Text Analytics & Sentiment Analysis Quiz

What is the definition of sentiment analysis?

• Analyzing people's facial expressions towards entities expressed in text

• Analyzing people's physical reactions towards entities expressed in text

• Analyzing people's opinions, sentiments, evaluations, and emotions towards entities expressed in text

• Analyzing people's behavior towards entities expressed in text

What are some examples of sentiment analysis jargons?

• Positive/negative, happy/sad, love/hate

• Semantic orientation, polarity

• Good/bad, like/dislike

• Opinion mining, emotional state

What does sentiment analysis determine?

• The state of any emotional communication that affects a reader or listener

• The attitude or inclination of a communicator through the contextual polarity of their speaking or writing

• The emotional state of the reader

• The factual information in a text

What are the different levels of analysis in sentiment analysis?

• Document level, word level, phrase level

• Word level, paragraph level, entity and aspect level

• Sentence level, paragraph level, entity and aspect level

• Document level, sentence level, entity and aspect level

What is the purpose of creating a classifier in sentiment analysis?

• To train the classifier with the training set

• To apply features to the classifier using the method apply_features()

• To decide what features are relevant

• To extract word features from the tweets

What is the purpose of Pointwise Mutual Information (PMI) in sentiment analysis?

• To measure the frequency of words in a text

• To calculate the probability of words occurring together

• To evaluate the sentiment lexicon/word list

• To find collocations and measure how much one word tells us about the other

What is SentiWordNet?

• A sentiment analysis tool for customer reviews

• A sentiment analysis tool for Twitter data

• A social media platform for sharing opinions

• A lexical resource for opinion mining based on Wordnet synsets

What is the purpose of Chat Analytics using Telegram data?

• To measure the frequency of words in a text

• To collect and analyze messages from Telegram groups

• To analyze the sentiment of customer reviews

• To evaluate the sentiment lexicon/word list

What is the main challenge in sentiment analysis?

• People express opinions in complex ways

• Intra-textual and sub-sentential reversals

• All of the above

• Rhetorical devices/modes such as sarcasm, irony, implication

What is the purpose of Steps in Sentiment Analysis using NLTK?

• To train the classifier with NLTK

• To evaluate the sentiment lexicon/word list

• To extract word features from the tweets

• To create a classifier