![]() Moreover, the predictive impact of search query data and the relation between Nike’s events and the subsequent Facebook activity was explored. The aim was to estimate the impact of each variable such as posts, comments and likes on a group of Nike’s Facebook page, examining each variable and each page individually, as well as a combination of all variables and all pages. After collecting about 3 million tweets, the authors used a linear regression model and performed sentiment analysis to conclude that there is a correlation between the fame of a movie prior to its release and the revenue it produces. The choice of film was based on the number of discussions and the difference of opinion, as well as by obtaining financial information about it. Twitter was employed to predict the commercial success of movies. This section reviews forecasting related literature. 4 and concludes with directions for future work in Sect. The remaining of this paper reviews background in Sect. The optimal number of N, the highest performing classification algorithm and the best feature combination can be determined by experimentation and result comparison. This requires a classifier to generate predictions. ![]() The aim is to predict the top N songs for the following week and evaluate the efficiency of the process. e) to assess the contribution of the features, extracted from the Billboard chart and collected posts, to forecast the chart for the week to come. d) to perform sentiment analysis on tweets, categorizing each post as positive, negative or neutral. c) to preprocess data into a homogenous, structured format removing redundant information. b) to collect Twitter posts concerning the top 10 songs utilizing the Twitter Search API. For that purpose we developed a method which extracts these parameters from the official site and saves them in a. ![]() This work has the following objectives: a) to acquire data from the Billboard chart, including rank, artist and song title of the top 100 songs at the current time. After gathering chart data, including titles, artist names and rankings, as well as tweets related to the top 10 songs for each week, results showed a moderate correlation between the number of mentions of a song and its future performance, but no relation between the number of mentions of an artist and their imminent success. In this paper, we chose Twitter to generate predictions for the Billboard chart. Social media can be exploited in a variety of cases, such as forecasting the commercial success of movies, election result predictions etc. The vast amount of data available through these services can be utilized in many domains including finance, marketing, and politics. ![]() Social media have penetrated everyday life to the point that they constitute an integral part of our daily routines. Given the accuracy and F-score achieved compared to previous research, our findings are deemed satisfactory, especially in predicting the top 20. We also focused on forecasting chart ranges, namely the top 5, 10 and 20. The problem was approached via regression analysis, which estimated the difference between the actual and predicted positions and moderated results. Our goal was to investigate the relation between the number of mentions of a song and its artist, as well as the semantic orientation of the relevant posts and its performance on the subsequent chart. This work aims at using Twitter data, related to songs and artists that appeared on the top 10 of the Billboard Hot 100 charts, performing sentiment analysis on the collected tweets, to predict the charts in the future. Past successes include predicting election results, stock prices and forecasting events or behaviors. With the advent of social media, concepts such as forecasting and now casting became part of the public debate. ![]()
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