We live in a world which is more opinionated than ever. Any service that we consume leaves us either satisfied or unsatisfied. And with the advent of social media, we make our views public in no time. Vast sources of data are available in the form of reviews, customer satisfaction surveys, customer complaints, etc. Businesses can use this data to understand what customers are talking about, and make data driven decisions to improve their services. Let's talk in terms of Machine Learning now! Sentiment Analysis is the process of understanding how satisfied customers are w.r.t. a service.

Prediction and quantification of future volatility and returns play an important role in financial modelling, both in portfolio optimization and risk management. Natural language processing today allows to process news and social media comments to detect signals of investors' confidence. We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements. We investigated the strength of the correlation between sentiment measures on a given day and market volatility and returns observed the next day. The findings suggest that there is evidence of correlation between sentiment and stock market movements: the sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility. Also, in a surprising finding, for the sentiment found in Twitter comments we obtained a correlation coefficient of -0.7, and p-value below 0.05, which indicates a strong negative correlation between positive sentiment captured from the tweets on a given day and the volatility observed the next day. We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information by deploying topic modelling, based on Latent Dirichlet Allocation, to extract feature vectors from a collection of tweets and financial news. The obtained features were used as additional input to the classifier. Thanks to the combination of sentiment and topic modelling our classifier achieved a directional prediction accuracy for volatility of 63%.

This project's aim, is to explore the world of Natural Language Processing (NLP) by building what is known as a Sentiment Analysis Model. A sentiment analysis model is a model that analyses a given piece of text and predicts whether this piece of text expresses positive or negative sentiment. To this end, we will be using the sentiment140 dataset containing data collected from twitter. An impressive feature of this dataset is that it is perfectly balanced (i.e., the number of examples in each class is equal). Our approach was unique because our training data was automatically created, as opposed to having humans manual annotate tweets.

Now we have our API set up; we can begin pulling tweet data. We will focus on Tesla for this article. We will be using the requests library to interact with the Twitter API. We can search for the most recent tweets given a query through the /tweets/search/recent endpoint. We need two more parts before sending our request, (1) authorization and (2) a search query.

Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.

We present EmoCoder, a modular encoder-decoder architecture that generalizes emotion analysis over different tasks (sentence-level, word-level, label-to-label mapping), domains (natural languages and their registers), and label formats (e.g., polarity classes, basic emotions, and affective dimensions). Experiments on 14 datasets indicate that EmoCoder learns an interpretable language-independent representation of emotions, allows seamless absorption of state-of-the-art models, and maintains strong prediction quality, even when tested on unseen combinations of domains and label formats.

From virtual assistants to content moderation, sentiment analysis has a wide range of use cases. AI models that can recognize emotion and opinion have a myriad of applications in numerous industries. Therefore, there is a large growing interest in the creation of emotionally intelligent machines. The same can be said for the research being done in natural language processing (NLP). To highlight some of the work being done in the field, below are five essential papers on sentiment analysis and sentiment classification.

I prepared this tutorial because it is somehow very difficult to find a blog post with actual working BERT code from the beginning till the end. They are always full of bugs. So, I have dug into several articles, put together their codes, edited them, and finally have a working BERT model. So, just by running the code in this tutorial, you can actually create a BERT model and fine-tune it for sentiment analysis. Natural language processing (NLP) is one of the most cumbersome areas of artificial intelligence when it comes to data preprocessing.

Machine Learning with Core ML 2 and Swift 5 Learn how to integrate machine learning into your apps. Hands-on Swift 5 coding using CoreML 2, Vision, NLP and CreateML What you'll learn Description ** A practical and concise Core ML 2 course you can complete in less than three hours ** Extra Bonus: Free e-book version included (sells for $28.80 on Amazon)! Wouldn't it be great to integrate features like synthetic vision, natural language processing, or sentiment analysis into your apps? In this course, I teach you how to unleash the power of machine learning using Apple Core ML 2. I'll show you how to train and deploy models for natural language and visual recognition using Create ML. I'm going to familiarize you with common machine learning tasks.

Artificial intelligence technologies are now so advanced that even facets of technology like sentiment analysis, cutting edge in 2010, have been …