General Sentiment Decomposition: opinion mining based on raw Natural Language text

The importance of person-to-person communication about a certain topic (Word of Mouth) is growing day by day, especially for decision-makers. These phenomena can be directly observed in online social networks. For example, the rise of influencers and social media managers. If more people talk about a specific product, then more people are encouraged to buy it and vice versa. Forby, those people usually leave a review for it. Such a review will directly impact the product, and this effect is amplified proportionally to how much the reviewer is considered to be trustworthy by the potential new customer. Furthermore, considering the negative reporting bias, it is easy to understand how customer satisfaction is of absolute interest for a company (as well as citizens' trust is for a politician). Textual data have then proved extremely useful, but they are complex, as the language is. For that, many approaches focus more on producing well-performing classifiers and ignore the highly complex interpretability of their models. Instead, we propose a framework able to produce a good sentiment classifier with a particular focus on the model interpretability. After analyzing the impact of Word of Mouth on earnings and the related psychological aspects, we propose an algorithm to extract the sentiment from a Natural Language text corpus. The combined approach of Neural Networks, characterized by high predictive power but at the cost of complex interpretation (usually considered as black-boxes), with more straightforward and informative models, allows not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. In fact, the General Sentiment Decomposition (GSD) framework that we propose is based on a combination of Threshold-based Naive Bayes (an improved version of the original algorithm), SentiWordNet (an enriched Lexical Database for Sentiment Analysis tasks), and the Words Embeddings features (a high dimensional representation of words) that directly comes from the usage of Neural Networks. Moreover, using the GSD framework, we assess an objective sentiment scoring that improves the results' interpretation in many fields. For example, it is possible to identify specific critical sectors that require intervention to improve the offered services, find the company's strengths (useful for advertising campaigns), and, if time information is present, analyze trends on macro/micro topics. Besides, we have to consider that NL text data can be associated (or not) with a sentiment label, for example: 'positive' or 'negative'. To support further decision-making, we apply the proposed method to labeled (Booking.com, TripAdvisor.com) and unlabelled (Twitter.com) data, analyzing the sentiment of people who discuss a particular issue. In this way, we identify the aspects perceived as critical by the people concerning the "feedback" they publish on the web and quantify how happy (or not) they are about a specific problem. In particular, for Booking.com and TripAdvisor.com, we focus on customer satisfaction, whilst for Twitter.com, the main topic is climate change

Age-Related Alternative Splicing: Driver or Passenger in the Aging Process?

Alternative splicing changes are closely linked to aging, though it remains unclear if they are drivers or effects. As organisms age, splicing patterns change, varying gene isoform levels and functions. These changes may contribute to aging alterations rather than just reflect declining RNA quality control. Three main splicing types-intron retention, cassette exons, and cryptic exons-play key roles in age-related complexity. These events modify protein domains and increase nonsense-mediated decay, shifting protein isoform levels and functions. This may potentially drive aging or serve as a biomarker. Fluctuations in splicing factor expression also occur with aging. Somatic mutations in splicing genes can also promote aging and age-related disease. The interplay between splicing and aging has major implications for aging biology, though differentiating correlation and causation remains challenging. Declaring a splicing factor or event as a driver requires comprehensive evaluation of the associated molecular and physiological changes. A greater understanding of how RNA splicing machinery and downstream targets are impacted by aging is essential to conclusively establish the role of splicing in driving aging, representing a promising area with key implications for understanding aging, developing novel therapeutical options, and ultimately leading to an increase in the healthy human lifespan

GOSPF: An energy efficient implementation of the OSPF routing protocol

Energy saving iscurrently one of the most challenging issues for the Internet research community. In- deed, the exponential growth of applications and services induces a remarkable increase in power consumption and hence calls for novel solutions which are capable to preserve energy of the infra- structures, at the same time maintaining the required Quality of Service guarantees. In this paper we introduce a new mechanism for saving energy through intelligent switch off of network links. The mechanism has been implemented as an extension to the Open Shortest Path First routing protocol.We first show through simulations that our solutionis capable to dramatically reduce energy consumption when compared to the standard OSPF implementation. We then illustrate a real-world implementation of the proposed protocol within the Quagga routing software suite

A cooperation-based approach to energy optimization in wireless ad hoc networks

A well known and still open issue for wireless ad hoc networks is the unfair energy consumption among the nodes. The specific position of certain nodes composing an ad hoc network makes them more involved in network operations than others, causing a faster drain of their energy. To better distribute the energy level and increase the lifetime of the whole network, we propose to periodically force the cooperation of less cooperative nodes while overwhelmed ones deliberately stop their service. A dedicated ad hoc network routing protocol is introduced to discover alternative paths without degradation in the overall network performance

Chapter Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction

The importance of the Word of Mouth is growing day by day in many topics. This phenomenon is evident in everyday life, e.g., the rise of influencers and social media managers. If more people positively debate specific products, then even more people are encouraged to buy them and vice versa. This effect is directly affected by the relationship between the potential customer and the reviewer. Moreover, considering the negative reporting bias is evident in how the Word of Mouth analysis is of absolute interest in many fields. We propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, with high predictive power but more challenging interpretation, with more simple but informative models, allows us to quantify a sentiment with a numeric value and to predict if a sentence has a positive (negative) sentiment. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify crucial specific sectors that require intervention, improving the company's services whilst finding the strengths of the company himself (useful for advertising campaigns). Moreover, considering that the time information is usually available in textual data with a web origin, to analyze trends on macro/micro topics. After showing how to properly reduce the dimensionality of the textual data with a data-cleaning phase, we show how to combine: WordEmbedding, K-Means clustering, SentiWordNet, and the Threshold-based Naïve Bayes classifier. We apply this method to Booking.com and TripAdvisor.com data, analyzing the sentiment of people who discuss a particular issue, providing an example of customer satisfaction

Evolution of Media Coverage on Climate Change and Environmental Awareness: An Analysis of Tweets from UK and US Newspapers

Climate change represents one of the biggest challenges of our time. Newspapers might play an important role in raising awareness on this problem and its consequences. We collected all tweets posted by six UK and US newspapers in the last decade to assess whether 1) the space given to this topic has grown, 2) any breakpoint can be identified in the time series of tweets on climate change, and 3) any main topic can be identified in these tweets. Overall, the number of tweets posted on climate change increased for all newspapers during the last decade. Although a sharp decrease in 2020 was observed due to the pandemic, for most newspapers climate change coverage started to rise again in 2021. While different breakpoints were observed, for most newspapers 2019 was identified as a key year, which is plausible based on the coverage received by activities organized by the Fridays for Future movement. Finally, using different topic modeling approaches, we observed that, while unsupervised models partly capture relevant topics for climate change, such as the ones related to politics, consequences for health or pollution, semi-supervised models might be of help to reach higher informativeness of words assigned to the topics

Semi-supervised topic representation through sentiment analysis and semantic networks

This paper proposes a novel approach to topic detection aimed at improving the semi-supervised clustering of customer reviews in the context of customers' services. The proposed methodology, named SeMi-supervised clustering for Assessment of Reviews using Topic and Sentiment (SMARTS) for Topic-Community Representation with Semantic Networks, combines semantic and sentiment analysis of words to derive topics related to positive and negative reviews of specific services. To achieve this, a semantic network of words is constructed based on word embedding semantic similarity to identify relationships between words used in the reviews. The resulting network is then used to derive the topics present in users' reviews, which are grouped by positive and negative sentiment based on words related to specific services. Clusters of words, obtained from the network's communities, are used to extract topics related to particular services and to improve the interpretation of users' assessments of those services. The proposed methodology is applied to tourism review data from Booking.com, and the results demonstrate the efficacy of the approach in enhancing the interpretability of the topics obtained by semi-supervised clustering. The methodology has the potential to provide valuable insights into the sentiment of customers toward tourism services, which could be utilized by service providers and decision-makers to enhance the quality of their services