early stage of depression which results in a declination in their mental health condition. On the other hand, many people are using social media platforms to share their feelings on a daily basis. Since then, there have been many studies on using social media to predict mental and physical diseases such as studies about cardiac arrest (Bosley et al., 2013), Zika virus (Miller, Banerjee, Muppalla, Romine, & Sheth, 2017), prescription drug abuse (Coppersmith, Dredze, Harman, Hollingshead, & Mitchell, 2015) mental health (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016) and studies particularly about depressive behavior within an individual (Kiang, Anthony, Adrian, Sophie, & Siyue, 2015). This research particularly focuses on leveraging social media data for detecting depressive thoughts among social media users. In essence, this research incorporated text analysis that focuses on drawing insights from written communication in order to conclude whether a tweet is related to depressive thoughts. This research produced a web application that performs a real-time enhanced classification of tweets based on a domain-specific lexicon-based method, which utilizes an improved dictionary that consists of depressive and non-depressive words with their associated orientations to classify depressive tweets. Problem understanding or Business Understanding Depression is the main cause of disability worldwide (De Choudhury et al., 2013). Statistically, an estimation of nearly 300 million people around the world suffers from depression. Shen et al (2017) mentioned that approximately 70% of people with early stages of depression would not consult a clinical psychologist. Many people are utilizing social media sites like Facebook and Instagram to disclose their feelings. This research persists the hypothesis that there are similarities between the mental state of an individual and the sentiment of their tweets and investigated the potentiality of social media (like twitter) as a data source for classifying depression among individuals.

The is a web app for twitter sentiment analysis.

Mashup of various Web APIs (Facebook, Yelp, Amazon, FatSecret, Twitter etc). Implemented sentiment analysis on Amazon products using Stanford NLP. View readme file for further details

A Twitter Sentiment Analysis web application

built machine learning model to predict sentiment on tweet or dataset of tweets into positive, negative ,neutral .I have used dataset of 1 lakh tweets to train ML model and also created a web app using streamlit for interactive interface.

Brand-tracking web app in real-time using Twitter, using BERT and RoBERTa models for sentiment analysis

Web app that performs sentiment analysis on Twitter

The purpose of this project is to classify the given twitter tweets in to positive, negative and neutral classes i.e. the project basically analyses the emotion of a particular tweet. To train and classify the given data, We have used different classification techniques such as Multinomial Naive Bayes, Linear SVM(Support Vector Machine), LSTM(Long Short Term Memory), CNN(Convolutional Neural Network) and ensembles of some of the models mentioned above. Before feeding the data into the model, pre-processing of data was done, features like unigram, bigram, trigram, padding, Word2vec etc. were used and finally the data was converted into a format which a machine learning model can understand. Out of all the classification techniques used, prediction taken by majority voting between CNN, LSTM, SVM performed better.

SEN-T-MENT is a Twitter Sentiment Analysis Web Application developed during HackSVIT Hackathon.

💀✅ Twitter user Sentiment Analysis on web app

This project provides a FastAPI-based web service for Twitter sentiment analysis using three different models:

A Django-based web app created for displaying analysis of the geographic distribution of Twitter sentiment related to COVID-19 in the UK.

Web application to support communicational environment analysis in Twitter. It shows graphs with content analysis results made on the tweets. It also tags them according to their sentiments with a machine learning model.

A web app that gives users a sentiment analysis from Twitter on different subjects/topics.