the visualization depicted in figure - shows us the topics from negative movie reviews and we can see the top relevant terms for topic highlighted in the output from the topics and the termswe can see terms like wastetimemoneycrapplotterribleactingand so on have contributed toward negative sentiment in various topics of coursethere are high chances of overlap between topics from positive and negative sentiment reviewsbut there will be distinguishabledistinct topics that further help us with interpretation and causal analysis ummary this case-study oriented introduces the imdb movie review dataset with the objective of predicting the sentiment of the reviews based on the textual content we covered concepts and techniques from natural language processing (nlp)text analyticsmachine learning and deep learning in this we covered multiple aspects from nlp including text pre-processingnormalizationfeature engineering as well as text classification unsupervised learning techniques using sentiment lexicons like afinnsentiwordnetand vader were covered in extensive detailto show how we can analyze sentiment in the absence of labeled training datawhich is very valid problem in today' organizations detailed workflow diagrams depicting text classification as supervised machine learning problem helped us in relating nlp with machine learning so that we can use machine learning techniques and methodologies to solve this problem of predicting sentiment when labeled data is available the focus on supervised methods was two-fold this included traditional machine learning approaches and models like logistic regression and support vector machines and newer deep learning models including deep neural networksrnnsand lstms detailed conceptsworkflowshands-on examples and comparative analyses with multiple supervised models and different feature engineering techniques have been covered for the purpose of predicting sentiment from movie reviews with maximum model performance the final section of this covered very important aspect of machine learning that is often neglected in our analyses we looked at ways to analyze and interpret the cause of positive or negative sentiment analyzing and visualizing model interpretations and topic models have been covered with several examplesto give you good insight into how you can re-use these frameworks on your own datasets the frameworks and methodologies used in this should be useful for you in tackling similar problems on your own text data in the future