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 …

Natural language processing (NLP) is one of the biggest fields of AI development. Numerous NLP solutions like chatbots, automatic speech recognition, and sentiment analysis programs can improve efficiency and productivity in various businesses around the world. 

Mausam is an Associate Professor of Computer Science department at IIT Delhi, and an affiliate faculty member at University of Washington, Seattle. His research explores several threads in artificial intelligence, including scaling probabilistic planning algorithms, large-scale information extraction over the Web, and enabling complex computation over crowdsourced platforms. He received his PhD from University of Washington in 2007 and a B.Tech. ArnetMiner, a global citation aggregator, has rated Mausam as the 25th most influential scholar in AI for 2019. He was recently awarded the AAAI Senior Member status for his long-term participation in AAAI and distinction in the field of artificial intelligence.

With all the mass reach social media has these days, the power that comes with riding on its wave is simply hard to deny. With thousands of posts and tweets, there is seemingly no end to the chatter. But it is important to know if all that chatter is in favor of or against your agendas. Imagine launching a product that has become the talk of the town. But is all that talk good or bad?