1 Introduction
I Spooky Authors Text Mining
- 1.1 Spooky Author Identification Dataset
2 Read the Data
3 Add Feature Number of Words
4 Peek into the Data
5 Length Comparison
6 Words Length Distribution
- 6.1 Words Length Distribution Plot 2
7 Tokensiation
- 7.1 Removing the Stop words
8 Top Twenty most Common Words
9 TF-IDF
10 Most Common Bigrams
11 Most Common Trigrams
12 Relationship among words
13 Sentiment Analysis
14 Sentiment Analysis using NRC Sentiment lexicon
15 Positive and Not So Postive Lines
16 Feature Sentiment Score
17 Feature NRC Sentiments
18 He or She Analysis
- 18.1 Gender associated verbs
19 Document Term Matrix
20 Modelling with XGBoost
- 20.1 Add features
- 20.2 Creating the XGBoost Model
21 Predictions using the XGB Model
22 Predictions using glmnet Model
23 Modelling using the text2vec package
II Yelp Data Reviews Text Mining
24 Introduction
25 Preparation
- 25.1 Load Libraries
- 25.2 Read the data
26 Business data
27 Reviews data
28 Detecting the language of the reviews
29 Most Popular Categories
30 Top Ten Cities with the most Business parties mentioned in Yelp
31 Map of the business parties in Las vegas
32 Business with most Five Star Reviews from Users
33 “Mon Ami Gabi”
34 Bacchanal Buffet
35 Top Ten Business in Toronto
36 Pai Northern Thai Kitchen
37 Chipotle business
38 Chipotle Business in Yonge Street Toronto
39 Topic Modelling
40 Phoenix City Analysis
III Simpsons Text Mining
41 Introduction
42 Ten Most Active Characters
43 Next Ten Most Active Characters
44 Top Twenty most Common Words
- 44.1 WordCloud of the Common Words
45 TF-IDF
46 Relationship among words
- 46.1 Dont word network graph
47 Sentiment Analysis
48 Topic Modelling
49 Location of Characters
50 Homer Simpson
51 Best and the Worst Episodes
52 Modelling with XGBoost
IV Movies Sentiment Analysis
53 Introduction
54 Read the data
55 Tokenisation
- 55.1 Removing the Stop words
56 Top Ten most Common Words
57 TF-IDF
- 57.1 The Math
- 57.2 Twenty Most Important Words
58 Most Common Bigrams
59 Most Common Trigrams
60 Relationship among words
61 Modelling using the text2vec package
V Chicago Food Inspections
62 Introduction
63 Analysis of Data ( Having Values Percentage)
64 Top Twenty Facility Types
65 Inspection Results for Top Twenty Facility Types
66 Trend of Inspections YearWise
- 66.1 All Results
- 66.2 Each Result Category
67 Trend of Inspections Monthwise
68 Trend of Inspections Daywise
69 Maps of Food Places
- 69.1 Pass or Fail Spots
- 69.2 Out of Business Spots
70 Top Twenty most Common Words
- 70.1 WordCloud of the Common Words
71 TF - IDF Theory
- 71.1 The Math
72 Term Frequency of Words (TF)
73 TF- IDF of Unigrams (One Word )
74 Word Cloud for Unigrams
75 TF - IDF Bigrams
76 Verification of important words for “Out of Business”
77 Word Cloud for Bigrams
78 Relationship among words
79 Sentiment Analysis for Results
80 Sentiment analysis by word
81 Sentiment analysis by word for Each Result Type
82 Sentiment analysis by Inspection Text
83 Plot of Results and Risks
84 Modelling with XGBoost
85 References

Little Book on Text Mining

Chapter 3 Add Feature Number of Words

We add a Feature, Number of Words for each line to the Train and Test data sets

train$len = str_count(train$text)
test$len = str_count(test$text)