Investigating the role of Word Embeddings in sentiment analysis

During decades, Natural language processing (NLP) expanded its range of tasks, from document classification to automatic text summarization, sentiment analysis, text mining, machine translation, automatic question answering and others. In 2018, T. Young described NLP as a theory-motivated range of computational techniques for the automatic analysis and representation of human language. Outside and before AI, human language has been studied by specialists from various disciplines: linguistics, philosophy, logic, psychology. The aim of this work is to build a neural network to perform a sentiment analysis on Italian reviews from the chatbot customer service. Sentiment analysis is a data mining process which identifies and extracts subjective information from text. It could help to understand the social sentiment of clients, respect a business product or service. It could be a simple classification task that analyses a sentence and tells whether the underlying sentiment is positive or negative. The potentiality of deep learning techniques made this simple classification task evolve, creating new, more complex sentiment analysis, e.g. Intent Analysis and Contextual Semantic Search

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

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Measurement of associated W plus charm production in pp collisions at √s=7 TeV

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