Fluent Essays

The project requires building of a NLP classifiers and sentiment analysis based on the below guidelines. I have attached the Rmarkdown file and required input files for the analysis. please use Python to do the below steps preferably. The cleaned_subtitles will be the dataset used for the analysis and movie reviews file is what you should use for sentiment analysis Classifcation: - Pick several nouns and/or verbs from your previous assignment. Create a column in the dataframe that indicates if that line from the movie/TV show includes that word or does not include that word. You can use 0 and 1 or any labels that make sense to you. Remember, we covered regular expression detection and deletion in the raw text assignments! - Once you have created this column, use string replacement to delete that word from your subtitles. We will take the word out to see if we can predict when it is used - if you leave it in, its a perfect predictor! - Use *two* feature extraction methods and *two* machine learning algorithms to determine if you can predict when your noun or verb will be used. You should include four different classification reports below.  Sentiment: - Use *one* of the unsupervised lexicon techniques to create sentiment scores for your movie/TV show. - What is the overall sentiment of your movie/TV show? How would you interpret the scores provided? - Using the movie reviews mini dataset provided online, create a sentiment tagging model (one feature extraction method + one algorithm). - With this new model, create sentiment scores for your movie/TV show. - What is the overall sentiment using the new model of sentiment tagging? How would you interpret the scores provided?subtitle 1 i think that 2 actually everything ive experiencedover these last few months... 3 is like being in a dream... 4 ...or in a movie. 5 but a very bad movie, 6 because that movie would bevery unrealistic. 7 its fashionable these days toblame everything on global warming. 8 particularly onman-made global warming. 9 but im afraid the evidencedoesnt point in that direction. 10 a little bit of warmingwould not be a bad thing for myself, 11 being a canadian 12 and the people in russiawouldnt mind a little... 13 a couple of degrees warmer either. 14 all of this with theglobal warming, a lot of its a hoax. 15 its a hoax, i mean, its a money-makingindustry, okay. its a hoax. 16 the wholeclimate crisis as they call it, 17 is not only fake news, its fake science. 18 there is no climate crisis.there is nothing to be afraid of. 19 how much warming will you get if you, 20 let us say, double the co2 in the air? 21 the answer is,you dont get very much. 22 the problem is not global warming. 23 the problem is that wekeep hearing one-sided arguments. 24 carbon dioxide isactually a benefit to the environment, 25 to agriculture and forestry,and to the climate of the earth. 26 there are some peoplewho are so arrogant to think 27 that they are so powerful,they can change climate. 28 man cant change climate. 29 adults always say one thing 30 and then do somethingcompletely different. 31 they say that we only have one planet, 32 and we should take care of it. 33 and yet no one gives a damnabout the climate crisis. 34 my name is greta thunberg,and i am 15 years old. 35 i am on a school strike for the climatein front of the parliament, 36 until the election day. 37 many have heard about climate change 38 and know vaguely what it means 39 with rising sea levels,increased average temperatures, 40 and so on. 41 but no one seems tofully understand the consequences. 42 that we are right nowliving as if we had several earths. 43 sometimes it feels like wewho have aspergers or autism 44 are the only oneswho see through the noise. 45 i am protesting about the climate crisisbecause its such an important issue. 46 what we are doing now,future generations cant change. 47 and no one is doing anything,nothing is happening. 48 so i must do what i can. 49 what did you thinkwhen you first heard her talking 50 about the idea to start protesting? 51 i told her if she woulddo that, i would not support it. 52 so she made a very long list of factsthat shes been handing out to people. 53 and, uh, she knowsthe climate issue, i would say 54 better than 97\% of all the politicians,probably in the world. 55 she has almost a photographic memory 56 when it comesto things shes interested in. 57 so she can read books and almostremember everything thats in there. 58 did you expect something like that? 59 i didnt expect anything. 60 i just wanted her to be ha--- title: Classification + Sentiment Assignment author: STUDENT NAME date: `r Sys.Date()` output: html_document --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` Be sure to list the group members at the top! This assignment is the first component for your class project. Be sure all group members contribute, *and* each group member must submit the assignment individually. You can use either *R* or Python or both for this assignment. You should use the code and packages we used in class assignments, but these can be a mix and match of each computing language. You will add the appropriate type of code chunk for each section depending on the language you pick for that section. ## Libraries / R Setup - In this section, include the libraries you need for the *R* questions. ```{r} ##r chunk ``` - In this section, include import functions to load the packages you will use for Python. ```{python} ##python chunk ``` ## Import Subtitles - Import your subtitle data for the movie or TV show that you selected in the course proposal - use the clean version from the previous assignment. ## Classification - Pick several nouns and/or verbs from your previous assignment. Create a column in the dataframe that indicates if that line from the movie/TV show includes that word or does not include that word. You can use 0 and 1 or any labels that make sense to you. Remember, we covered regular expression detection and deletion in the raw text assignments! - Once you have created this column, use string replacement to delete that word from your subtitles. We will take the word out to see if we can predict when it is used - if you leave it in, its a perfect predictor! - Use *two* feature extraction methods and *two* machine learning algorithms to determine if you can predict when your noun or verb will be used. You should include four different classification reports below. ### Interpretation - Can you predict when the noun or verb will occur? Remember 50/50 is chance! - What combination of feature extraction and algorithm created the best prediction for your word choice? - Print out ten examples of misclassified spoken sections. Do you see any pattern that may help inform a better process to predict word choice? ## Sentiment - Use *one* of the unsupervised lexicon techniques to create sentiment scores for your movie/TV show. - What is the overall sentiment of your movie/TV show? How would you interpret the scores provided? - Using the movie reviews mini dataset provided online, create a sentiment tagging model (one feature extraction method + one algorithm). - With this new model, create sentiment scores for your movie/TV show. - What is the overall sentiment using the new model of sentiment tagging? How would you interpret the scores provided? ## Interpretation - Using classification techniques, can we reasonably predict word context? - Does the sentimereview sentiment This film is a complete re-imagining of Romeo and Juliet in Tel Aviv and Nablus. The lovers are one from Tel Aviv et the other from Nablus. There is a border between them, and a constant state of war with the Israeli army ever present everywhere and the Palestinian militants everywhere else with their bombs. The situation is bleak enough. We can imagine love in that enormous loveless trap. But the film goes several light years further by imagining the two lovers are gay, Noam from Tel Aviv and Ashraf from Nablus. To be gay is accepted in Tel Aviv. It is off limits in Nablus. The conflict between the two peoples, the two communities is thus doubled with a conflict between two cultures, two ethics. But this could even be livable if the war did not bring some extra dimension. Ashrafs sister is going to get married to a militant activist in Nablus. Ashraf finally tells his sister about his being gay. She cannot accept it but accepts to speak about it later. From the wedding itself the newly married husband sends a commando into Tel Aviv to set up a bomb attack. It takes place in a café in Tel Aviv and one friend of Noams is severely wounded. Bad enough. The Isareli army sends a commando to Nablus to arrest the person responsible for this attack, but it turns sour and the newly married wife is shot dead in the street. The funeral follows the wedding. The husband and widower volunteers for a suicide bomb attack. Ashraf volunteers to take his place. The exiled lover comes back to Tel Aviv to die and kill a few people to avenge his sister. He arrives at a diner managed by some friends of Noams. But Noam sees him and gets out to speak to him. Ashraf has moved back to the middle of the street and he detonates his bomb when Noam reaches him in the street. The vengeance reunites the two lovers in death. We thus have the dual conflict but we do not have the Prince of Verona, a neutral character that can impose peace, or even worse the Prince seems to have chosen sides and to be on the side of Israel. The game is entirely false and death is sure on both sides. But the dimension of impossible love is all the stronger because it is redoubled by a play in the film, a play that shows love in Auschwitz, between two prisoners, one wearing a yellow star and the other a pink triangle. This is both strikingly strong and breathtakingly shocking: gay love in Auschwitz. What comes out of the film is that over there in Tel Aviv or Nablus love is impossible. The film is thus a denunciation of the conflict in Palestine that cannot but continue though it has no reason to even exist though it has thousands of reasons to go on. We should never have let Great Britain deal with the region a long time ago. Today we have to find a solution in which no one will be humiliated. This will only be able to succeed if everyone comes together in order to find a lasting solution. But so far everyone is trying to avoid that general confrontation and discussion preferrin