Value of expressions behind the letter capitalization in product reviews

Product reviews from consumers are the source of opinions and expressions about purchased items or services. Thus, it is essential to understand the true meaning behind text reviews. One of the ways is to analyze sentiments, expressions and emotions behind the text. However, there are different styles of writing used in the text. One of widely used in the text is letter capitalization. It is commonly used to strengthen an expression or louder tone within the text. This paper explores the value of expression behind letter capitalization in product reviews. We compared fully capitalized text, text with one capitalized words and text without capitalization through the readers’ perspective by asking them to rate the text based on Likert scale. Furthermore, we tested two samples of text with and without capitalization on 27 available online sentiment tools. Testing was done in order to check how current sentiment tools treat letter capitalization in their sentiment score. Results show that of letter capitalization is able to enforce the different level of expression. If the nature of the review is positive, the capitalization makes it more positive. Similar for the negative reviews, the capitalization tends to increase negativity

Customer intent prediction using sentiment analysis techniques

Analysing the voice of the customer (VoC) through the customer intent has many applications ranging from personalised marketing to behaviour study. Individuals express their feelings in a language that is frequently accompanied by ambiguity and figure of speech, making it difficult even for humans to understand. Customer feedback is crucial as part of the customer experience (CX) management in customer retention and improves the sales strategy. Modern research has been using machine learning and word embedding technique for sentiment analysis, and it is focused on the predictive model without further context. In this study, the customer feedback comes in the form of Net Promoter Score (NPS)with a text box for written feedback. We analyse the data and demonstrate a hybrid representation that has resulted in the accuracy improvement of the sentiment classification task and predicting customer intent. The datasets were first trained using Word2Vec with the previous dataset and then fit into the Random Forest classifier, tested as the best configuration to prevent overfitting. The hybrid representation is compared against the baseline sentiment polarity tool through few experiments; the results have shown that the hybrid model has improved accuracy for the sentiment classification task. Lastly, we performed customer intent prediction by using the Power BI influencer module. The outcome of the result can be used as a reference for IT management in decision making

A criação de um modelo de Natural Language Processing para extração de habilidades técnicas na área de Ciência de Dados

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceO trabalho surgiu da necessidade do homem de suprir suas necessidades básicas. Na antiguidade, época de Gregos e Romanos, era ensinado a prole como cuidar da terra e esse conhecimento era passado por gerações. Com a chegada da primeira revolução industrial e posteriormente com a popularização dos computadores, o trabalho se tornou o que conhecemos hoje. A forma de contratação mudou ao longo dos anos e com a chegada da tecnologia mais do que nunca, o mercado de trabalho está em constante mudança e como consequência disto as habilidades necessárias para estes trabalhos também seguem esta mudança. Grande parte das vagas de trabalho hoje estão anunciadas em sites de buscas de emprego como Indeed, Glassdoor e Linkedin. Um grande desafio atual, é prever as mudanças e tendências do mercado de trabalho em termos de habilidades, e a analise textual pode gerar uma vantagem competitiva neste sentido. A proposta deste trabalho é analisar através de técnicas de Natural Language Processing (NLP) diferentes oportunidades de emprego da área de Data Science a fim de obter um modelo que possa ser utilizado para extrair as habilidades requisitadas para esta área. Para alcançar este objetivo primeiro é feita uma revisão dos conceitos de Aprendizado de Máquina, Natural Language Processing, Transfer Learning e das técnicas de preparação de dados, e em seguida será apresentada a metodologia utilizada. Depois, são destacadas as técnicas que funcionam melhor para extração de habilidades, a escolha e criação do modelo, e por fim a apresentação de resultados.The work originated from man's need to meet his basic needs. In ancient times, the times of the Greeks and Romans, offspring were taught how to take care of the land and this knowledge was passed on for generations. With the arrival of the first industrial revolution and later with the popularization of computers, work became what we know today. The way of hiring has changed over the years and with the arrival of technology more than ever, the job market is constantly changing, and consequently, the skills needed for these jobs also follow this change. A large part of job vacancies today is advertised on job search sites such as Indeed, Neuvoo, and Linkedin. A big challenge today is to predict the changes and trends in the job market in terms of skills and textual analysis can generate a competitive advantage in this sense. The purpose of this work is to analyze through Natural Language Processing (NLP) techniques different job opportunities in the Data Science area to obtain a model that can be used to extract the required skills for this area. To achieve this goal first a review of the concepts of Machine Learning, Natural Language Processing, Transfer Learning, and data preprocessing, then the methodology used is presented. Next, the techniques that work best for skill extraction is highlighted, the choice and creation of the model, and finally the presentation of results