now that we have our normalization module readywe can start modeling and analyzing our corpus nlp and text analytics enthusiasts who might be interested in more in-depth details of text normalization can refer to the section "text normalization,page of text analytics with python (apressdipanjan sarkar unsupervised lexicon-based models we have talked about unsupervised learning methods in the pastwhich refer to specific modeling methods that can be applied directly on data features without the presence of labeled data one of the major challenges in any organization is getting labeled datasets due the lack of time as well as resources to do this tedious task unsupervised methods are very useful in this scenario and we will be looking at some of these methods in this section even though we have labeled datathis section should give you good idea of how lexicon based models work and you can apply the same in your own datasets when you do not have labeled data unsupervised sentiment analysis models use well curated knowledgebasesontologieslexiconsand databases that have detailed information pertaining to subjective wordsphrases including sentimentmoodpolarityobjectivitysubjectivityand so on lexicon model typically uses lexiconalso known as dictionary or vocabulary of words specifically aligned toward sentiment analysis usually these lexicons contain list of words associated with positive and negative sentimentpolarity (magnitude of negative or positive score)parts of speech (postagssubjectivity classifiers (strongweakneutral)moodmodalityand so on you can use these lexicons and compute sentiment of text document by matching the presence of specific words from the lexiconlook at other additional factors like presence of negation parameterssurrounding wordsoverall context and phrases and aggregate overall sentiment polarity scores to decide the final sentiment score there are several popular lexicon models used for sentiment analysis some of them are mentioned as follows bing liu' lexicon mpqa subjectivity lexicon pattern lexicon afinn lexicon sentiwordnet lexicon vader lexicon this is not an exhaustive list of lexicon modelsbut definitely lists among the most popular ones available today we will be covering the last three lexicon models in more detail with hands-on code and examples using our movie review dataset we will be using the last , reviews and predict their sentiment and see how well our model performs based on model evaluation metrics like accuracyprecisionrecalland -scorewhich we covered in detail in since we have labeled datait will be easy for us to see how well our actual sentiment values for these movie reviews match our lexiconmodel based predicted sentiment values you can refer to the python file titled unsupervised_sentiment_ analysis py for all the code used in this section or use the jupyter notebook titled sentiment analysis unsupervised lexical ipynb for more interactive experience before we start our analysislet' load the necessary dependencies and configuration settings using the following snippet in [ ]import pandas as pd import numpy as np import text_normalizer as tn import model_evaluation_utils as meu np set_printoptions(precision= linewidth=