Module 3 bcis 466

Sentiment analysis projects require a lexicon for use. if a project in English is undertaken, you must generally make sure to......
-Create an English lexicon for the project
-use any general english lexicon
-use an english lexicon appropriate to the proj

use an English lexicon appropriate to the project at your discretion

Natural language processing (NLP) is associated with which of the following areas?
-text mining
-computational linguistics
-artificial intelligence
-all

all of the above

What do voice of the market (VOM) applications of sentiment analysis do?
-They examine the stock market for trends
-They examine employee sentiment
-they examine the market of ideas in politics

They examine customer sentiment at the aggregate level.

In sentiment analysis, which of the following is an implicit opinion?
-The customer service i got for my tv was laughable
-our new mayor is great for the city
-the hotel we stayed in was terrible
-the cruise we went on last summer was a disaster

The customer service i got for my tv was laughable

All of the following are challenges associated with natural language processing except
-recognizing typographical or grammatical errors in texts
-distinguishing between words that have more than one meaning
-dividing up a text into individual words in Eng

Dividing up a text into individual words in English

In text analysis, what is a lexicon?
A) a catalog of words, their synonyms, and their meanings
B) a catalog of customers, their words, and phrase
C) a catalog of letters, words, phrases and sentences
D) a catalog of customers, products, words, and phrase

a catalog of words, their synonyms, and their meanings

Current use of sentiment analysis in voice of the customer applications allows companies to change their products or services in real time in response to customer sentiment. T/F

TRUE

in sentiment analysis, sentiment suggests a transient, temporary opinion reflective of one's feelings T/F

FALSE

In text mining, if an association between two concepts has 7% support, it means that 7% of the documents had both concepts represented in the same document. T/F

TRUE

Regional accents present challenges for natural language processing. T/F

TRUE

In text mining, tokenizing is the process of
-categorizing a block of text in a sentence
-transforming the term-by-document matrix to a manageable size
-creating new branches or stems of recorded paragraphs
-reducing multiple words to their base or root

categorizing a block of text in a sentence

In text mining, if an association between two concepts has 7% support, it means that 7% of the documents had both concepts represented in the same document. T/F

TRUE

In sentiment analysis, it is hard to classify some subjects such as news as good or bad, but easier to classify others, e.g., movie reviews, in the same way. T/F

TRUE

Current use of sentiment analysis in voice of the customer applications allows companies to change their products or services in real time in response to customer sentiment. T/F

TRUE

text mining

A semi-automated process of extracting knowledge from unstructured data sources

Differences between data mining vs text mining

text mining
-uses unstructured data (Word, pdf, text excerpts)
-Structure must be imposed on the data

corpus

large and structured set of texts

terms

single/multi word phrases extracted from the corpus of a specific domain using nlp methods

concepts

Features generated from a collection of documents. compared to terms, concepts are the result of higher level abstraction

stemming

process of reducing inflected words to their stem form.

stop words

Words that are filtered out prior to or after processing of natural language data

part-of-speech tagging

The process of marking up the words in a text as corresponding to a particular part of speech based on a word's definition and context of its use

Text mining application areas

#NAME?

natural language processing

-the studies of "understanding" the natural human language

Text mining process

1. Establish the Corpus
2. Create the Term-Document Matrix
3. Extract Knowledge

sentiment analysis process

Step 1 - Sentiment Detection
Step 2 - N-P Polarity Classification
Step 3 - Target Identification
Step 4 - Collection and Aggregation