A model would be able to recognize the sentiment more easily from adjectives to discern if a movie review is good or bad, or what the movie would need to improve upon Only keeping the necessary words can allow you to more easily tag parts of speech in your data, for example, if you tagged adjectives only, and used that text in your data for your model - like ‘beautiful, ‘amazing’, ‘loud’ would be left to use for predicting your target variable of a movie review.Similar to above, you can work to isolate just the lemma of the word.Removing unnecessary words so that you can perform stemming - where you can isolate the root of the words that are left after removal.Now that we have shown one way of cleaning text data, let’s discuss the possible applications where this process would be useful to data scientists: In rows 1, 3, and 8, are where the stopwords have been removed as you can see by the before and after, side by side. From there, we assign the stop words that we will remove from the text of the ‘title’ column, which will show their respective effect in the ‘clean_title’ column. In the code above, we are importing necessary libraries, and then reading our data into a pandas dataframe. With that being said, let us look at how we can remove some stop words from movie titles: import pandas as pd import rpus nltk.download(‘stopwords’) from rpus import stopwords df = pd.read_csv('.path/tmdb_5000_movies.csv') stop_words = stopwords.words('english') df = df.apply(lambda x: ' '.join()) If you removed stop words and a few more unnecessary words, you would get just time, July, holiday, party leftover. An example would be ‘there was the time where they went to the store in July for the holiday party’. ![]() ![]() The reason you would want to remove these words is if you want to keep the main subject of the words, phrase, sentence, etc. Some common examples of stop words are ‘the’, ‘of’, etc. That is why you should think about the list of words that you want to remove first. However, it is important to note that when you do use the common stopwords library, you may be removing words that you actually want to keep in. Īnother way to interpret ‘stop words’ is by removing unnecessary text.
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