Key Phrase Extraction: Using this function, you can feed big chunks of unstructured text to the system and get a list of key phrases. Unlike sentiment analysis, this function can deliver better results if you provide text in bigger blocks. Language Detection: This function analyzes the input text and provides the ISO identifier and language name. It can be leveraged to evaluate data columns in which the language of the text in not known. Currently about 120 languages are supported. Image Tagging: This function supports tagging of 2000 recognizable objects, living species, environmental settings, and actions.

Sentiment quantification is the task of estimating the relative frequency (or "prevalence") of sentiment-related classes (such as Positive, Neutral, Negative) in a sample of unlabelled texts; this is especially important when these texts are tweets, since most sentiment classification endeavours carried out on Twitter data actually have quantification (and not the classification of individual tweets) as their ultimate goal. It is well-known that solving quantification via "classify and count" (i.e., by classifying all unlabelled items via a standard classifier and counting the items that have been assigned to a given class) is suboptimal in terms of accuracy, and that more accurate quantification methods exist. In 2016, Gao and Sebastiani carried out a systematic comparison of quantification methods on the task of tweet sentiment quantification. In hindsight, we observe that the experimental protocol followed in that work is flawed, and that its results are thus unreliable. We now re-evaluate those quantification methods on the very same datasets, this time following a now consolidated and much more robust experimental protocol, that involves 5775 as many experiments as run in the original study. Our experimentation yields results dramatically different from those obtained by Gao and Sebastiani, and thus provide a different, much more solid understanding of the relative strengths and weaknesses of different sentiment quantification methods.

Sentiment Analysis can be defined as the process of analyzing text data and categorizing them into Positive, Negative, or Neutral sentiments. Sentiment Analysis is used in many cases like Social Media Monitoring, Customer service, Brand Monitoring, political campaigns, etc. Analyzing customer feedback such as social media conversations, product reviews, and survey responses allows companies to understand the customer's emotions better which is becoming more essential to meet their needs. It is almost impossible to manually sort thousands of social media conversations, customer reviews, and surveys. So we have to use either ML/DL to build a model that analyzes the text data and performs the required operations. The problem I am trying to solve here is part of this Kaggle competition.

Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent.

Being to new text analytics, I haven't gotten the hang of my typical ML workflow given how long processes take to run in the commonly large feature space of text analytics. I would like to know what the typical strategy is to balance effort/time in terms of optimizing transformation decision, feature down-selection, and model tuning. In an effort to get a sense of which of the decision points above I should run further tuning on, I ran untuned RF, Logistic, Naive Bayes, SGD, and KNN models on (with cross validation). No clear decision point was commonly "better" in the resulting f-1 scores, and the difference is often noteworthy. As I have no bias towards a particular algorithm type (only the best f-1 score), I'm stuck in a quandry-- I have not successfully narrowed my decision space enough.

Deep neural networks and other intricate Artificial Intelligence (AI) models have reached high levels of accuracy on many biomedical natural language processing tasks. However, their applicability in real-world use cases may be limited due to their vague inner working and decision logic. A post-hoc explanation method can approximate the behavior of a black-box AI model by extracting relationships between feature values and outcomes. In this paper, we introduce a post-hoc explanation method that utilizes confident itemsets to approximate the behavior of black-box classifiers for medical information extraction. Incorporating medical concepts and semantics into the explanation process, our explanator finds semantic relations between inputs and outputs in different parts of the decision space of a black-box classifier. The experimental results show that our explanation method can outperform perturbation and decision set based explanators in terms of fidelity and interpretability of explanations produced for predictions on a disease-treatment information extraction task.

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that studies how machines understand human language. Its goal is to build systems that can make sense of text and perform tasks like translation, grammar checking, or topic classification. Companies are increasingly using NLP-equipped tools to gain insights from data and to automate routine tasks. This sentiment analyzer, for instance, can help brands detect emotions in text, such as negative comments on social media. But what exactly is Natural Language Processing?

Social media has penetrated into multilingual societies, however most of them use English to be a preferred language for communication. So it looks natural for them to mix their cultural language with English during conversations resulting in abundance of multilingual data, call this code-mixed data, available in todays' world.Downstream NLP tasks using such data is challenging due to the semantic nature of it being spread across multiple languages.One such Natural Language Processing task is sentiment analysis, for this we use an auto-regressive XLNet model to perform sentiment analysis on code-mixed Tamil-English and Malayalam-English datasets.

Aspect-based sentiment analysis (ABSA) and Targeted ASBA (TABSA) allow finer-grained inferences about sentiment to be drawn from the same text, depending on context. For example, a given text can have different targets (e.g., neighborhoods) and different aspects (e.g., price or safety), with different sentiment associated with each target-aspect pair. In this paper, we investigate whether adding context to self-attention models improves performance on (T)ABSA. We propose two variants of Context-Guided BERT (CG-BERT) that learn to distribute attention under different contexts. We first adapt a context-aware Transformer to produce a CG-BERT that uses context-guided softmax-attention. Next, we propose an improved Quasi-Attention CG-BERT model that learns a compositional attention that supports subtractive attention. We train both models with pretrained BERT on two (T)ABSA datasets: SentiHood and SemEval-2014 (Task 4). Both models achieve new state-of-the-art results with our QACG-BERT model having the best performance. Furthermore, we provide analyses of the impact of context in the our proposed models. Our work provides more evidence for the utility of adding context-dependencies to pretrained self-attention-based language models for context-based natural language tasks.

Helen Dixon, head of Ireland's Data Protection Commission, in May submitted a draft decision to more than two dozen of the bloc's privacy regulators for review, as required under the law. Eleven regulators objected to the proposed ruling, sparking a lengthy dispute-resolution mechanism, she said. The contents of the draft decision haven't been disclosed. Twitter's European operations are based in Dublin. "It's a long process," Ms. Dixon said at The Wall Street Journal's virtual CIO Network conference.