Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion. In recent years, Twitter opinion mining has attempted to combat these issues. Despite achieving promising results, it experiences its own set of shortcomings such as an unrepresentative sample population and a lack of long term stability. This paper aims to merge data from both these techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum. This paper demonstrates the effectiveness of the proposed approach using Twitter opinion data and survey data from trusted pollsters. Firstly, the possible existence of a time gap of 16 days between the two data sets is identified. This gap is subsequently incorporated into a proposed assimilation architecture. This method was found to adequately incorporate information from both sources and measure a strong upward trend in Leave support leading up to the Brexit referendum. The proposed technique provides useful estimates of true opinion, which is essential to future opinion measurement and forecasting research.

With the growth of social medias, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter -- the tweets -- have earned significant attention as a rich source of information to guide many decision-making processes. However, their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks, including sentiment analysis. Sentiment classification is tackled mainly by machine learning-based classifiers. The literature has adopted word representations from distinct natures to transform tweets to vector-based inputs to feed sentiment classifiers. The representations come from simple count-based methods, such as bag-of-words, to more sophisticated ones, such as BERTweet, built upon the trendy BERT architecture. Nevertheless, most studies mainly focus on evaluating those models using only a small number of datasets. Despite the progress made in recent years in language modelling, there is still a gap regarding a robust evaluation of induced embeddings applied to sentiment analysis on tweets. Furthermore, while fine-tuning the model from downstream tasks is prominent nowadays, less attention has been given to adjustments based on the specific linguistic style of the data. In this context, this study fulfils an assessment of existing language models in distinguishing the sentiment expressed in tweets by using a rich collection of 22 datasets from distinct domains and five classification algorithms. The evaluation includes static and contextualized representations. Contexts are assembled from Transformer-based autoencoder models that are also fine-tuned based on the masked language model task, using a plethora of strategies.

Natural Language Processing, also known as NLP, is a subfield of computer science, specifically artificial intelligence, that focuses on understanding written and spoken text. It covers various tasks some of which are speech recognition, sentiment analysis and language generation; And, it has been applied in several use cases such as machine translation, spam detection, virtual assistants and chatbots. The project covered in this article is a sentiment analysis project called Natural Language Processing with Disaster Tweets. Sentiment analysis is the process to extract subjective qualities from text such as emotion or attitude. The objective of the project is to identify if a specific tweet is a real disaster or not. The project is ideal for beginners in NLP.

"Sentiment" is a rather intriguing concept. It can mean attitude, feeling, bias, view, thought, and even something as deeply felt as emotion. Sentiment analysis that utilizes AI and machine learning has become a powerful tool for companies to understand how their customers and/or potential customers feel about their company. It can also be used for competitive analysis to take the temperature of a rival's products or services. "When you can't convince them with intellect, persuade them with sentiment," is author Amit Kalantri's recommendation.

Predictive machine learning models often lack interpretability, resulting in low trust from model end users despite having high predictive performance. While many model interpretation approaches return top important features to help interpret model predictions, these top features may not be well-organized or intuitive to end users, which limits model adoption rates. In this paper, we propose Intellige, a user-facing model explainer that creates user-digestible interpretations and insights reflecting the rationale behind model predictions. Intellige builds an end-to-end pipeline from machine learning platforms to end user platforms, and provides users with an interface for implementing model interpretation approaches and for customizing narrative insights. Intellige is a platform consisting of four components: Model Importer, Model Interpreter, Narrative Generator, and Narrative Exporter. We describe these components, and then demonstrate the effectiveness of Intellige through use cases at LinkedIn. Quantitative performance analyses indicate that Intellige's narrative insights lead to lifts in adoption rates of predictive model recommendations, as well as to increases in downstream key metrics such as revenue when compared to previous approaches, while qualitative analyses indicate positive feedback from end users.

Social media services like Facebook and Twitter will need to have databases of Russian users kept in Russia by July or face fines. The news was first reported by Interfax news agency, citing communications regulator Roskomnadzor as saying on Wednesday. Russia is considering legislation that would force foreign technology companies to open offices in Russia or face penalties such as advertising bans, as part of Moscow's wider efforts to exert greater control over Big Tech. Google and Facebook were fined on Tuesday for failing to delete content Moscow deems illegal, while Twitter has been the victim of a punitive slowdown since March. Facebook, Twitter and others must localise their databases of Russian users by July 1 or face a fine of up to 18 million roubles ($245,100) for non-compliance, the deputy head of Roskomnadzor Milos Wagner was cited as saying on Wednesday.

Its uniqueness lies in its'self-supervised', pre-training objective architecture. Unlike other models that infer on the meaning of a sentence by extracting small parts of it, Pegasus completely'masks' the sentence and tries to find it by reading the text before and after it. Pegasus is really good at data summarization, but it is also great at paraphrasing sentences. The model is extremely easy to use, doesn't require many dependencies and with just a few lines of code we'll have our augmented dataset ready for training. To be able to leverage our small dataset efficiently, we will be performing text Paraphrasing along with Synonym Replacement to come up with a dataset large and unique enough to train our Sentiment Analysis model with.

Social media has become a dominant form of communication for individuals, and for companies looking to market and sell their products and services. Properly understanding and responding to customer feedback on Twitter, Facebook and other social media platforms is critical for success, but it is incredibly labor intensive. That's where sentiment analysis comes in. The term refers to the automated process of identifying the emotion -- either positive, negative or neutral -- associated with text. While artificial intelligence refers to logical data analysis and response, sentiment analysis is akin to correctly identifying emotional communication.

Typically, information extraction (IE) requires a pipeline approach: first, a sequence labeling model is trained on manually annotated documents to extract relevant spans; then, when a new document arrives, a model predicts spans which are then post-processed and standardized to convert the information into a database entry. We replace this labor-intensive workflow with a transformer language model trained on existing database records to directly generate structured JSON. Our solution removes the workload associated with producing token-level annotations and takes advantage of a data source which is generally quite plentiful (e.g. database records). As long documents are common in information extraction tasks, we use gradient checkpointing and chunked encoding to apply our method to sequences of up to 32,000 tokens on a single GPU. Our Doc2Dict approach is competitive with more complex, hand-engineered pipelines and offers a simple but effective baseline for document-level information extraction. We release our Doc2Dict model and code to reproduce our experiments and facilitate future work.

At the beginning of the year, WhatsApp took the seemingly mundane step of updating its terms of use and privacy policy, mostly focused on the app's business offerings. The changes sparked a major backlash, though, because they inadvertently highlighted WhatsApp's years-old policy of sharing certain user data, like phone numbers, with parent company Facebook. Rather than change the policy that sparked the controversy, WhatsApp instead moved the deadline for users to accept it from the original date of February 8 to Saturday. If you haven't accepted the new policy by now, you'll start to see more pop-ups in WhatsApp outlining the changes with a big green Accept button at the bottom. If you tap it, WhatsApp will continue to share certain account data of yours with Facebook.