Using NLP and sentiment analysis dictionaries, different features are computed. NLP and sentiment analysis is a must for the visual introduction of machine learning. A brief feature engineering is performed to get realistic results. Out of all computed features, the most outperformed features are selected for the Machine Learning model. The outperformed features are computed using various techniques that include information gain, gain ratio, and correlation score.

Japan shouldn't ignore the data security risks posed by the Chinese video app TikTok, a senior ruling party official said. "Not only President Trump but also other countries such as the U.K. and India, are gradually becoming aware of the risks," Akira Amari, the ruling Liberal Democratic Party's tax panel chief, said Sunday on Fuji Television Network. "Since there are so many countries pointing out the risks, Japan cannot just stand by and watch." U.S. President Donald Trump on Friday ordered ByteDance Ltd., TikTok's Chinese owner, to sell its U.S. assets. Trump cited national security grounds, delivering the latest salvo in his standoff with Beijing.

Recent advances in information extraction have motivated the automatic construction of huge Knowledge Graphs (KGs) by mining from large-scale text corpus. However, noisy facts are unavoidably introduced into KGs that could be caused by automatic extraction. To validate the correctness of facts (i.e., triplets) inside a KG, one possible approach is to map the triplets into vector representations by capturing the semantic meanings of facts. Although many representation learning approaches have been developed for knowledge graphs, these methods are not effective for validation. They usually assume that facts are correct, and thus may overfit noisy facts and fail to detect such facts. Towards effective KG validation, we propose to leverage an external human-curated KG as auxiliary information source to help detect the errors in a target KG. The external KG is built upon human-curated knowledge repositories and tends to have high precision. On the other hand, although the target KG built by information extraction from texts has low precision, it can cover new or domain-specific facts that are not in any human-curated repositories. To tackle this challenging task, we propose a cross-graph representation learning framework, i.e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently. This is achieved by embedding triplets based on their semantic meanings, drawing cross-KG negative samples and estimating a confidence score for each triplet based on its degree of correctness. We evaluate the proposed framework on datasets across different domains. Experimental results show that the proposed framework achieves the best performance compared with the state-of-the-art methods on large-scale KGs.

The internet is flooded with numerous opinions, reviews, suggestions, making brands need a way to categorize them into the good, the bad, the ugly, the emergency and the neutral sections. To prioritize whom to respond to first, and understand how the consumers feel about certain services or products. For this, businesses need the right metrics to understand why customers react positively or negatively with their brand. Hence, brands are paying more attention to sentiment analysis, which basically uses AI and machine learning to study customer feedback. Sentiment analytics tools help in measuring the brand health by analyzing KPIs like brand awareness, brand reputation, and brand's share of voice.

The winning of the brand comparison is the only motto of any business brand in the market. The only solution is social media monitoring, where sentiment analysis should be conducted. Social media is flooded with more audience or customers' opinions, and the business brands can trace those sentiments prioritizing the positive, negative, and neutral social mentions. Depending on that, they can categorize the customers responding first, and the brands can understand why the customers are positive or negative reactions towards their brand. To make effective use of it, the businesses can go through the below-mentioned sentiment analysis tools that you can find nowhere.

Take for example, the loan origination and loan servicing process in a financial institution. There are 5 key activities amongst several that if changed can fuel better productivity. So, if an AI engine is in place at activity 2, it can process customer data regarding financial history and propensity to pay etc. and flag potential defaulters or fraudsters. Similarly, AI-based chat bots can help improve customer service (activity 4) by either automating the transaction completely or offering sentiment-analysis based insights to agents for better customer experience(see Figure 1). Bringing technology in these areas will improve productivity and reduce cost and effort, validating investment.

As expected, artificial intelligence (AI) and machine learning (ML) applications are already having an impact on society. Many industries that we tap into daily--such as banking, financial services and insurance (BFSI), and digitized health care--can benefit from AI and ML applications to help them optimize mission-critical operations and execute functions in real time. The BFSI sector is an early adopter of AI and ML capabilities. Natural language processing (NLP) is being implemented for personal identifiable information (PII) privacy compliance, chatbots and sentiment analysis; for example, mining social media data for underwriting and credit scoring, as well as investment research. Predictive analytics assess which assets will yield the highest returns.

Natural Language Processing is considered one of the many critical aspects of making intelligent systems. By training your solution with data gathered from the real-world, you can make it faster and more relevant to users, generating crucial insight about your customer base. In this article, we will be taking a look at how Python offers some of the most useful and powerful libraries for leveraging the power of Natural Language Processing into your project and where exactly do they fit in. Often recognized as a professional-grade Python library for advanced Natural Language Processing, spaCy excels at working with incredibly large-scale information extraction tasks. Built using Python and Cython, spaCy combines the best of both languages, the convenience from Python and the speed from Cython to deliver one of the best-in-class NLP experiences. Stanford CoreNLP is a suite of tools built for implementing a Natural Language Processing into your project.

Basic Sentiment Analysis with TensorFlow Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow. Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow. In this project, you will learn the basics of using Keras with TensorFlow as its backend and you will learn to use it to solve a basic sentiment analysis problem. By the end of this 2-hour long project, you will have created, trained, and evaluated a Neural Network model that, after the training, will be able to predict movie reviews as either positive or negative reviews – classifying the sentiment of the review text. Welcome to this project-based course on Basic Sentiment Analysis with TensorFlow.

Having understood the context and starting point, now we will go a little further with the interaction between our two individuals and their open relationship (still maintaining their anonymity, of course, as "Él" (He) and "Ella" (She)), analyzing the diversity of vocabulary and performing sentiment analysis based on the expressed emojis. Okay, so going back, using the same libraries, same defined variables, and the same txt file so far, let's continue. You will remember that in the first part, using the stopwords() function, we discriminate the words whose meaning is little or nothing relevant. Based on this and looking for words that are repeated only by the same user, we can measure the diversity of vocabulary. So we will obtain as a result the following plot where we can see that She is the one who has the greatest diversity of lexicon.