From a trader's point of view, there is one commodity that is worth infinitesimally more than any other. And it's not cutting-edge technology, advanced technical analysis, or profound macroeconomic insight – although these are undoubtedly hugely valuable – it's information. Not just any information – after all, the world is filled with more information than even the most powerful computers could hope to store, and the most intelligent brains could hope to begin to comprehend. No, there's one type of information that has the potential to give traders a bigger edge than any other, and that's the latest information. Information that the rest of the market has yet to factor into their equations.

This research question is one of the main motivations of our work to explain the prediction of model. Since most machine learning models provide no Twitter has been growing in popularity and now-a-days, it explanations for the predictions, their predictions is used everyday by people to express opinions about different are obscure for the human. The ability to explain topics, such as products, movies, health, music, politicians, a model's prediction has become a necessity events, among others. Twitter data constitutes a rich in many applications including Twitter mining. In source that can be used for capturing information about any this work, we propose a method called Explainable topic imaginable. This data can be used in different use cases Twitter Mining (Ex-Twit) combining Topic Modeling such as finding trends related to a specific keyword, measuring and Local Interpretable Model-agnostic Explanation brand sentiment, and gathering feedback about new products (LIME) to predict the topic and explain the and services. In this work, we use text mining to mine the model predictions. We demonstrate the effectiveness Twitter health-related data. Text mining is the application of of Ex-Twit on Twitter health-related data.

I hope you are enjoying the "Advanced Analytics Introduction" blog post series; here is a link to the previous segment (Step One) to provide some helpful background. In the previous installment, I provided an overview of the advanced analytics, data science and text analytics concepts. In this blog post, I review detailed definitions of text analytics and mining concepts to provide more context on this rapidly evolving market. In his book "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", John Elder, Ph.D., characterized the text analytics concept best when he stated the following: The diagrams below also come from the same publication by Dr. Elder. In this first diagram, the text mining field is separated into seven "practice areas."

City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the biggest channels of public expression and is often used to communicate opinions and content related to City Logistics. The idea of this research is that analysing social media content could help in understanding the public perception of City logistics. This paper offers a methodology for collecting content from Twitter and implementing Machine Learning techniques (Unsupervised Learning and Natural Language Processing), to perform content and sentiment analysis. The proposed methodology is applied to more than 110 000 tweets containing City Logistics key-terms. Results allowed the building of an Interest Map of concepts and a Sentiment Analysis to determine if City Logistics entries are positive, negative or neutral.

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models.

Nowadays social media is a huge platform of data. People usually share their interest, thoughts via discussions, tweets, status. It is not possible to go through all the data manually. We need to mine the data to explore hidden patterns or unknown correlations, find out the dominant topic in data and understand people's interest through the discussions. In this work, we explore Twitter data related to health. We extract the popular topics under different categories (e.g. diet, exercise) discussed in Twitter via topic modeling, observe model behavior on new tweets, discover interesting correlation (i.e. Yoga-Veganism). We evaluate accuracy by comparing with ground truth using manual annotation both for train and test data.

Twitter customer service interactions have recently emerged as an effective platform to respond and engage with customers. In this work, we explore the role of negation in customer service interactions, particularly applied to sentiment analysis. We define rules to identify true negation cues and scope more suited to conversational data than existing general review data. Using semantic knowledge and syntactic structure from constituency parse trees, we propose an algorithm for scope detection that performs comparable to state of the art BiLSTM. We further investigate the results of negation scope detection for the sentiment prediction task on customer service conversation data using both a traditional SVM and a Neural Network. We propose an antonym dictionary based method for negation applied to a CNN-LSTM combination model for sentiment analysis. Experimental results show that the antonym-based method outperforms the previous lexicon-based and neural network methods.

This paper studies the problem of learning a sequence of sentiment classification tasks. The learned knowledge from each task is retained and used to help future or subsequent task learning. This learning paradigm is called Lifelong Learning (LL). However, existing LL methods either only transfer knowledge forward to help future learning and do not go back to improve the model of a previous task or require the training data of the previous task to retrain its model to exploit backward/reverse knowledge transfer. This paper studies reverse knowledge transfer of LL in the context of naive Bayesian (NB) classification. It aims to improve the model of a previous task by leveraging future knowledge without retraining using its training data. This is done by exploiting a key characteristic of the generative model of NB. That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks. Experimental results show that the proposed method markedly outperforms existing LL baselines.

Our interest in this paper is in meeting a rapidly growing industrial With the rapid advancement of Deep Learning (DL) for computer demand for information extraction from images of documents such vision problems, many DL architectures are available today for as invoices, bills, receipts etc. In practice users are able to provide a document image understanding ([11], [18], [22], [28]). But like most very small number of example images labeled with the information DLbased techniques, training these models from scratch is resource that needs to be extracted. We adopt a novel'two-level''neurodeductive', and data intensive. This is a major stumbling block for industrial approach where (a) we use pre-trained deep neural problems for which collecting and annotating data incur significant networks to populate a relational database with facts about each costs in time and money. In this paper, we use two complementary document-image; and (b) we use a form of deductive reasoning, forms learning to address this problem: related to meta-interpretive learning of transition systems to learn extraction programs: Given task-specific transitions defined using (1) Neural-learning: Using pre-trained DL models for reading the entities and relations identified by the neural detectors and document images and converting them into a structured a small number of instances (usually 1, sometimes 2) of images form by populating a predefined database schema.

The state-of-the-art solutions for Aspect-Level Sentiment Analysis (ALSA) are built on a variety of deep neural networks (DNN), whose efficacy depends on large amounts of accurately labeled training data. Unfortunately, high-quality labeled training data usually require expensive manual work, and are thus not readily available in many real scenarios. In this paper, we aim to enable effective machine labeling for ALSA without the requirement for manual labeling effort. Towards this aim, we present a novel solution based on the recently proposed paradigm of gradual machine learning. It begins with some easy instances in an ALSA task, which can be automatically labeled by the machine with high accuracy, and then gradually labels the more challenging instances by iterative factor graph inference. In the process of gradual machine learning, the hard instances are gradually labeled in small stages based on the estimated evidential certainty provided by the labeled easier instances. Our extensive experiments on the benchmark datasets have shown that the performance of the proposed approach is considerably better than its unsupervised alternatives, and also highly competitive compared to the state-of-the-art supervised DNN techniques.