Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall

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

Find your trading, investing edge using the most advanced web app for technical and fundamental research combined with real time sentiment analysis.

Sentiment analysis on Amazon Review Dataset available at http://snap.stanford.edu/data/web-Amazon.html

Technical and sentiment analysis to predict the stock market with machine learning models based on historical time series data and news article sentiment collected using APIs and web scraping.

Financial Web Scraper & Sentiment Classifier

Web interface to sentiment analyzer.

Create a web app for monitoring sentiment, price, and news for individual listed stocks, using IBM Watson Discovery and CloudantDB as well as Nodejs and Alpha Vantage.

基于UIE的舆论情感分析Web系统，前后端分离式架构部署，支持单文本属性级情感分析及上传txt文件进行批量情感分析，并支持分析结果的可视化展示。 技术栈：后端：FastAPI + UIE；前端：Vue + ElementUI + Echarts。

Web app enabling users to either record or upload audio files. Then utilizing OpenAI API (Whisper, GPT4) generates transcriptions, summaries, fact checks, sentiment analysis, and text metrics. Users can also intelligently chat about their transcriptions with a GPT4 chatbot. Data is stored relationally in SQLite and also vectorized in Pinecone.

The backend and ML code for sentiment analysis. Also needs this code to run: https://github.com/nikhilvangumalla/web_sentiment_analysis

A real time Tweet Trend Map and Sentiment Analysis web application with kafka, Angular, Spring Boot, Flink, Elasticsearch, Kibana, Docker and Kubernetes deployed on the cloud

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

[不再更新]中文短文本情感分析 web 应用 | A web app about Chinese sentences sentiment analysis

Twitter Sentiment Analysis using #tag, words and username

A web application for real-time machine learning and sentiment analysis on Tweets

Python 3 wrapper for SentiStrength. SentiStrength is capable of automatic sentiment analysis of up to 16,000 social web texts per second with up to human level accuracy for English.

A stock market text sentiment analysis website. A股舆情分析, web-crawler, bayesian algorithm, SQL, django, data-visualization.

YouTube Sentiment Analysis is a web application that analyzes the sentiment of YouTube comments, providing insights into comment sentiment using VADER sentiment analysis and interactive visualizations.

:chart: A web app to search twitter based on #Hashtags and calculate the sentiment of tweets.

The objective of this project is to scarp the data from IMDb website and form an analysis that will help data analyst or production company to decide how they are going to proceed with making a new movie, second is to form a model to predict what are the sentiments of movies based on user reviews.

A Flask, React and Machine Learning based web application for movie recommendation and sentiment analysis on movie reviews.

The tutorial uses several Azure services to power a real-time chat infrastructure that is readymade for analytics. Event Hubs ingest chat messages received from websites running in Web Apps. Web Jobs are used to pull chat messages from Event Hubs, invoke the Text Analytics API to apply sentiment scores to each message and to forward messages to Service Bus Topics from which chat participants receive their messages. Stream Analytics is used to drive the archival of scored chat messages into Document DB and Azure Search is used to make the stored chat messages full text searchable.

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 machine learning end to end flask web app for sentiment analysis model created using Scikit-learn & VADER Sentiment.

Sentiment analysis web application for open source projects

A Web Page Of Public Sentiment For P2P Industry( P2P 行业的舆情分析前端展示)

Web api built on flask for keras-based sentiment analysis using Word Embedding, RNN and CNN

Web Page Inspection Tool UI. Article Summary, Sentiment Analysis, Keyword Extraction, Named Entity Recognition & Spell Check