Revealing People’s Sentiment in Natural Italian Language Sentences

Abstract

Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision.

Automated Social Media Text Clustering Based on Financial Ontologies

“…Sentiment analysis involves determining the sentiment or emotional tone expressed in social media content. This technique classifies text as positive, negative, or neutral, enabling businesses to understand customer sentiment toward their brand, products, or services [5]. Topic modeling techniques, such as Latent Dirichlet Allocation (LDA) [6,7] or Non-Negative Matrix Factorization (NMF) [8,9], are used to identify latent topics or themes in social media discussions.…”