For this project, I wanted to design a model that would do a simple classification of whether a phrase is positive or negative. Since I'm only looking for a binary result, I chose to use Sklearn's logistic regression module. If you were trying to predict more than two labels, you would have to use a different ML model. The data used is a corpus of 5,000 movie reviews -- 2,500 positive and 2,500 negative. The model has an accuracy of 90% and probably performs better with text that is similar to a review because it would more like the training data.

In this paper, we propose sentiment classification models based on BERT integrated with DRO (Distributionally Robust Classifiers) to improve model performance on datasets with distributional shifts. We added 2-Layer Bi-LSTM, projection layer (onto simplex or Lp ball), and linear layer on top of BERT to achieve distributionally robustness. We considered one form of distributional shift (from IMDb dataset to Rotten Tomatoes dataset). We have confirmed through experiments that our DRO model does improve performance on our test set with distributional shift from the training set.

We provide a hands-on introduction to optimized textual sentiment indexation using the R package sentometrics. Textual sentiment analysis is increasingly used to unlock the potential information value of textual data. The sentometrics package implements an intuitive framework to efficiently compute sentiment scores of numerous texts, to aggregate the scores into multiple time series, and to use these time series to predict other variables. The workflow of the package is illustrated with a built-in corpus of news articles from two major U.S. journals to forecast the CBOE Volatility Index.

I decided to sign up to Medium "by the other side" with the aim to publish my AI for Finance projects and empower my knowledge in these sectors, thanks to this great community! I love to analyse stocks and alternative assets prices and making inference using regressions, ensemble methods and sentiment analysis. I may still be not so capable of using Medium but I'll give it a shot! I'm so excited to start this!

Sentimental Analysis helps in quickly analyzing the numerous amount of data. Since Artificial Intelligence and its advanced technologies have started influencing different sectors. A lot of research work is taking place for developing different tools that can evolve Artificial Intelligence and Machine Learning more stronger. And Sentiment Analysis is one such topic that has created a buzz in the field of scientific and market research in the field of Natural Language Processing and Machine Learning with the help of its amazing applications. Basically, Sentiment Analysis is a Machine Learning tool.

In this paper we investigate a simple hypothesis for the Open Information Extraction (OpenIE) task, that it may be easier to extract some elements of an triple if the extraction is conditioned on prior extractions which may be easier to extract. We successfully exploit this and propose a neural multilingual OpenIE system that iteratively extracts triples by conditioning extractions on different elements of the triple leading to a rich set of extractions. The iterative nature of MiLIE also allows for seamlessly integrating rule based extraction systems with a neural end-to-end system leading to improved performance. MiLIE outperforms SOTA systems on multiple languages ranging from Chinese to Galician thanks to it's ability of combining multiple extraction pathways. Our analysis confirms that it is indeed true that certain elements of an extraction are easier to extract than others. Finally, we introduce OpenIE evaluation datasets for two low resource languages namely Japanese and Galician.

Document-level information extraction is a flexible framework compatible with applications where information is not necessarily localized in a single sentence. For example, key features of a diagnosis in radiology a report may not be explicitly stated, but nevertheless can be inferred from the report's text. However, document-level neural models can easily learn spurious correlations from irrelevant information. This work studies how to ensure that these models make correct inferences from complex text and make those inferences in an auditable way: beyond just being right, are these models "right for the right reasons?" We experiment with post-hoc evidence extraction in a predict-select-verify framework using feature attribution techniques. While this basic approach can extract reasonable evidence, it can be regularized with small amounts of evidence supervision during training, which substantially improves the quality of extracted evidence. We evaluate on two domains: a small-scale labeled dataset of brain MRI reports and a large-scale modified version of DocRED (Yao et al., 2019) and show that models' plausibility can be improved with no loss in accuracy.

In this article we will tackle the task of information extraction from semi-structured data (documents). We shortly cover the difficulties posed by the semi-structured nature of documents as well as the current solutions to ensure better extraction results. Paper documents are still an integral part of all areas of life. They appear in everyday life as invoices, contracts or user manuals. Their structure, purpose and content can therefore vary greatly.

For too many organizations, the intertwined technologies of artificial intelligence (AI) and machine learning (ML) have been long on promise but short on delivery. Increasingly, however, the fault lies not in these advanced technologies themselves, but in the challenges associated with deploying them easily and effectively to support everyday business processes. True, some AI proponents did over-promise on the field's capabilities and timetable in past decades. But a number of AI disciplines – everything from natural language processing to computer vision – have become incredibly sophisticated and powerful in recent years. AI technologies, in turn, are now powering object recognition, language translation, sentiment analysis, and a host of other use cases.

Listening to what's being said about your brand can be invaluable for any business. Humans can identify positive and negative sentiments, identify slang, sarcasm, irony, and more. However, the enormous volumes of chatter on the internet make it difficult to determine the overall public sentiments. No need to get anxious, that is exactly what sentiment analysis tools are for. Sentiment analysis tools can help you compile and analyze everything that's being said about your brand.