Sentiment analysis is the automated process of tagging data according to their sentiment, such as positive, negative and neutral. Sentiment analysis allows companies to analyze data at scale, detect insights and automate processes. In the past, sentiment analysis used to be limited to researchers, machine learning engineers or data scientists with experience in natural language processing. However, the AI community has built awesome tools to democratize access to machine learning in recent years. Nowadays, you can use sentiment analysis with a few lines of code and no machine learning experience at all!

Due to this complexity, research into online hate The rapid integration of the Internet into our daily lives has led to is fragmented throughout numerous disciplines. Despite all these many benefits but also to a number of new, wide-spread threats extensive approaches and methods proposed to analyse online hate such as online hate, trolling, bullying, and generally aggressive [1, 12], limited research has investigated how hateful behaviours behaviours. While research has traditionally explored online hate, and content compare and relate across different online platforms in particular, on one platform, the reality is that such hate is a [8]. It has only recently been recognised within academic literature phenomenon that often makes use of multiple online networks. In that online hate is not simply an issue for a select few platforms, this article, we seek to advance the discussion into online hate by rather networks of hate are often linked across these platforms, harnessing a comparative approach, where we make use of various forming a global'network of networks' dynamic [6]. Natural Language Processing (NLP) techniques to computationally Our study applies various computational methods, including analyse hateful content from Reddit and 4chan relating to the 2020 topic modelling and sentiment analysis, to explore the type of US Presidential Elections. Our findings show how content and content that is promoted on Reddit and 4chan to provide unique posting activity can differ depending on the platform being used.

Multilingual task-oriented dialogue (ToD) facilitates access to services and information for many (communities of) speakers. Nevertheless, the potential of this technology is not fully realised, as current datasets for multilingual ToD - both for modular and end-to-end modelling - suffer from severe limitations. 1) When created from scratch, they are usually small in scale and fail to cover many possible dialogue flows. 2) Translation-based ToD datasets might lack naturalness and cultural specificity in the target language. In this work, to tackle these limitations we propose a novel outline-based annotation process for multilingual ToD datasets, where domain-specific abstract schemata of dialogue are mapped into natural language outlines. These in turn guide the target language annotators in writing a dialogue by providing instructions about each turn's intents and slots. Through this process we annotate a new large-scale dataset for training and evaluation of multilingual and cross-lingual ToD systems. Our Cross-lingual Outline-based Dialogue dataset (termed COD) enables natural language understanding, dialogue state tracking, and end-to-end dialogue modelling and evaluation in 4 diverse languages: Arabic, Indonesian, Russian, and Kiswahili. Qualitative and quantitative analyses of COD versus an equivalent translation-based dataset demonstrate improvements in data quality, unlocked by the outline-based approach. Finally, we benchmark a series of state-of-the-art systems for cross-lingual ToD, setting reference scores for future work and demonstrating that COD prevents over-inflated performance, typically met with prior translation-based ToD datasets.

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria (Hausa, Igbo, Nigerian-Pidgin, and Yor\`ub\'a ) consisting of around 30,000 annotated tweets per language (and 14,000 for Nigerian-Pidgin), including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a rangeof pre-trained models and transfer strategies on the dataset. We find that language-specific models and language-adaptivefine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivizeresearch on sentiment analysis in under-represented languages.

Turning machine learning models into actual applications other people can use is not something that is covered in most AI and Machine Learning Tutorials. In this article, we are going to create an end-to-end AI Sentiment Analysis web application using Gradio and hugging face transformers. According to Wikipedia, Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. In simple words, Sentiment Analysis is the ability of Artificial Intelligence to analyze a sentence or block of text and get the emotions behind that sentence or block of text. Gradio is an open-source python library that allows you to quickly create easy-to-use, customizable UI components for your ML model, any API, or any arbitrary function in just a few lines of code. Gradio makes it very easy for you to build Graphical User Interfaces and deploy machine learning models.

Sentiment analysis, also called opinion mining, is a typical application of Natural Language Processing (NLP) widely used to analyze a given sentence or statement's overall effect and underlying sentiment. A sentiment analysis model classifies the text into positive or negative (and sometimes neutral) sentiments in its most basic form. Therefore naturally, the most successful approaches are using supervised models that need a fair amount of labelled data to be trained. Providing such data is an expensive and time-consuming process that is not possible or readily accessible in many cases. Additionally, the output of such models is a number implying how similar the text is to the positive examples we provided during the training and does not consider nuances such as sentiment complexity of the text.

From the perspective of a dialog system, it is important to identify the intention behind the segments in a dialog, since it provides an important cue regarding the information that is present in the segments and how they should be interpreted. ISO 24617-2, the standard for dialog act annotation, defines a hierarchically organized set of general-purpose communicative functions which correspond to different intentions that are relevant in the context of a dialog. We explore the automatic recognition of these communicative functions in the DialogBank, which is a reference set of dialogs annotated according to this standard. To do so, we propose adaptations of existing approaches to flat dialog act recognition that allow them to deal with the hierarchical classification problem. More specifically, we propose the use of an end-to-end hierarchical network with cascading outputs and maximum a posteriori path estimation to predict the communicative function at each level of the hierarchy, preserve the dependencies between the functions in the path, and decide at which level to stop. Furthermore, since the amount of dialogs in the DialogBank is small, we rely on transfer learning processes to reduce overfitting and improve performance. The results of our experiments show that our approach outperforms both a flat one and hierarchical approaches based on multiple classifiers and that each of its components plays an important role towards the recognition of general-purpose communicative functions.

In today's digital world businesses need to make sense of online reviews and analyze what customers are trying to tell them. They can do this using AI-powered text analytics and sentiment analysis. One of the basic lessons that all companies should follow is that success lies in the hands of their customers. Understanding how those customers feel about your product or service is essential to financial survival and prosperity. In this blog, we understand the process of sentiment analysis on reviews and how it can help businesses improve their products and services.

You're likely familiar with the saying, "Texting is a brilliant way to miscommunicate how you feel and misinterpret what other people mean." You've probably even experienced it directly! Substitute "texting" with "email" or "online reviews" and you've struck the nerve of businesses worldwide. Gaining a proper understanding of what clients and consumers have to say about your product or service or, more importantly, how they feel about your brand, is a universal struggle for businesses everywhere. What if I told you it doesn't have to be this way?

Task-oriented dialogue (TOD) systems are required to identify key information from conversations for the completion of given tasks. Such information is conventionally specified in terms of intents and slots contained in task-specific ontology or schemata. Since these schemata are designed by system developers, the naming convention for slots and intents is not uniform across tasks, and may not convey their semantics effectively. This can lead to models memorizing arbitrary patterns in data, resulting in suboptimal performance and generalization. In this paper, we propose that schemata should be modified by replacing names or notations entirely with natural language descriptions. We show that a language description-driven system exhibits better understanding of task specifications, higher performance on state tracking, improved data efficiency, and effective zero-shot transfer to unseen tasks. Following this paradigm, we present a simple yet effective Description-Driven Dialog State Tracking (D3ST) model, which relies purely on schema descriptions and an "index-picking" mechanism. We demonstrate the superiority in quality, data efficiency and robustness of our approach as measured on the MultiWOZ (Budzianowski et al.,2018), SGD (Rastogi et al., 2020), and the recent SGD-X (Lee et al., 2021) benchmarks.