Students' perception of classes measured through their opinions on teaching surveys allows to identify deficiencies and problems, both in the environment and in the learning methodologies. The purpose of this paper is to study, through sentiment analysis using natural language processing (NLP) and machine learning (ML) techniques, those opinions in order to identify topics that are relevant for students, as well as predicting the associated sentiment via polarity analysis. As a result, it is implemented, trained and tested two algorithms to predict the associated sentiment as well as the relevant topics of such opinions. The combination of both approaches then becomes useful to identify specific properties of the students' opinions associated with each sentiment label (positive, negative or neutral opinions) and topic. Furthermore, we explore the possibility that students' perception surveys are carried out without closed questions, relying on the information that students can provide through open questions where they express their opinions about their classes.

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The opinion expressed in various Web sites and social-media is an essential contributor to the decision making process of several organizations. Existing sentiment analysis tools aim to extract the polarity (i.e., positive, negative, neutral) from these opinionated contents. Despite the advance of the research in the field, sentiment analysis tools give \textit{inconsistent} polarities, which is harmful to business decisions. In this paper, we propose SentiQ, an unsupervised Markov logic Network-based approach that injects the semantic dimension in the tools through rules. It allows to detect and solve inconsistencies and then improves the overall accuracy of the tools. Preliminary experimental results demonstrate the usefulness of SentiQ.

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.

Large end-to-end neural open-domain chatbots are becoming increasingly popular. However, research on building such chatbots has typically assumed that the user input is written in nature and it is not clear whether these chatbots would seamlessly integrate with automatic speech recognition (ASR) models to serve the speech modality. We aim to bring attention to this important question by empirically studying the effects of various types of synthetic and actual ASR hypotheses in the dialog history on TransferTransfo, a state-of-the-art Generative Pre-trained Transformer (GPT) based neural open-domain dialog system from the NeurIPS ConvAI2 challenge. We observe that TransferTransfo trained on written data is very sensitive to such hypotheses introduced to the dialog history during inference time. As a baseline mitigation strategy, we introduce synthetic ASR hypotheses to the dialog history during training and observe marginal improvements, demonstrating the need for further research into techniques to make end-to-end open-domain chatbots fully speech-robust. To the best of our knowledge, this is the first study to evaluate the effects of synthetic and actual ASR hypotheses on a state-of-the-art neural open-domain dialog system and we hope it promotes speech-robustness as an evaluation criterion in open-domain dialog.

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.