Best Solution for Analyzing Twitter User Stream Data | SEO Marketing Firm

Answer: B.

Option A is incorrect.

The Orthogonal Sparse Bigram natural language processing algorithm creates groups of words and outputs the pairs of words that include the first word.

You are trying to determine how important a word is in a document by finding relevant repeated common words.

Option B is correct.

Term Frequency-Inverse Document Frequency determines how important a word is in a document by giving weights to words that are common and less common in the document.

You can use this information to select the most important repeated phrases in the user's tweets in your client marketing material.

Option C is incorrect.

The Bag-of-Words natural language processing algorithm creates tokens of the input document text and outputs a statistical depiction of the text.

The statistical depiction, such as a histogram, shows the count of each word in the document.

You are looking for relevant common repeated phrases, not individual words.

Option D is incorrect.

The N-Gram natural language processing algorithm is used to find multi-word phrases in the text of a document.

However, it does not weigh common words or phrases.

You need the weighting aspect of the tf-idf algorithm to find the relevant, important repeated phrases used in the tweets.

Reference:

Please see the article titled Introduction to Natural Language Processing for Text.

The best text feature engineering technique for this task would be the Term Frequency-Inverse Document Frequency (tf-idf).

The goal of this task is to find important or relevant repeated common words and phrases from Twitter user stream data, which can be achieved using feature engineering techniques.

Orthogonal Sparse Bigram (OSB) is a feature selection technique that aims to reduce the dimensionality of the feature space by selecting the most relevant features that have the highest correlation with the target variable. However, it may not be the best solution for this task because it may not capture the context of the text and may not be able to identify important phrases.

Bag-of-Words is a technique that represents the text as a bag of individual words, ignoring the order of the words and their context. While it may be useful in some text analysis tasks, it may not be suitable for this particular task because it would not capture the relationship between words or phrases.

N-Gram is a technique that captures the relationship between words by considering sequences of n words. However, it may not be the best solution for this task because it may not be able to identify important phrases or words that are not necessarily adjacent to each other.

On the other hand, tf-idf is a technique that represents the importance of each word in a document or a corpus based on its frequency and inverse document frequency. It considers both the frequency of a word in a document and the frequency of the word across the entire corpus, thus highlighting important words and phrases that are unique to a particular document or a group of documents. This technique is commonly used in text mining, information retrieval, and machine learning applications.

Therefore, the best solution for this task would be to use tf-idf to identify important and relevant words and phrases from the Twitter user stream data, and correlate this data to client products to include these topics in the client product marketing material.