Rapid advancements in predictive and prescriptive analytics have seemingly surpassed the overall utility of descriptive analytics. But as we strive to determine what will happen, and to prepare accordingly using technologies like machine learning, it is easy to forget the main value proposition of descriptive analytics which, although less celebrated, continues to endure. Descriptive analytics doesn't reveal what might happen, what should happen, or what your plan of action should be. Instead, it illustrates something much more concrete--what actually did happen and, with the proper analysis, what to do to get the most advantageous outcome out of a situation. Sentiment analysis is perhaps one of the most pervasive use cases for descriptive analytics today.

Sentiment Analysis or opinion mining is important for organisations, irrespective of industry. It helps organisations extract insights from social data and understand their customer base -- what they feel about the products and services and what else they expect from the company. Simply put, it tries to analyse the feelings of the customers hidden behind the words and it is able to do that by making use of a technology called Natural Language Processing (NLP). Today, to make the work a little easier for organisations and gain an overview of the wider public opinion behind certain topics, there are several tools available. And in this article, we are going to take a look at some of the tools one can use for sentiment analysis.

--This research proposes a new (old) metric for evaluating goodness of fit in topic models, the coefficient of determination, or R 2 . Within the context of topic modeling, R 2 has the same interpretation that it does when used in a broader class of statistical models. Reporting R 2 with topic models addresses two current problems in topic modeling: a lack of standard cross-contextual evaluation metrics for topic modeling and ease of communication with lay audiences. The author proposes that R 2 should be reported as a standard metric when constructing topic models. I NTRODUCTION According to an often-quoted but never cited definition, "the goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question." 1 Goodness of fit measures vary with the goals of those constructing the statistical model. Inferential goals may emphasize in-sample fit while predictive goals may emphasize out-of-sample fit. Prior information may be included in the goodness of fit measure for Bayesian models, or it may not. Goodness of fit measures may include methods to correct for model overfitting. In short, goodness of fit measures the performance of a statistical model against the ground truth of observed data. Fitting the data well is generally a necessary--though not sufficient--condition for trust in a statistical model, whatever its goals. Of course, goodness of fit is only one concern in statistical modeling.

In this paper we present a model for unsupervised topic discovery in texts corpora. The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents. These probabilities are used to recover by marginalization probabilities of words given documents. For very large corpora where the number of documents can be in the order of billions, using a neural auto-encoder based document embedding is more scalable then using a lookup table embedding as classically done. We thus extended the lookup based document embedding model to continuous auto-encoder based model. Our models are trained using probabilistic latent semantic analysis (PLSA) assumptions. We evaluated our models on six datasets with a rich variety of contents. Conducted experiments demonstrate that the proposed neural topic models are very effective in capturing relevant topics. Furthermore, considering perplexity metric, conducted evaluation benchmarks show that our topic models outperform latent Dirichlet allocation (LDA) model which is classically used to address topic discovery tasks.

End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While in non-collaborative settings, for example, negotiation and persuasion, users and systems do not share a common goal. As a result, compared to collaborate tasks, people use social content to build rapport and trust in these non-collaborative settings in order to advance their goals. To handle social content, we introduce a hierarchical intent annotation scheme, which can be generalized to different non-collaborative dialog tasks. Building upon Transfer-Transfo (Wolf et al. 2019), we propose an end-to-end neural network model to generate diverse coherent responses. Our model utilizes intent and semantic slots as the intermediate sentence representation to guide the generation process. In addition, we design a filter to select appropriate responses based on whether these intermediate representations fit the designed task and conversation constraints. Our non-collaborative dialog model guides users to complete the task while simultaneously keeps them engaged. We test our approach on our newly proposed A NTIS CAM dataset and an existing P ERSUASIONF ORG OOD dataset. Both automatic and human evaluations suggest that our model outperforms multiple baselines in these two non-collaborative tasks. Introduction Considerable progress has been made building end-to-end dialog systems for collaborative tasks in which users cooperate with the system to achieve a common goal. Examples of collaborative tasks include making restaurant reservations and retrieving bus timetable information. Since users typically have clear and explicit intentions in collaborative tasks, existing systems commonly classify user utterances into predefined intents. In contrast, non-collaborative tasks are those where the users and the system do not strive to achieve the same goal.

T ask-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context Yichi Zhang Tsinghua University Beijing, China zhangyic17@mails.tsinghua.edu.cn Abstract Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property leads to different valid dialog policies towards task completion. However, none of the existing task-oriented dialog generation approaches takes this property into account. We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. Specifically, we first use dialog states to summarize the dialog history, and then discover all possible mappings from every dialog state to its different valid system actions. During dialog system training, we enable the current dialog state to map to all valid system actions discovered in the previous process to create additional state-action pairs. By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. Experimental results show that the proposed framework consistently improves dialog policy diversity, and results in improved response diversity and appropriateness. Introduction One big challenge in dialog system generation is that multiple responses can be appropriate under the same conversation context. This challenge originated from the intrinsic diversity of human conversations. Although recent progress in sequence-to-sequence (seq2seq) learning (Sutskever, Vinyals, and Le 2014) improves dialog systems performance (Serban et al. 2017; Wen et al. 2017; Lei et al. 2018).

GuessWhat?! is a visual dialogue task between a guesser and an oracle. The guesser aims to locate an object supposed by the oracle oneself in an image by asking a sequence of Yes/No questions. Asking proper questions with the progress of dialogue is vital for achieving successful final guess. As a result, the progress of dialogue should be properly represented and tracked. Previous models for question generation pay less attention on the representation and tracking of dialogue states, and therefore are prone to asking low quality questions such as repeated questions. This paper proposes visual dialogue state tracking (VDST) based method for question generation. A visual dialogue state is defined as the distribution on objects in the image as well as representations of objects. Representations of objects are updated with the change of the distribution on objects. An object-difference based attention is used to decode new question. The distribution on objects is updated by comparing the question-answer pair and objects. Experimental results on GuessWhat?! dataset show that our model significantly outperforms existing methods and achieves new state-of-the-art performance. It is also noticeable that our model reduces the rate of repeated questions from more than 50% to 21.9% compared with previous state-of-the-art methods.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.

To have an effective conversation, it is important to understand the sentiment and respond appropriately. In a customer service call, a simple acknowledgment when talking to an unhappy customer might be helpful, such as, "Sorry to hear you are having trouble." Understanding sentiment is also useful in determining when you need to hand over the call to a human agent for additional support. To achieve such a conversational flow with a bot, you have to detect the sentiment expressed by the user and react appropriately. Previously, you had to build a custom integration by using Comprehend APIs.