An expanding field of substantive interest for the theory of the law and the practice-of-law entails Legal Sentiment Analysis and Opinion Mining (LSAOM), consisting of two often intertwined phenomena and actions underlying legal discussions and narratives: (1) Sentiment Analysis (SA) for the detection of expressed or implied sentiment about a legal matter within the context of a legal milieu, and (2) Opinion Mining (OM) for the identification and illumination of explicit or implicit opinion accompaniments immersed within legal discourse. Efforts to undertake LSAOM have historically been performed by human hand and cognition, and only thinly aided in more recent times by the use of computer-based approaches. Advances in Artificial Intelligence (AI) involving especially Natural Language Processing (NLP) and Machine Learning (ML) are increasingly bolstering how automation can systematically perform either or both of Sentiment Analysis and Opinion Mining, all of which is being inexorably carried over into engagement within a legal context for improving LSAOM capabilities. This research paper examines the evolving infusion of AI into Legal Sentiment Analysis and Opinion Mining and proposes an alignment with the Levels of Autonomy (LoA) of AI Legal Reasoning (AILR), plus provides additional insights regarding AI LSAOM in its mechanizations and potential impact to the study of law and the practicing of law.

Understanding individuals' feelings are fundamental for organizations since clients can communicate their feelings and sentiments more transparently than ever before. By automatically analyzing customer feedback, from study reactions to social media discussions, brands can listen mindfully to their clients, and tailor products and services to address their issues. Sentiment analysis is a machine learning method that recognizes polarity (for example a positive or negative thought) within the text, whether a whole document, paragraph, sentence, or clause. Marketing is ending up being one of the artworks most disrupted by the digital revolution. A lot to the aversion of customary marketing proponents and maybe to the pleasure of technologists, it is presently a lot about codifying the whole knowledge chain – catching the abundance of digital data, sorting out it, applying algorithms to process it and taking care of back noteworthy decisions to different functions– all in real-time, with end to end automation, and at lightening quick speed.

Transformer models have shown impressive performance on a variety of NLP tasks. Off-the-shelf, pre-trained models can be fine-tuned for specific NLP classification tasks, reducing the need for large amounts of additional training data. However, little research has addressed how much data is required to accurately fine-tune such pre-trained transformer models, and how much data is needed for accurate prediction. This paper explores the usability of BERT (a Transformer model for word embedding) for gender prediction on social media. Forensic applications include detecting gender obfuscation, e.g. males posing as females in chat rooms. A Dutch BERT model is fine-tuned on different samples of a Dutch Twitter dataset labeled for gender, varying in the number of tweets used per person. The results show that finetuning BERT contributes to good gender classification performance (80% F1) when finetuned on only 200 tweets per person. But when using just 20 tweets per person, the performance of our classifier deteriorates non-steeply (to 70% F1). These results show that even with relatively small amounts of data, BERT can be fine-tuned to accurately help predict the gender of Twitter users, and, consequently, that it is possible to determine gender on the basis of just a low volume of tweets. This opens up an operational perspective on the swift detection of gender.

Proposed context-aware embeddings to refine the embeddings of targets and aspects using highly correlative words. A lot of previous work uses context-independent vectors to construct targets and aspects embeddings, which lead to loss of semantic information and failed to capture the interconnection between the specific target, its aspect, and its context. This approach has led to SOTA results in targeted aspect-based sentiment analysis (TABSA). The goal of TABSA is that given an input sentence, we want to extract the sentiment of the aspect that belongs to a target. The refined target embeddings can be computed by multiplying the sentence word embeddings X with the sparse coefficient vector u'.

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

There are two main features in SA. Sentiment Analysis is widely used in the area of Machine Learning under Natural Language Processing. In this course, you will know how to use sentiment analysis on reviews with the help of a NLP library called TextBlob. You will learn and develop a Flask based WebApp that takes reviews from the user and perform sentiment analysis on the same.

Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to "predict the future through analysing the past" – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans. Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new "alternative" data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.

In this paper, we describe our system submitted for SemEval 2020 Task 9, Sentiment Analysis for Code-Mixed Social Media Text alongside other experiments. Our best performing system is a Transfer Learning-based model that fine-tunes "XLM-RoBERTa", a transformer-based multilingual masked language model, on monolingual English and Spanish data and Spanish-English code-mixed data. Our system outperforms the official task baseline by achieving a 70.1% average F1-Score on the official leaderboard using the test set. For later submissions, our system manages to achieve a 75.9% average F1-Score on the test set using CodaLab username "ahmed0sultan".

Facebook, Twitter and YouTube have your data. The Trump administration said Friday that it would bar two popular Chinese-owned mobile apps WeChat and TikTok from U.S. app stores as of midnight Sunday, escalating the U.S. standoff with China. "Today's actions prove once again that President Trump will do everything in his power to guarantee our national security and protect Americans from the threats of the Chinese Communist Party," Commerce Secretary Wilbur Ross said in a statement. The Trump administration contends the data collected from American users by TikTok and WeChat could be accessed by the Chinese government. "The Trump administration is looking to make sure U.S. TikTok consumer data stays out of Beijing," said Wedbush Securities analyst Daniel Ives.

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with their own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector. To eliminate the majority of overhead from network communication, we also introduce a low-latency containerized version of our architecture. Finally, we demonstrate that the services we investigate are competitive on a variety of benchmarks, and present two applications of this framework to create intelligent search engines, and real time auto race analytics systems.