Search results for "Topic modeling"
showing 7 items of 7 documents
Supervised vs Unsupervised Latent DirichletAllocation: topic detection in lyrics.
2020
Topic modeling is a type of statistical modeling for discovering the abstract ``topics'' that occur in a collection of documents. Latent Dirichlet Allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It builds a fixed number of topics starting from words in each document modeled according to a Dirichlet distribution. In this work we are going to apply LDA to a set of songs from four famous Italian songwriters and split them into topics. This work studies the use of themes in lyrics using statistical analysis to detect topics. Aim of the work is to underline the main limits of the standard unsupervised LDA and to propose a supervised…
Social Collaborative Viewpoint Regression with Explainable Recommendations
2017
A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To th…
Analysing Tourist Destination Image through Topic Modeling
2019
Topic modeling has become one of the most used methods to analyse textual data, proving able to “discover” hidden dimensions (topics) which characterise a corpus. This methodology can be used fruitfully to analyse complex phenomena like tourist destination image. With this aim in mind, this paper discusses the use of topic modeling over TV commercials which have been broadcast by four of the major cruise lines operating in Italy in recent years.
Automated Content Analysis of Destination Image: a Case Study
2020
Automated content analysis has become one of the most used approaches to extract “hidden” dimensions from text corpora over the last years. One of the data analysis techniques belonging to this approach is topic modeling, which can be fruitfully used to analyse complex phenomena like tourist destination image. With this aim in mind, this paper discusses the use of topic modeling to identify the main components of the image of cruise holidays spread through a specific type of visual text, i.e. the Television commercial. In order to achieve this goal, the paper presents the methodology and main results of a study carried out over a sample of TV commercials, which have recently been broadcast …
Exploring the challenges of remote work on Twitter users’ sentiments: From digital technology development to a post-pandemic era
2022
The boost in the use and development of technology, spurred by COVID-19 pandemic and its consequences, has sped up the adoption of new technologies and digital platforms in companies. Specifically, companies have been forced to change their organizational and work structures. In this context, the present study aims to identify the main opportunities and challenges for remote work through the use of digital technologies and platforms based on the analysis of user-generated content (UGC) in Twitter. Using computer-aided text analysis (CATA) and natural language processing (NLP), in this study, we conduct a sentiment analysis developed with Textblob, which works with machine learning. We then …
Examining Competing Entrepreneurial Concerns in a Social Question and Answer (SQA) Platform
2021
This study aims to determine the competing concerns of people interested in startup development and entrepreneurship by using topic modeling and sentiment analysis on a social question-and-answer (SQA) website. Understanding the underlying concerns of startup entrepreneurs is critical to society and economic growth. Therefore, greater scientific support for entrepreneurship remains necessary, including data mining from virtual social communities. In this study, an SQA platform was used to identify the sentiment of thirty concerns of people interested in startup entrepreneurship. Based on topic modeling and sentiment analysis of 18819 inquiries in various forums on an SQA, we identified addi…
Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis
2020
With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. This paper investigates the topic modeling subject and its common application areas, methods, and tools. Also, we examine and compare five frequen…