1. Lyric Classification by
Pronouns
-By Zhiyu Huo and Matthew Luce

2. Problem Introduction
• Sentiment analysis aims to determine the
attitude of a speaker or a writer with respect
to some topic or the overall contextual
polarity of a document.
• In our project, we use pronounce-based
feature to classify lyrics

3. Problem Introduction
• We work on four classification topics. They
are:
– Mood:
• Self-Center
• Self-Reflective
– Target Figure:
• Speaking Globally
• Speaking about a relationship
• We develop binary classifier to see if a song
can be classified to a category or not.

4. Dataset
• 222 songs from different genres and singers
are selected and manually labeled by Luce and
me.

5. Features
• We define features based on pronouns
– Pronouns frequency (PF)
– Regular expression (RE)
– Contextual (CON)

6. PF
•
•
•
•
•
•
•
•
•
•
I: {I, me, my, mine}
You: {you, ya, your, yours}
He/She: {he, him, his, she, her, hers}
We: {we, us, our, ours}
They: {they, them, their, theirs}
First: { I, me, my, mine, we, us, our, ours}
Second: { you, ya, your, yours }
Third: { he, him, his, she, her, hers, they, them, their, theirs}
Singular: { I, me, my, mine, he, him, his, she, her, hers, you, ya, your, yours}
Plural: { we, us, our, ours, you, ya, your, yours, they, them, their, theirs }
Feature Value
𝑁𝑖
𝑓𝑖 =
𝑁

7. RE
•
•
•
•
•
•
•
•
r”I .* her”: e.g. “I miss her so much.”
r”You .* my”: e.g. “You got my attention.”
r”^I .*”: e.g. “I am riding a tank.”
r”They .* us”: e.g. “They wanna control us.”
r”Let us .*”: e.g. “Let us share the world.”
r”You .* me”: e.g. “You raise me up.”
r”We .*you”: e.g. “We will rock you for free.”
……
The value of each element in the feature vector is
𝑁𝑘
𝑓𝑘 =
𝑁𝑆

8. CON
context = [[1, 22], [25, 25], [25, 22], [23, 22], [22, 23], [23, 23],
[1, 1], [24, 23], [23, 1], [1, 24], [24, 24], [22, 1], [23, 10],
[10, 22], [22, 22], [7, 24], [26, 23], [23, 11], [23, 24], [23, 25],
[25, 23], [24, 7], [16, 16], [5, 24], [22, 25], [26, 24], [24, 22],
[11, 1], [22, 24], [24, 11], [13, 23], [7, 23], [1, 23], [27, 22],
[11, 24], [23, 13], [23, 26], [26, 22], [26, 26], [13, 13], [13, 22],
[1, 15], [15, 18], [27, 11], [11, 22], [22, 26], [27, 1], [24, 27],
[27, 23], [7, 25], [24, 25], [25, 24], [24, 1], [1, 26], [22, 11],
[11, 23], [23, 27], [27, 27], [27, 24], [22, 10], [23, 16], [10, 11],
[16, 23], [25, 12], [12, 22], [26, 12], [12, 25], [16, 1], [12, 24],
[1, 25], [22, 27], [15, 25], [25, 1], [24, 26], [25, 13], [13, 24],
[24, 5], [1, 16], [16, 24], [25, 10], [10, 23], [16, 26], [15, 24],
[24, 13], [1, 11], [26, 13], [11, 11], [24, 16], [26, 1]]

9. Classifier - Boosting
• Boosting: Using multiple weak classifiers to
build a strong one.
• For the first three features, we train linear
SVM on them. Then we build a boosting
classifier based on that

10. Experiment & Result
• Tools: Python NLTK & MATLAB
S-C
Boosting
Recall
0.66
0.76
0.57
0.96
0.31
0.40
0.55
0.71
Recall
0.66
0.67
0.61
0.99
0.33
0.31
0.53
0.69
Recall
0.48
0.56
0.40
0.87
Precision
CON
SP-R
Precision
RE
SP-G
Precision
PF
S-R
0.29
0.50
0.48
0.74
Recall
0.76
0.64
0.59
0.99
Precision
0.36
0.44
0.64
0.81

11. Difficulty and Challenging
• Feature Selection:
– From frequency to context
• Dataset Bias:
– All data is human labelled (by us)
– Mood metric

12. Conclusion
• We developed and tested three kings of
pronouns feature. In addition, we trained
boosting classifier for a better result.
• The pronouns feature is more effective on
“Target Figure” problem rather than “Mood”
Problem
• This kind of detector can be used in song
searching