NlP and sentiment analysis

Most of the data in the world is in fact unstructured

• True

• False

The subfield of data science/machine learning related to text is called

• Image processing

• Regression

• Classification

• Natural language processing

In NLP you are not limited simply to classification and regression

• True

• No

In Sentiment analysis

• List the topics that a document deals with

• Assess the emotional content of a document

• Compress a document as much as possible without losing meaning, producing another document

• Given a question in natural language, provide an appropriate answer in natural language

Of we want to separate each document into a collection of tokens we need to use:

• from nltk.tokenize import word_tokenize

• from nltk.stem import PorterStemmer

• from nltk.stem import WordNetLemmatizer

• w o r d

The effectiveness of an SVM depends upon:

• Selection of Kernel

• Kernel Parameters

• Soft Margin Parameter C

• All of the above

The SVM’s are less effective when:

• The data is linearly separable

• The data is clean and ready to use

• The data is noisy and contains overlapping points

• 

When the C parameter is set to infinite, which of the following holds true?

• The optimal hyperplane if exists, will be the one that completely separates the data

• The soft-margin classifier will separate the data

• None of the above

• 

What is a Bag-of-Words?

• it will result a vocabulary of known (words) and measure of the presence of known (words).

• the result will be percentage of the words occurrence

• its about baging

• It can be used for manipulating numbers