--In this paper, we propose a multimodal framework for speech emotion recognition that leverages entropy-aware score selection to combine speech and textual predictions. The proposed method integrates a primary pipeline that consists of an acoustic model based on wav2vec2.0 We propose a late score fusion approach based on entropy and varentropy thresholds to overcome the confidence constraints of primary pipeline predictions. Speech Emotion Recognition (SER), which aims to recognise emotions directly from voice inputs as discrete emotion classes [1], has become a crucial area of study in human-computer interaction, enhancing the emotional intelligence of virtual assistants, interactive robots, and mental health monitoring systems [2]. The rapid development of deep SER models, such as Convolutional Neural Networks (CNNs) [3], Recurrent Neural Networks (RNNs) [4], and Transformer-based architectures [5], [6], [7], has substantially improved recognition accuracy by capturing complex temporal and contextual patterns in speech.

Resources in high-resource languages have not been efficiently exploited in low-resource languages to solve language-dependent research problems. Spanish and French are considered high resource languages in which an adequate level of data resources for informal online social behavior modeling, is observed. However, a machine translation system to access those data resources and transfer their context and tone to a low-resource language like dialectal Arabic, does not exist. In response, we propose a framework that localizes contents of high-resource languages to a low-resource language/dialects by utilizing AI power. To the best of our knowledge, we are the first work to provide a parallel translation dataset from/to informal Spanish and French to/from informal Arabic dialects. Using this, we aim to enrich the under-resource-status dialectal Arabic and fast-track the research of diverse online social behaviors within and across smart cities in different geo-regions. The experimental results have illustrated the capability of our proposed solution in exploiting the resources between high and low resource languages and dialects. Not only this, but it has also been proven that ignoring dialects within the same language could lead to misleading analysis of online social behavior.

Sentiment analysis is a methodology for analysing text data and classifying the sentiment contained within it. It is a useful technique for every customer facing industry (retail, finance, telco, utilities, etc) which needs to understand how consumers are thinking about them and their products, features and services. Sentiment analysis is a key feature in understanding and predicting churn, developing more accurate customer segmentations and creating recommender systems which have a good take-up of product and service offerings. Today, organisations have access to vast amounts of digital data from multiple platforms, including social media, review platforms, chatbots and influencer marketing campaigns, as well as internal CRM and Enterprise Marketing Systems. This heterogeneous data environment means that multiple types of sentiment model may be needed to truly understand customers, with different models used for understanding emotions, opinions, future intent or what aspects of a product or service are liked or disliked.

In the age of digital interaction, person-to-person relationships existing on social media may be different from the very same interactions that exist offline. Examining potential or spurious relationships between members in a social network is a fertile area of research for computer scientists -- here we examine how relationships can be predicted between two unconnected people in a social network by using area under Precison-Recall curve and ROC. Modeling the social network as a signed graph, we compare Triadic model,Latent Information model and Sentiment model and use them to predict peer to peer interactions, first using a plain signed network, and second using a signed network with comments as context. We see that our models are much better than random model and could complement each other in different cases.

In a time where we are so interconnected, it is baffling that we can be just as alone. I remember feeling alone, and I'm sure we all have at some point in our lives. Everyone in the world has felt incompetent, damaged, or misguided. But in that we also have found new ways to adapt and grow through platforms that speak openly about mental health to new outlets for getting help. It's that human connection that makes us complete, but we still have a long ways to go from living in a world where we can fairly and effectively treat an individual's mental health issues. The barriers to mental health access are far more pronounced today than ever. Teens and adults alike are finding it harder to find access to care, and people are becoming more isolated than ever. Globally, more than 70% of people with mental illness receive no treatment whatsoever while a study by the World Health Organization found that between 30 and 80 percent of people with mental health issues don't seek treatment.

When developing an NLP model, it's a standard practice to test how well a model generalizes to unseen examples by evaluating it on a held-out dataset. Suppose we reach our target performance metric of 95% on a held-out dataset and thus deploy the model to production based on this single metric. But, when real users start using it, the story could be completely different than what our 95% performance metric was saying. Our model might perform poorly even on simple variations of the training text. In contrast, the field of software engineering uses a suite of unit tests, integration tests, and end-to-end tests to evaluate all aspects of the product for failures.

Natural Language Processing or NLP is an area of Data Science, Machine Learning and Linguistics which focuses on processing the language that people speak. NLP used to be one of the slowest developing areas. When Computer Vision has been using fancy neural networks since the dawn of AlexNet, NLP was lagging behind. In recent years the area is starting to get closer and closer to the development speed of CV. You might have heard about the Transformer, BERT, XLnet, and Ernie. What is NLP overall, how do machines understand our speech, and do they?

Elections are a vital part of democracy allowing people to vote for the candidate they think can best lead the country. A candidate's campaign aims to demonstrate to the public why they think they are the best choice. However, in this age of constant media coverage and digital communications, the candidate is scrutinized at every step. A single misquote or negative news about a candidate can be the difference between him winning or losing the election. It becomes crucial to have a public relations manager who can guide and direct the candidate's campaign by prioritizing specific campaign activities. One critical aspect of the PR manager's work is to understand the public perception of their candidate and improve public sentiment about the candidate.

The dataset used for training the model consists of public tweets which are already labelled -- positive or negative. There are lots of npm package that implement the Naive Bayes algorithm out there and most of them are very similar in the way they implement Bayes theorem. Some packages are a bundle of NLP algorithms. The package used in the demo is this. After initializing the bayes classifier in your node application, it is very easy to train the model and use it for real world applications.

Sentiment expression in microblog posts often reflects user's specific individuality due to different language habit, personal character, opinion bias and so on. Existing sentiment classification algorithms largely ignore such latent personal distinctions among different microblog users. Meanwhile, sentiment data of microblogs are sparse for individual users, making it infeasible to learn effective personalized classifier. In this paper, we propose a novel, extensible personalized sentiment classification method based on a variant of latent factor model to capture personal sentiment variations by mapping users and posts into a low-dimensional factor space. We alleviate the sparsity of personal texts by decomposing the posts into words which are further represented by the weighted sentiment and topic units based on a set of syntactic units of words obtained from dependency parsing results. To strengthen the representation of users, we leverage users following relation to consolidate the individuality of a user fused from other users with similar interests. Results on real-world microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.