mrrobot-sentiment-analysis

Data

Subtitles: https://www.opensubtitles.org/en/ssearch/sublanguageid-eng/idmovie-197690

Episode Names: https://en.wikipedia.org/wiki/List_of_Mr._Robot_episodes

IMDB Scores:

Repo: https://github.com/ttsteiger/mrrobot-sentiment-analysis

Part 1:

• Data prep. & cleaning
• Applied to subtitles, remove all timestamps & empty lines, score each line? What about sentences going over multiple lines?
• Scoring of long texts -> Score sentences, average for chapters, great visualizations: https://towardsdatascience.com/basic-nlp-on-the-texts-of-harry-potter-sentiment-analysis-1b474b13651d

Part 2:

• Lexical analysis

Part 3:

• VADER Sentiment Analysis
• http://datameetsmedia.com/ranking-the-negativity-of-black-mirror-episodes-with-sentiment-analysis/
• http://datameetsmedia.com/vader-sentiment-analysis-explained/
• https://github.com/cjhutto/vaderSentiment
• https://towardsdatascience.com/basic-nlp-on-the-texts-of-harry-potter-sentiment-analysis-1b474b13651d

Part 4:

• ML for Sentiment Analysis
• https://levelup.gitconnected.com/sentiment-analysis-using-machine-learning-python-9122e03f8f7b
• http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/
• https://www.twilio.com/blog/2017/12/sentiment-analysis-scikit-learn.html
• https://towardsdatascience.com/sentiment-analysis-with-python-part-1-5ce197074184
• https://stackabuse.com/python-for-nlp-movie-sentiment-analysis-using-deep-learning-in-keras/

Notes:

• Try with MonkeyLearn API: https://app.monkeylearn.com/main/classifiers/cl_CsfDyd3m/tab/demo/ https://monkeylearn.com/pricing/

• Twitter SA: https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python/

• Next Idea: Customer Review Sentiment Analysis for Restaurants in Zürich -> Display in WebApp https://rstudio-pubs-static.s3.amazonaws.com/155272_c91116f9fd774349bef82cb154b62f5c.html

Lexical Approach -> VADER

• Machine Learning Approach -> Training Data??
• Negativity vs. IMDB Scores?
• Positiv vs. negative/split into individual sentiments
• IMDB score for episode vs. sentiment -> predict IMDB scores for S04 based on subtitles

http://millionsongdataset.com/