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.

Previous work in the social sciences, psychology and linguistics has show that liars have some control over the content of their stories, however their underlying state of mind may "leak out" through the way that they tell them. To the best of our knowledge, no previous systematic effort exists in order to describe and model deception language for Brazilian Portuguese. To fill this important gap, we carry out an initial empirical linguistic study on false statements in Brazilian news. We methodically analyze linguistic features using the Fake.Br corpus, which includes both fake and true news. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from.

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.

Topic modeling methods and algorithms are different from the use of rule-based text mining which uses keywords in a dictionary or regular expressions in search techniques but rather an unsupervised approach to finding and observing a bunch of words known as topics in large clusters of text. These bunch of words (topics) and patterns are hidden in across document but get discovered and noticed after training a topic model on it. The assumptions of topic models are that each document consist of a mixture of topics and each topic consists of a collection of words. There are many algorithms and methods for building and training a topic model of which some are Latent Semantic Analysis or Indexing (LSA), Probabilistic Latent Semantic Analysts (PLSA), Latent Dirichlet Allocation (LSA), Hierarchical Dirichlet Process (HDP) and others. Among all these, the most common and popular is LDA which is also the first algorithm that has been implemented in this open-source tool.

Can we develop visually grounded dialog agents that can efficiently adapt to new tasks without forgetting how to talk to people? Such agents could leverage a larger variety of existing data to generalize to new tasks, minimizing expensive data collection and annotation. In this work, we study a setting we call "Dialog without Dialog", which requires agents to develop visually grounded dialog models that can adapt to new tasks without language level supervision. By factorizing intention and language, our model minimizes linguistic drift after fine-tuning for new tasks. We present qualitative results, automated metrics, and human studies that all show our model can adapt to new tasks and maintain language quality. Baselines either fail to perform well at new tasks or experience language drift, becoming unintelligible to humans. Code has been made available at https://github.com/mcogswell/dialog_without_dialog

Designing task-oriented dialogue systems is a challenging research topic, since it needs not only to generate utterances fulfilling user requests but also to guarantee the comprehensibility. Many previous works trained end-to-end (E2E) models with supervised learning (SL), however, the bias in annotated system utterances remains as a bottleneck. Reinforcement learning (RL) deals with the problem through using non-differentiable evaluation metrics (e.g., the success rate) as rewards. Nonetheless, existing works with RL showed that the comprehensibility of generated system utterances could be corrupted when improving the performance on fulfilling user requests. In our work, we (1) propose modelling the hierarchical structure between dialogue policy and natural language generator (NLG) with the option framework, called HDNO; (2) train HDNO with hierarchical reinforcement learning (HRL), as well as suggest alternating updates between dialogue policy and NLG during HRL inspired by fictitious play, to preserve the comprehensibility of generated system utterances while improving fulfilling user requests; and (3) propose using a discriminator modelled with language models as an additional reward to further improve the comprehensibility. We test HDNO on MultiWoz 2.0 and MultiWoz 2.1, the datasets on multi-domain dialogues, in comparison with word-level E2E model trained with RL, LaRL and HDSA, showing a significant improvement on the total performance evaluated with automatic metrics.

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.