Sentiment Analysis, as the name suggests, it means to identify the view or emotion behind a situation. It basically means to analyze and find the emotion or intent behind a piece of text or speech or any mode of communication. In this article, we will focus on the sentiment analysis of text data. We, humans, communicate with each other in a variety of languages, and any language is just a mediator or a way in which we try to express ourselves. And, whatever we say has a sentiment associated with it.

Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again. In this course, we learn all you need to know to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR. Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.

The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. We discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.

Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect-opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i.e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies). Especially, we design novel self-supervised strategies for ABSA, which have strengths in dealing with multiple aspects. Experimental results show that DCRAN significantly outperforms previous state-of-the-art methods by large margins on three widely used benchmarks.

Investors and activists are presenting Alphabet, Amazon, Facebook, and Twitter with a list of shareholder resolutions this week that call for investigations into alleged racial bias in Amazon's facial recognition software and other surveillance products, stronger safeguards against the spread of disinformation on Facebook, and the establishment of stronger worker and human rights protections at all four companies. Shareholder advocates and activist allies held a press conference on Monday detailing several resolutions being presented this week and next to the boards of Alphabet, Amazon, Facebook, and Twitter. While the advocates didn't expect the resolutions to pass -- some of the company boards have reportedly already advised shareholders to vote against them -- an Alphabet union representative said her union might organize walkouts if Alphabet doesn't adopt the worker protection and civil and human rights reforms being presented to its board next month.

Regulators have opened an investigation into Facebook amid concerns it is using its vast troves of personal data to push its own shopping and data tools. The probe by the UK's competition regulator will examine whether it is abusing its dominant position in online advertising. It comes amid growing antitrust concerns about the way many technology companies – not just Facebook but others such as Apple – have been able to use their vast size and hold on the market to unfairly benefit themselves. The Competition and Markets Authority (CMA) will look into how the social network gathers and uses certain data and whether it may provide an unfair advantage over rivals in the online classified ads and online dating space. As well as Facebook's advertising services, Facebook Login, a feature that allows people to sign into other websites and apps, will also form part of the probe.

Sentiment analysis (SA) has become an extensive research area in recent years impacting diverse fields including ecommerce, consumer business, and politics, driven by increasing adoption and usage of social media platforms. It is challenging to extract topics and sentiments from unsupervised short texts emerging in such contexts, as they may contain figurative words, strident data, and co-existence of many possible meanings for a single word or phrase, all contributing to obtaining incorrect topics. Most prior research is based on a specific theme/rhetoric/focused-content on a clean dataset. In the work reported here, the effectiveness of BERT(Bidirectional Encoder Representations from Transformers) in sentiment classification tasks from a raw live dataset taken from a popular microblogging platform is demonstrated. A novel T-BERT framework is proposed to show the enhanced performance obtainable by combining latent topics with contextual BERT embeddings. Numerical experiments were conducted on an ensemble with about 42000 datasets using NimbleBox.ai platform with a hardware configuration consisting of Nvidia Tesla K80(CUDA), 4 core CPU, 15GB RAM running on an isolated Google Cloud Platform instance. The empirical results show that the model improves in performance while adding topics to BERT and an accuracy rate of 90.81% on sentiment classification using BERT with the proposed approach.

Information extraction (IE) from visually-rich documents (VRDs) has achieved SOTA performance recently thanks to the adaptation of Transformer-based language models, which demonstrates great potential of pre-training methods. In this paper, we present a new approach to improve the capability of language model pre-training on VRDs. Firstly, we introduce a new IE model that is query-based and employs the span extraction formulation instead of the commonly used sequence labelling approach. Secondly, to further extend the span extraction formulation, we propose a new training task which focuses on modelling the relationships between semantic entities within a document. This task enables the spans to be extracted recursively and can be used as both a pre-training objective as well as an IE downstream task. Evaluation on various datasets of popular business documents (invoices, receipts) shows that our proposed method can improve the performance of existing models significantly, while providing a mechanism to accumulate model knowledge from multiple downstream IE tasks.

Let's say you've built an NLP model for some specific task, whether it be text classification, question answering, translation, or what have you. You've tested it out locally and it performs well. You've had others test it out as well, and it continues to perform well. Now you want to roll it out to a larger audience, be that audience a team of developers you work with, a specific group of end users, or even the general public. You have decided that you want to do so using a REST API, as you find this to be your best option.

In the past decade, there has been much discussion about the issue of biased reporting in clinical research. Despite this attention, there have been limited tools developed for the systematic assessment of qualitative statements made in clinical research, with most studies assessing qualitative statements relying on the use of manual expert raters, which limits their size. Also, previous attempts to develop larger scale tools, such as those using natural language processing, were limited by both their accuracy and the number of categories used for the classification of their findings. With these limitations in mind, this study's goal was to develop a classification algorithm that was both suitably accurate and finely grained to be applied on a large scale for assessing the qualitative sentiment expressed in clinical trial abstracts. Additionally, this study seeks to compare the performance of the proposed algorithm, GAN-BioBERT, to previous studies as well as to expert manual rating of clinical trial abstracts. This study develops a three-class sentiment classification algorithm for clinical trial abstracts using a semi-supervised natural language process model based on the Bidirectional Encoder Representation from Transformers (BERT) model, from a series of clinical trial abstracts annotated by a group of experts in academic medicine. Results: The use of this algorithm was found to have a classification accuracy of 91.3%, with a macro F1-Score of 0.92, which is a significant improvement in accuracy when compared to previous methods and expert ratings, while also making the sentiment classification finer grained than previous studies. The proposed algorithm, GAN-BioBERT, is a suitable classification model for the large-scale assessment of qualitative statements in clinical trial literature, providing an accurate, reproducible tool for the large-scale study of clinical publication trends.