A cosine based validation measure for Document Clustering

Document Clustering is the peculiar application of cluster analysis methods on huge documentary databases. Document Clustering aims at organizing a large quantity of unlabelled documents into a smaller number of meaningful and coherent clusters, similar in content. One of the main unsolved problems in clustering literature is the lack of a reliable methodology to evaluate results, although a wide variety of validation measures has been proposed. If those measures are often unsatisfactory when dealing with numerical databases, they definitely underperform in Document Clustering. This paper proposes a new validation measure. After introducing the most common approaches to Document Clustering, our attention is focused on Spherical K-means, do to its strict connection with the Vector Space Model, typical of Information Retrieval. Since Spherical K-means adopts a cosine-based similarity measure, we propose a validation measure based on the same criterion. The new measure effectiveness is shown in the frame of a comparative study, by involving 13 different corpora (usually used in literature for comparing different proposals) and 15 validation measures

A polarity-based strategy for ranking social media reviews

TheOpinionMining methods are widely used to analyse and classify the choices, preferences and behaviours of consumers through the opinions gathered on the Web. On social media like TripAdvisor such opinions are usually expressed with a score and a short text. This paper proposes a strategy for ranking reviews using a scale based jointly on the rating and on the text of the reviews

Georeferencing sentiment scores to map and explore tourist points of interest

[EN] Tourists are increasingly involved in co-creating attractions’ symbolic images, sharing their experiences and opinions on websites like TripAdvisor and other similar rating and review platforms. In this paper, we propose a strategy for analyzing people’ opinions about tourist points of interest, using an Ambient Geographic Information approach to georeference the polarity scores of reviews. Visualizing these scores on a map can be used to obtain helpful information for implementing strategic actions and policies of institutional and business actors involved in the tourist industry, as well as to help users plan their future experiences. A case study concerning the reviews of the restaurants in Naples (Italy) shows the effectiveness of the proposal.Celardo, L.; Misuraca, M.; Spano, M. (2023). Georeferencing sentiment scores to map and explore tourist points of interest. Editorial Universitat Politècnica de València. 115-122. https://doi.org/10.4995/CARMA2023.2023.1642911512

A mixed-frequency approach for exchange rates predictions

Selecting an appropriate statistical model to forecast exchange rates is still today a relevant issue for policymakers and central bankers. The so-called Meese and Rogoff puzzle assesses that exchange rate fluctuations are unpredictable. In the literature, a lot of studies tried to solve the puzzle finding both alternative predictors (e.g., interest rates, price levels) and statistical models based on temporal aggregation. In this paper, we propose an approach based on mixed frequency models to overcome the lack of information caused by temporal aggregation. We show the effectiveness of our approach with an application to CAD/USD exchange rate predictions.

Georeferencing sentiment scores to map and explore tourist points of interest

Tourists are increasingly involved in co-creating attractions’ symbolic images, sharing their experiences and opinions on websites like TripAdvisor and other similar rating and review platforms. In this paper, we propose a strategy for analyzing people’ opinions about tourist points of interest, using an Ambient Geographic Information approach to georeference the polarity scores of reviews. Visualizing these scores on a map can be used to obtain helpful information for implementing strategic actions and policies of institutional and business actors involved in the tourist industry, as well as to help users plan their future experiences. A case study concerning the reviews of the restaurants in Naples (Italy) shows the effectiveness of the proposal