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.

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. 

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?

This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Significant progress has been achieved recently on the development of DST technologies. However, building a DST module that is scalable and effective is still a challenging issue. This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. It employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue. Seq2Seq-DU has the following advantages. It can jointly model the relations between intents, slots, and slot values; it can utilize rich language representations of utterances and schemas; it can effectively deal with categorical slots, non-categorical slots, and unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system. Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2, WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.

We present a new approach to dialogue specification for Virtual Personal Assistants (VPAs) based on so-called dialogue workflow graphs, with several demonstrated advantages over current ontology-based methods. Our new dialogue specification language (DSL) enables customers to more easily participate in the VPA modeling process due to a user-friendly modeling framework. Resulting models are also significantly more compact. VPAs can be developed much more rapidly. The DSL is a new modeling layer on top of our ontology-based Dialogue Management (DM) framework OntoVPA. We explain the rationale and benefits behind the new language and support our claims with concrete reduced Level-of-Effort (LOE) numbers from two recent OntoVPA projects.

Goal-oriented dialog systems enable users to complete specific goals like requesting information about a movie or booking a ticket. Typically the dialog system pipeline contains multiple ML models, including natural language understanding, state tracking and action prediction (policy learning). These models are trained through a combination of supervised or reinforcement learning methods and therefore require collection of labeled domain specific datasets. However, collecting annotated datasets with language and dialog-flow variations is expensive, time-consuming and scales poorly due to human involvement. In this paper, we propose an approach for automatically creating a large corpus of annotated dialogs from a few thoroughly annotated sample dialogs and the dialog schema. Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal. We validate our approach by generating data and training three different downstream conversational ML models. We achieve 18 ? 50% relative accuracy improvements on a held-out test set compared to a baseline dialog generation approach that only samples natural language and entity value variations from existing catalogs but does not generate any novel dialog flow variations. We also qualitatively establish that the proposed approach is better than the baseline. Moreover, several different conversational experiences have been built using this method, which enables customers to have a wide variety of conversations with Alexa.