Search results for "Sentiment analysis"
showing 6 items of 46 documents
La politica su Twitter: analisi di rete e sentiment analysis d'un caso studio
2021
Le interazioni sociali e le opinioni riguardanti la politica, espresse su Twitter da leader, candidati, normali cittadini o influencer, occupano una grossa parte del dibattito politico contemporaneo. Esse possono essere studiate facendo ricorso a tecniche computazionali, che comportano l’estrazione e l’analisi d’una enorme quantità di dati. In questo contributo, mostro le potenzialità di due di queste tecniche: la social network analysis e la sentiment analysis, indagando – come caso studio – il tema della scuola, oggetto di dibattito politico e trending topic su Twitter, nel settembre del 2020. A tale scopo, metto a confronto due reti di discussione diverse, una tematica e l’altra personal…
Prediction of User-Brand Associations Based on Sentiment Analysis
2023
Finding the right users to be chosen as targets for advertising campaigns is not a trivial task, and it may allow important commercial advantages. A novel approach is presented here for the recommendation of new possible consumers to brands interested in distributing advertising campaigns, ranked according to the “compatibility” between users and brands. A database containing both descriptions associated with different brands, and textual information about users' opinions on different topics, is required in input. Then, sentiment analysis techniques are applied to measure to what extent the users match with the brands, based on the texts associated with their opinions. The approach has been…
Implementing sentiment analysis to an open-ended questionnaire: Case study of digitalization in elderly care during COVID-19
2022
[EN] The rise of digital technology has enabled us to utilize even more integrated systems for social and health care, but these systems are often complex and time-consuming to learn for the end users without relevant training or experience. We aim to perform Named Entity Recognition based sentiment analysis using the answers of eldercare workers that have taken a survey about the effects of digitalization on their work. The collection of the panel survey data was carried out in two waves: in 2019 and 2021. For the sentiment analysis we compare these two waves to determine the effects of COVID-19 on the work of eldercare workers. The research questions we ask are the following: “Has technol…
Dealing with a small amount of data : developing Finnish sentiment analysis
2022
Sentiment analysis has been more and more prominently visible among all natural language processing tasks. Sentiment analysis entails information extraction of opinions, emotions, and sentiments. In this paper, we aim to develop and test language models for low-resource language Finnish. We use the term “low-resource” to describe a language lacking in available resources for language modeling, especially annotated data. We investigate four models: the state-of-the-art FinBERT [1], and competitive alternative BERT models Finnish ConvBERT [2], Finnish Electra [3], and Finnish RoBERTa [4]. Having a comparative framework of multiple BERT variations is connected to our use of additional methods …
How news affect the trading behavior of different categories of investors in a financial market
2015
We investigate the trading behavior of a large set of single investors trading the highly liquid Nokia stock over the period 2003-2008 with the aim of determining the relative role of endogenous and exogenous factors that may affect their behavior. As endogenous factors we consider returns and volatility, whereas the exogenous factors we use are the total daily number of news and a semantic variable based on a sentiment analysis of news. Linear regression and partial correlation analysis of data show that different categories of investors are differently correlated to these factors. Governmental and non profit organizations are weakly sensitive to news and returns or volatility, and, typica…
Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations
2023
AbstractThis study explores the consistency between star ratings and sentiments expressed in online reviews and how they relate to the different components of the customer experience. We combine deep learning applied to natural language processing, machine learning and artificial neural networks to identify how the positive and negative components of 20,954 online reviews posted on TripAdvisor about tourism attractions in Venice impact on their overall polarity and star ratings. Our findings showed that sentiment valence is aligned with star ratings. A cancel-out effect operates between the positive and negative sentiments linked to the service experience dimensions in mixed-neutral reviews.