Social media is a veritable gold mine of information and a window into the collective psychology of people across the world. Be it politicians, celebrities, creative artists, professors or students - everyone seems to be on Twitter. It has become increasingly popular with tweets from famous personalities influencing millions of followers and the markets too! So Twitter data is used for sentiment analysis in various spheres including trading. This blog will show how we can fetch data from Twitter using the Twitter API.

The Web is a vast virtual space where people can share their opinions, impacting all aspects of life and having implications for marketing and communication. The most up-to-date and comprehensive information can be found on social media because of how widespread and straightforward it is to post a message. Proportionately, they are regarded as a valuable resource for making precise market predictions. In particular, Twitter has developed into a potent tool for understanding user sentiment. This article examines how well tweets can influence stock symbol trends. We analyze the volume, sentiment, and mentions of the top five stock symbols in the S&P 500 index on Twitter over three months. Long Short-Term Memory, Bernoulli Na\"ive Bayes, and Random Forest were the three algorithms implemented in this process. Our study revealed a significant correlation between stock prices and Twitter sentiment.

Sentiment analysis is a type of NLP that aims to label data according to its sentiments, such as positive, negative, and neutral. This analysis helps companies understand how their customers feel about their products or services or identify trends in public opinion about a particular topic. For example, a company like Audi can learn whether people like the colors of its new car by examining Twitter shares like the image below. With the developing technology, it is now much easier to express all kinds of emotions, feelings, and thoughts through social networking sites. Social media scraping is the process of extracting data from social media platforms.

The world of finance and stock trading has changed in recent years. As more and more retail investors enter the market, the more important stories and social sentiment become. Think Tesla - one can argue that a lot of the company's value comes from successful social storytelling by its CEO Elon Musk. Social media has the power to turn a bull into a bear and a bear into a bull. Classifying finance tweets using NLP to understand social sentiment is increasingly more important.

Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes. In the past, sentiment analysis used to be limited to researchers, machine learning engineers or data scientists with experience in natural language processing. However, the AI community has built awesome tools to democratize access to machine learning in recent years. Nowadays, you can use sentiment analysis with a few lines of code and no machine learning experience at all!

Daouadi et al. [5] used deep learning methods on features based on the amount of interaction to and from each Social Media Platforms (SMPs) like Facebook, Twitter, Instagram Twitter account along with other set of features used previously, etc. have large user base all around the world that generates huge for fake user detection. Abu-El-Rub and Mueen [1] used trending amount of data every second. This includes a lot of posts by fake hashtags to detect bots interested in political trends. Graph based and spam users, typically used by many organisations around the techniques are used to cluster the collected bots and those are fed globe to have competitive edge over others. In this work, we aim to supervised learning to detect user's agreement/disagreement to at detecting such user accounts in Twitter using a novel approach.

I live in the curious intersection of art, design, and code. For the past two years, I've worked with a small group of artists to develop Alexa, Call Mom!, an immersive storytelling installation using Amazon's Alexa platform. Our project is far from the type of third-party apps you typically see for Amazon's voice assistant -- "Alexa, Play Jeopardy!" and "Alexa, Ask Pikachu to Talk" are two popular examples -- as it invites users to engage with Alexa in a way that's just a bit… off. Alexa, Call Mom! leads participants through an immersive séance experience. It is a parodic reimaging of the classic horror séance and an exploration of the tense relationships we share with conversational devices in our home.

One of the main challenges that every data scientist face is model deployment. Unless you are one of the lucky few who has loads of data engineers to help you deploy a model, it's really an issue in enterprise projects. I am not even implying that the model needs to be production ready but even a seemingly basic issue of making the model and insights accessible to business users is more of a hassle then it needs to be. These are two ends of the spectrum. Ad-hoc runs are just too tedious and clients typically demand for some self-serve interface but good luck trying to get a permanent server to host your code.

We will now explore the most important ones.

Twitter is a popular social network where users can share short SMS-like messages called tweets. Users share thoughts, links and pictures on Twitter, journalists comment on live events, companies promote products and engage with customers. The list of different ways to use Twitter could be really long, and with 500 millions of tweets per day, there's a lot of data to analyse and to play with. This is the first in a series of articles dedicated to mining data on Twitter using Python. In this first part, we'll see different options to collect data from Twitter.