This document discusses sentiment analysis and techniques for predicting sentiment intensity as valence-arousal values. It introduces convolutional neural networks (CNNs) for sentiment analysis that represent words as dense vectors and learn relationships between words and sentiment through convolutional and pooling layers. CNN models outperform lexicon-based methods in predicting valence-arousal ratings, with mean squared error rates ranging from 0.61 to 2.25 across datasets. Transfer learning is proposed to improve performance by pre-training a CNN on sentiment classification tasks and fine-tuning it for valence-arousal prediction.

1. Yunchao He (何云超)
2015.9.15 @ Yuan Ze University

2.  “unbelievably disappointing”
 “Full of zany characters and richly applied satire, and some great plot twists”
 “this is the greatest screwball comedy ever filmed”
 “It was pathetic. The worst part about it was the boxing scenes.”
 Sentiment Analysis
 Using NLP, statistics, or machine learning methods to extract, identify, or otherwise
characterize the sentiment content of a text unit
 Sometimes called opinion mining, although the emphasis in this case is on extraction
 Other names: Opinion extraction、Sentiment mining、Subjectivity analysis
2

3. 3

4.  Movie: is this review positive or negative?
 Products: what do people think about the new iPhone?
 Public sentiment: how is consumer confidence? Is despair increasing?
 Politics: what do people think about this candidate or issue?
 Prediction: predict election outcomes or market trends from sentiment
4

5.  Short text classification based on Semantic clustering
 Sentiment intensity prediction using CNN
 Transfer Learning*
* Future works 5

6.  People express opinions in complex ways
 In opinion texts, lexical content alone can be misleading
 Intra-textual and sub-sentential reversals, negation, topic change common
 Rhetorical devices such as sarcasm, irony, implication, etc.
6

8.  Tokenization
 Feature Extraction: n-grams, semantics, syntactic, etc.
 Classification using different classifiers
 Naïve Bayes
 MaxEnt
 SVM
 Drawback
 Feature Sparsity
S1: I really like this movie
[...0 0 1 1 1 1 1 0 0 ... ]
8
S1: This phone has a good keypad
S2: He will move and leave her for good

9.  Using clustering algorithm to aggregate short text to form big clusters, in which
each cluster has the same topic and the same sentiment polarity, to reduce the
sparsity of short text representation and keep interpretation.
S1: it works perfectly! Love this product
S2: very pleased! Super easy to, I love it
S3: I recommend it
it works perfectly love this product very pleased super easy to I recommend
S1: [1 1 1 1 1 1 0 0 0 0 0 0 0]
S2: [0 0 0 1 0 0 1 1 1 1 1 1 0]
S3: [1 0 0 0 0 0 0 0 0 0 0 1 1]
S1+S2+S3: [...0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0...]
9

10.  Training data labeled with positive and negative polarity
 K-means clustering algorithm is used to cluster positive and negative text
separately.
 K-means, KNN, LDA…
works perfectly! Love this product
completely useless, return policy
very pleased! Super easy to, I am pleased
was very poor, it has failed
highly recommend it, high recommended!
it totally unacceptable, is so bad
works perfectly! Love this product
very pleased! Super easy to, I am pleased
highly recommend it, high recommended!
completely useless, return policy
was very poor, it has failed
it totally unacceptable, is so bad
Topical clusters
10

11. Classifier: Multinomial Naive Bayes
Probabilistic classifier: get the probability of label given a clustered text
,
1
arg max ( | )
arg max ( ) ( | )
Ci
i
s S
i j
s S j N
s P s C
P s P C s

  

 
$
( ) sN
P s
N

,
,
( , ) 1
( | )
( | ) | |
i j
i j
x V
N C s
P C s
N x s V




Bayes’ theory
Independent assumption
11

12.  Given an unlabeled text , we use Euclidean distance to find the most similar
positive cluster , and the most similar negative cluster
 The sentiment of , is estimated according to the probabilistic change of the
two clusters when merging with . (vs. KNN)
 This merging operation is called two-stage-merging method, as each
unlabeled text will be merged two times.
0, | ( ) ( ) | | ( ) ( ) |
( )
1, .
m m n n
j
P NC P C P NC P C
f x
otherwise
   
   
 

mC 
jx
nC 
jx
jx
12

13.  Dataset: Stanford Twitter Sentiment Corpus (STS)
 Baseline: bag-of-unigrams and bigrams without clustering
 Evaluation Metrics: accuracy, precision, recall
 The average precision and accuracy is 1.7% and 1.3% higher than the baseline
method.
Methods Accuracy Precision Recall
Our Method 0.816 0.82 0.813
Bigrams 0.805 0.807 0.802
13

15.  Continuous sentiment intensity provides fine-grained representation of sentiment.
 Representing sentiment as Valence-arousal can easily convert to discrete categories.
“unbelievably disappointing” Model
V: -0.5
A: 0.3
15

16.  Lexicon based Method. To find the relationship between word-level and sentence-level
sentiment values. Word-level information comes from sentiment lexicon, e.g. ANEW.
 Paltoglou 2013: Weighted Arithmetic Mean、Weighted Geometric Mean
 Malandrakis 2013: linear regression
Paltoglou, G., Theunis, M., Kappas, A., & Thelwall, M. (2013). Predicting emotional responses to long informal text. Affective Computing, IEEE Transactions on, 4(1), 106-115.
Malandrakis, N., Potamianos, A., Iosif, E., & Narayanan, S. (2013). Distributional semantic models for affective text analysis. Audio, Speech, and Language Processing, IEEE
Transactions on, 21(11), 2379-2392.
16

17.  To find the relationship between words and sentence-level sentiment.
CNN Method Lexicon-based Methods
Word Dense vector VA value
Relationship Auto learned Manually specified
Training data Many Few or None
Word Order Considered* Not Considered
Interpretation Black Box Easy
17

18.  To find the relationship between words and sentence-level sentiment.
 Sentence Matrix -> Convolution Operator -> Max Pooling -> Regression
 Word Representation: dense vector, distributed representation
我们的
心
不像
明镜
不可以
美丑
善恶
全部
包容
boat
ship
vessel
good
happy
Beijing
Shanghai
glad
Semantic information of word is encoded in the dense vector. 18

19.  Sentence Matrix -> Convolution Operator -> Max Pooling -> Regression
我们的
心
不像
明镜
不可以
美丑
善恶
全部
包容Sentence
Matrix
[ : 1,:]( )i i i mc f w S b   
 Dimension Reduced
 Reduce the parameters of the model
 Parameter sharing
f: Activation function, Relu, tanh, sigmoid, …
𝑓 𝑥 = max(0, 𝑥)
19

20.  Sentence Matrix -> Convolution Operator -> Max Pooling -> Regression
 Aggregate the information and capture the most important features
我们的
心
不像
明镜
不可以
美丑
善恶
全部
包容
3 6 79 7 54
79 9
Max pool with 5×1 filters and stride 1
20

21.  Sentence Matrix -> Convolution Operator -> Max Pooling -> Regression
我们的
心
不像
明镜
不可以
美丑
善恶
全部
包容
x1
x2
xn
linear ℎ 𝑥𝑖, 𝑤 = 𝑤𝑇𝑥𝑖 = 𝑦𝑖
Objective function: mean squared error (MSE)
21

22.  Learning Algorithm: stochastic gradient descent (SGD)
 Learning the parameters of the model with labeled data
 Word vectors
 Convolution filters weights
 Linear regression weights
 Labeled data
 Chinese: CVAT dataset
 English: VADER dataset
Dataset size #word L Dims
CVAT 720 21094 192.1 V+A
Tweets 4000 15284 13.62 V
Movie 10605 29864 18.86 V
Amazon 3708 8555 17.3 V
NYT 5190 20941 17.48 V
22

23.  All the dataset is separated into training set, validation set and test set for model training,
hyper-parameters selection and model evaluation.
 Evaluation Metrics
 MSE, Mean Square Error
 MAE, Mean Absolute Error
 Pearson’s correlation coefficient r
%2
1
1
( )
n
i i
i
MSE y y
n 
 
%
1
1
| |
n
i i
i
MAE y y
n 
 
% %
% %
1 1 1
2 2
1 1 1 1
1 1
( )( )
1 1
( ) ( )
n n n
i ii j
i j j
n n n n
i ii j
i j i j
y y y y
n n
r
y y y y
n n
  
   
 

  
  
   
23

24. Methods CNN wGW RMAR LCEL RMV
Metrics MSE MAE r MSE MAE r MSE MAE r MSE MAE r MSE MAE r
valence ratings prediction
CVAT 1.17 0.88 0.73 2.30 1.23 0.62 1.89 1.14 0.63 1.81 0.95 0.66 1.49 0.98 0.72
Tweets 1.00 0.76 0.79 2.54 1.25 0.65 1.30 0.89 0.69 1.25 0.85 0.75 1.18 0.86 0.74
Movie 2.14 1.18 0.67 6.46 2.02 0.17 3.54 1.73 0.16 2.54 1.36 0.42 2.25 1.26 0.62
Amazon 1.50 0.95 0.67 3.75 1.51 0.35 2.66 1.38 0.27 1.45 1.14 0.45 2.20 1.19 0.56
NYT 0.84 0.72 0.36 3.47 1.54 0.28 0.79 0.71 0.26 0.83 0.75 0.37 0.61 0.63 0.60
arousal ratings prediction
CVAT 0.98 0.81 0.64 1.34 0.94 0.31 1.20 0.89 0.35 1.07 0.91 0.62 0.98 0.79 0.53
CNN method improved the VA prediction experiment performance compared with the lexicon-based and
RMV method.
Baseline method:
• wGW, Weighted geometric mean method
• RMAR, Regression on mean affective ratings
• LCEL, linear combination using expanded lexicon
• RMV, regression on mean vectors method
24

25.  Using Transfer Learning Techniques to improve VA prediction performance.
 Motivation: There are numerous dataset for sentiment classification but only a few dataset
for VA prediction. The sentiment polarity maybe useful for VA prediction.
 Method: Pre-training the classification-CNN model, and then use the parameters of the pre-
trained networks as the initial value of VA prediction-CNN model, keep training on VA corpus.
25

26. 何云超 yunchaohe@gmail.com
Thank you
26