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Purpose: Acting as a well-known artist’s/band’s manager, using social media analytics to help improve their popularity.
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Technologies used: Rstudio, Gephi, Tableau
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Data Collection: Utilized vosonSML, rtweet, Rspotify, and tuber packages in Rstudio to collect all recent data about the artist through Twitter, Spotify, and YouTube API.
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Data Manipulation and Analysis: Leveraged tidyverse packages in Rstudio to manipulate the large amount of data collected from social media and extract insights about the artist's work, fan base, and other relevant information.
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Network Visualization: Created and visualized actor and semantic networks using Rstudio and Gephi to identify important actors and trending topics related to the artist.
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Data Visualization: Utilized ggplot package, adding layers, and customizations to visualize different audio features in every song, along with data of top trending videos on YouTube.
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Twitter User Clustering: Employed k-means to find clusters of Twitter users based on their friends and followers counts.
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Topic Modeling: Utilized Latent Dirichlet Allocation (LDA) for topic modeling of tweet text.
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Sentiment Analysis: Performed sentiment analysis on Twitter data using syuzhet package in R to classify tweets according to the sentiment and emotion.
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Buidling Decision Tree for ML Model: Used data from the Spotify API to train a decision tree model and evaluate its performance for predicting whether a song is by a certain artist, given the audio features of the song.