Exploring Sentiment Analysis Techniques

What is the primary goal of sentiment analysis?

• To determine the emotional tone behind a body of text.

• To identify the author of a text.

• To summarize the main ideas in a document.

• To analyze the grammatical structure of text.

What is tokenization in the context of text preprocessing?

• Tokenization is the process of summarizing text into a single sentence.

• Tokenization refers to the encryption of text data for security purposes.

• Tokenization is the method of translating text into numerical values for analysis.

• Tokenization is the process of dividing text into individual tokens, such as words or phrases.

List a method of tokenization.

• Character Tokenization

• Sentence Tokenization

• Phrase Tokenization

• Word Tokenization

Why is stop words removal important in text analysis?

• Stop words removal is important because it reduces noise and enhances the focus on meaningful words in text analysis.

• Stop words are the most important words in a text.

• Removing stop words increases the size of the dataset.

• Stop words removal is unnecessary for text analysis.

Give an example of a common stop word in English.

• outside

• the

• below

• good

What is the difference between stemming and lemmatization?

• Stemming is a crude method that removes suffixes, while lemmatization considers context and returns valid base forms.

• Stemming analyzes the meaning of words, while lemmatization does not.

• Lemmatization is faster than stemming in all cases.

• Stemming always produces valid words, while lemmatization does not.

Which algorithm is commonly used for stemming?

• Porter Stemming Algorithm

• Support Vector Machine

• Naive Bayes Classifier

• K-means Clustering Algorithm

What is the purpose of feature extraction in text analysis?

• To summarize the text into a single sentence.

• To translate the text into multiple languages.

• To remove all punctuation from the text.

• The purpose of feature extraction in text analysis is to transform unstructured text into a structured format that can be used for analysis and modeling.

How does the bag-of-words model work?

• It uses word embeddings to represent text meaning.

• It analyzes the grammatical structure of sentences.

• It focuses on the order of words in a sentence.

• The bag-of-words model works by representing text as a collection of words, ignoring grammar and order, and counting word occurrences.

What is the impact of punctuation on tokenization?

• Tokenization is only influenced by whitespace.

• Punctuation is ignored during text analysis.

• Punctuation can change how text is split into tokens, affecting meaning and analysis.

• Punctuation has no effect on tokenization.