Frequent Words Remover | |||||
Description | Frequent words remover eliminates the frequent word/words before further processing. These words are called as Stop words. | ||||
Why to use | Textual Analysis – Pre Processing | ||||
When to use | When you want to remove stop words from the data. | When not to use | On numerical data. | ||
Prerequisites | It should be textual data. | ||||
Input | Nick likes to play football, however he is not too fond of tennis. | Output | 'Nick', 'likes', 'play', 'football', ',', 'however', 'fond', 'tennis', '.' | ||
Related algorithms |
| Alternative algorithm | - | ||
Statistical Methods used | - | Limitations | It cannot be used on Numerical data. | ||
One of the major tasks of data pre-processing is to filter out useless data. It is also called as text mining. In NLP, useless words are called stop words. Frequent words remover eliminates the frequent word/words before further processing. The output is a text devoid of stop words. This helps you to extract your data as required.
Stop words |
'ourselves', 'hers', 'between', 'yourself', 'but', 'again', 'there', 'about', 'once', 'during', 'out', 'very', 'having', 'with', 'they', 'own', 'an', 'be', 'some', 'for', 'do', 'its', 'yours', 'such', 'into', 'of', 'most', 'itself', 'other', 'off', 'is', 's', 'am', 'or', 'who', 'as', 'from', 'him', 'each', 'the', 'themselves', 'until', 'below', 'are', 'we', 'these', 'your', 'his', 'through', 'don', 'nor', 'me', 'were', 'her', 'more', 'himself', 'this', 'down', 'should', 'our', 'their', 'while', 'above', 'both', 'up', 'to', 'ours', 'had', 'she', 'all', 'no', 'when', 'at', 'any', 'before', 'them', 'same', 'and', 'been', 'have', 'in', 'will', 'on', 'does', 'yourselves', 'then', 'that', 'because', 'what', 'over', 'why', 'so', 'can', 'did', 'not', 'now', 'under', 'he', 'you', 'herself', 'has', 'just', 'where', 'too', 'only', 'myself', 'which', 'those', 'i', 'after', 'few', 'whom', 't', 'being', 'if', 'theirs', 'my', 'against', 'a', 'by', 'doing', 'it', 'how', 'further', 'was', 'here', 'than' |