Deep Learning Framework based on Word2Vec and

CNN for Users Interests Classification

Abubakr H. Ombabi Computer Sciences.

Sudan University of

Science and Technology.

Khartoum, Sudan.

abubakr.h.ombabi@gmail.com

Onsa Lazzez REGIM-Lab.

University of Sfax.

National School of Engineers.

Sfax, Tunisia.

onsa.lazzez.tn@ieee.org

Wael Ouarda REGIM-Lab.

University of Sfax.

National School of Engineers.

Sfax, Tunisia.

wael.ouarda@ieee.org

Adel M. Alimi REGIM-Lab.

University of Sfax.

National School of Engineers.

Sfax, Tunisia.

adel.alimi@ieee.org

Abstract— Social media has given internet users a venue for

sharing and expressing their interests and opinions on different

life sides. Daily, millions of users generate huge volume of reviews

and comments on social media which reflect their opinions on

different issues. Analyzing these opinions manually is a very hard

task. Thus, opinion analysis is the task of computationally

analyzing opinions expressed in social data. However, there are

few works that have considered both sentiment analysis and

classification to determine users’ topic of interest. In this study, an

approach that combines both sentiment analysis and classification

was proposed. The main objective of this work is to design an

effective method to provide a summary of users interests from

Twitter based on their social textual data on five categories which

are sports, travel, fashion, food and religion. Thus we are able to

discover the topic in which users are interested. Inspired by the

successes of deep learning, our proposed system takes advantages

of pre-trained Word2Vec for text pre-processing and to gain

vector representations of words which will be the input for suitable

Convolutional Neural Network architecture for deep features

extraction. Rectified Linear Unit and Dropout functions were

applied to improve the accuracy. Support Vector Machine

classifier was used to predict the final classification. TensorFlow

running on Python 2.7.12 was used to implement our system. This

system was tested and validated on different publicly available

corpus of reviews and comments from Twitter. The proposed

system achieved best accuracy of 97.3% for users interests

classification.

Keywords—Sentiment Analysis; Word2Vec; CNN; Twitter.

I. INTRODUCTION

Recently, Social networks (SN) such as Twitter, Google plus, Facebook, etc have become popular channels of communication and expressing divers attitudes and opinions. A vast volume of reviews and comments are generated in social networks. These social data reflects the opinions and the sentiments on issues in different applications such as recommending systems, government, media and other activities. This huge content has gained the attention of researchers to focus on social networks analysis (SNA) in their researches to obtain valuable information form SN users. Recently, many studies have proposed several approaches to semi-automated or an automated analysis approaches which can effectively assist in analyzing and managing this huge amounts of social data instead of manually analysis these big data which is costly task.

In fact, Twitter, which is commonly popular social network, various users post tweets for specific area and event. Daily, there are more than 316 million active users on Twitter generate more than 500 million tweets. We can use these tweets for discovering their topic of interests. Divers works have been done in the field of social network analysis (SNA), namely, classification of personal attributes, sentiment analysis of twitter users based on tweets. Now, if a user is interested in that topic or event than he/she get tweet on twitter about the topic / event positive or negative based on her / his sentiment or opinion. Sentiment analysis based on these tweets is necessary to obtain the positive or negative user’ opinion. Classification is used to determine which topic corresponds to a particular tweet, thus discovering the user interest topic. However, sentiment analysis is the task of computationally obtaining and categorizing opinions expressed in textual or visual information in order to classify whether the writer's opinion towards specific service ,topic ,product, etc [2] that can be negative ,positive, or neutral. Various techniques can be applied in the area of users sentiment predictions. As Duwairi in [3] has confirmed, sentiment analysis requires an implementation of a set of algorithms in order to define and exploit emotions and opinions in social network platform. According to [4] the sentiment analysis based on text classification are focused essentially on NLP, machine learning, statistical and linguistics knowledge and text mining methods to obtain subjective information textual data. SA is used to determine the emotions and orientations from large data to assist in making predictions [5].

In this study we focused on the Twitter Social Network which is the most common social network where social users provides huge amount of textual data (tags, reviews). We proposed an approach for the users’ interests analysis based on their sentiments (positive/ negative/ neutral) and the topic of which tweets are related to, in order to obtain the correct positive or negative users’ interests. In fact, the trends of sentiment user discovery from their provided social textual data consist on the recognition of deep features that can be extracted from the social data. For this reason, we have applied well know feed-forward CNN architecture.

This paper is organized as the following: section 2 describes the related works. Section 3 describes our method for analyzing tweets to discover the users’ interests based on users’ sentiments. Section 4 presents the implementation details and

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results obtained by our approach. Conclusion and future work are given in section 5.

II. LITERATURE REVIEW According to [5], sentiment analysis studies are classified

into supervised and unsupervised based on the applied

approach. In this work we proposed a system using supervised

approach to classify the users sentiments / opinions on

particular topic of users interests from the users generated

tweets.

In supervised (corpus-based) approach, different types of

machine learning (ML) classifiers for instance Support Vector

Machine (SVM), , Naïve Bayes (NB),K-Nearest Neighbor

(KNN), Decision Tree (D-Tree), etc can be applied to a pre -

annotated database in form of training set and testing set [5].

the classifiers must be trained on the training data to later build

a model which should be used to classify testing data corpus.

Regarding SA, this approach has achieved much higher

accuracy than other approaches, However, it involves creating

and labeling a large datasets manually which is very difficult

and very time consuming process even for expertise [7]. In

addition to, this model may be a domain-biased.

At the other hand, in the unsupervised (lexicon-based)

approach, dictionary is used for the semantic polarity of a

sentence and word predictions. For this , every word is assigned

polarity (strength) value for instance range from +1 to +5 may

be used for positive polarities in which word with +5 value

means it is much more positive than word with polarity of +1.

The lexicon could be initiated manually or automatically [5]. In

the automatic approach, a list of seed words is constructed, then,

the lexicon size can be expanded by applying some words

similarities. The total polarity of the sentence can be obtained

by calculating the polarity score of each word from the

dictionary and then add these polarities scores into one score to

obtain the sentiment of the entire text. This approach does not

cope well with different domains, besides its accuracy is lower

than the supervise approach [8].

Recently, Deep learning been explored for natural language

processing (NLP) tasks [9], particularly on textual data

representations at the sentences, documents, or words levels.

In sentiment analysis and text classification, many researches

have proposed several deep learning models to gain better

performance. Convolutional Neural Networks can be defined as

kind of feed-forwarding neural network. The basic CNN

consists of convolution, fully connected, relevance weights and

pooling layers. Compared to other deep nets approaches, CNN

involves less training data and easier to train. Moreover, CNN

is characterized by its fewer parameters and connections [10].

In the sentiment analysis or opinion mining CNN has proved to

be efficient and has introduced great performance on textual

data [9]. Unlike other neural net such as RNN, we just need to

annotate the whole corpus artificially. A key enabling factors in

CNN is that it uses convolutional filters to automatically

capture and learn features suitable to a particular task. CNN

performs feature extraction by applying convolution operation,

it’s able to learn local features automatically, thus, reducing the

manual operation. CNN takes advantage of applying the same

weights of neurons on the same feature map, this enables the

network to learn in parallel [11]. In machine learning,

implementation of one deep learning algorithm cannot obtain

best results, therefore combination of deep learning algorithm

and other pre-trained methods can obtain higher accuracy.

A. Word2vec In order to transform any NLP task into machine learning

algorithms, text must firstly be transferred into corresponding

vector representation. For this there are two vectorization

algorithms. One-hot representation, in which very long vector

is used to represent the words, the vector length is the same as

the size of the dictionary used in the corpus. It uses only 1 and

0 weights. According to [10] with One-hot representations it is

not easy to depend only on words vectors to define the

relationship between words. Another approach is distributed

representation which has recorded the best performance in deep

learning field. This method is based on mapping each word into

fixed length vector, distributing these vectors to form the vector

space [12]. A word vector is described as a low dimensional

vector representations that encode semantic features of words

learned in unsupervised neural nets models on a very big text

corpus. Word2vec is a neural network used to process the text

before this text is received by deep-learning algorithms [13]. It

takes text corpus as an input and generates the word vectors as

output. The vector representation of words is obtained after

word2vec builds vocabulary from the training corpus. The

resulting word vectors file could be used as features to deep

learning algorithms. In this algorithm, the sentence words are

initially represented in form of words matrix, then it transferred

into vectors in an n-dimensional vector space. In this method,

similar words are represented near each other in the vector

space [4]. Moreover, with Word2vec features can be obtained

without human intervention. Word2Vec can also perform

effectively even when its input is an individual word. With this

tool, very accurate predictions about a word’s meaning can be

obtained and the semantic relationship between words can be

easily evaluated.

B. Social textual data analysis

Recently several studies have proposed different

approaches for user’ attributes mining such as users’

sentiments, opinions, personal information, based on their

generated textual data from social networks. In this section we

presented some of the recent studies. First of all, Conneau et al.

[14] have proposed new character level model (VD- CNN) for

Natural Language Processing (NLP) task in the Social Network

analysis area. It is the first time that very deep Convolutional

Neural Network (CNN) been applied to NLP task. For this,

deep stack of local operations, convolutions and max-pooling

of size 3 for sentence high-level representation were applied.

Ngrams and ngrams-TF-IDF were used as features. The model

was tested on eight public large-scale datasets. An architecture

of small temporal convolution filters with different types of

pooling was examined which shown that significant

enhancement of the CNN configurations can be reached when

setting the depth to 29 convolutional layers.

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For the medical field, authors in [13] have analyzed the patient

(dis)satisfaction using doctors performance reviews to predict

their ratings on different measures. A static word vector model

was used for word representation then a CNN structure was

deployed contains Convolutional Layer, ReLu Layer, Pooling

Layer, and Fully-Connected Layer. The proposed model was

validated on 35000 user reviews. The model obtained an

accuracy of 93% in predicting rating on a 5-point scale.

Still with the reviews, Sahu et al.[2] have proposed model to

determine the polarity of the movie reviews on a scale of 0 to

4. A computation linguistic technique was applied for text

preprocessing. For features extraction an approach based on

structured N-grams was used, feature extraction impact analysis

was performed by computing information gain for each feature.

Furthermore, Zhou et al, [6], have proposed a novel deep

framework for movies reviews evaluation using word2vec to

obtain words vector representations with 7-layers CNN

architecture. The CNN contains 3 pairs of convolutional layers

and pooling layer to extract sentiments from texts. This model

incorporated ReLU, Normalization and Dropout techniques.

Different classifiers were examined such as NB, SVM. The

model has achieved highest accuracy of 45.4% when compared

with RNN and MV-RNN models.

For the user’ sentiment analysis, Joulin et al.[16] have proposed

a baseline model for text classification. In this work fastText

was evaluated and compared with existing classifiers for

sentiment analysis problem. Eight datasets and evaluation

protocol were used to evaluate the model. The best accuracy

was 98.6 in fastText. The experimental results shown that fast

text classifier is often on par in terms of accuracy with other

deep learning classifiers. In [17] the authors have instructed a

novel combined method for sentiment analysis. For this, rule-

based classification, supervised learning and machine learning

approaches were used. They proposed semi-automatic,

complementary model which has achieved good level of

classification effectiveness. Pawar et al. [18], have proposed

hybrid approach for sentiment classification, on Sanders twitter

dataset, after preprocessing, several features were extracted

such as N-gram feature, Lexicon Feature, Positive lexicons. The

opinion score of each tweet is calculated to classify the tweets.

Tweet is considered to as positive if it’s calculated score is

greater than 0, if it is less than 0 it is considered as negative, and

if it is zero it is considered as neutral class. A Neural Network,

QDA, SVM, LDA, Naive Bayes, Random Forest classifiers

were evaluated. SVM and Random Forest have recorded the

highest accuracy of 88.65 for both. Despite user’ sentiment, in

[8] the authors have designed an opinion mining model for

tweeter. After tweets are crawled from Twitter, pre-processing

steps were deployed.

Recently, many works are focusing on the understanding of

users on social media using user’s generated social data on its

different types. Authors in [23] have proposed user ontology

profiling in social networks by using framework containing

Facebook application. The model aimed to predict social

networks user’s Age, Gender, Race and Smile based on social

textual and visual Data. Also authors in [24] have proposed a

novel framework to understand both textual and visual data

form social networks to extract the user’s soft biometrics

information from posted pictures. Study in [25] has investigated

classifying lie or truth from speech signal. The model was based

on the Mel Frequency Cepstral Coefficient and performed on

ReLiDDB dataset. Users interest can be applied in many

security context like in [26] which aimed to use facial biometric

modality. Gabor and LBP features for face characterization and

the Euclidian and Mahcosine distance for classification were

tested. Also work in [27] has presented an experimental study

on the proposed face recognition approaches by building

systems with different techniques for features extraction and

classification. Authors in [28] have propose a bag of

geometrical features based face recognition approaches using

SVM, GA and other algorithms. This model was performed on

the two benchmarks ORL and Caltech Faces. Also [29] has

proposed a Smart Riding Club Biometric System with new

features extraction technique based on the fusion between two

basic texture descriptors Gabor and Local Binary Pattern.

Motivated by these works, we examine if the tweets shared

by social users in Twitter can be applied to discover their topic

of interest that present an important research area in the social

network analysis process. However, it is hard to predict the

interest of social users automatically from their shared tweets

because it’s not contains all the features of each topic.

III. PROPOSED APPROACH

In this study, we proposed a novel deep framework for user’

interest discovery based on user’s sentiment / opinions. This

model which is called (Deep Text Users Interests System

(DTUIS)) Focused on using Word2vec, CNN, and the

supervised classifier SVM.

Fig.1 shows the basic flow diagram of (DTUIS), first, we have

used some standard databases that contains set of tweets in

order to determine the interests according to their contained

text. Sentiment analysis has been done on tweets to know the

inclination of users, whether he/she is positively indicated his

sentiment over a particular topic or not.

Fig .1. Overview of the proposed approach

We have used matching of words for tweets classification to

categorize it under a certain topic (Sport, Religion, Culture,

Food and Fashion). Finally, user’ interest is obtained that shows

the positive of user’ inclination towards a specific topic.

In (DTUIS) we have used word2vec to transform tweets into its

corresponding vectors to build up the sentences vectors, and

then we used the word vector file which is generated by the

word2vec as the input data to the CNN to perform features

extraction. Finally, to classify the sentences into different

sentiment labels we used linear Support Vector Machine in

order to predict whether the user sentiment is positive, negative

or natural on the interest in various topics. Fig. 2 illustrates the

overall process of (DTUIS).

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In the following, we will present and details each step.

Fig. 2. Deep Text Users Interests System (DTUIS).

A. Data Collection This architecture was trained and validated on two publicly

available corpuses of pre-labeled tweets, one is the Movie

Reviews corpus originally collected and published by Pang et

al. 2002. It contains 10662 sentences balanced into positive and

negative. Second dataset is Sanders-Twitter Sentiment Corpus

version 0.2 created by Pawar et al. 2015 [18], contains 5500

hand-classified tweets on 4 topics , the tweets are labeled as

positive, negative, neutral and irrelevant. Table.1 illustrates

details of these datasets.

TABLE 1.Sanders and MR datasets labels distribution

Label

Number of reviews

Sanders MR dataset

Positive 570 5331

Negative 654 5331

Neutral 2503 -

Irrelevant 1786 -

Total 5513 10662

B. Preprocessing In (DTUIS), word2vec is used to obtain vectors

representation of words, these vectors are the input to the CNN.

In this paper, we use publicly available pre-trained Word2vec

model (static word vector model) which is trained on Google

News dataset of about 100 billion words [6], this model

contains 3 million words and phrases from Google News each

word is represented in 300-dimensional vector. From this big

corpus we can obtain precise relations of words.

C. Features Extraction After we obtained words vectors representation using pre-

trained word2vec, we will train Convolutional Neural Network.

The CNN architecture used in this system is inspired by the

CNN architecture used in [19]. In this architecture, the input to

the network is a sequence of words (the input sentence). Each

word is represented as vector, all vectors have the same length.

A sentence is represented as a 2-dimensional matrix. Our CNN

architecture as illustrated in Fig. 3 consisted of Convolutional

Layer for automatically features extraction using three

convolution kernels (convolution filters) of different sizes,

ReLu Layer, Pooling Layer with nonlinear sampling method in

order to decrease the number of characteristic parameters and

prevent overfitting, and Fully-Connected Layer.

Fig. 3. CNN architecture.[1]

At the input layer, the sentences of length k are considered

as vector of words, each word is represented as 300-dimentions

vector. A sentence becomes 2-dimensional matrix. The

sentence is considered as concatenation of words (word

vectors). Convolution can be defined as a binary operation

requiring two operands both of them represented as matrix, one

is the text segment and the other is the convolution filter (CF).

The output of this process is a single real number. The input

words is a matrix of the words vectors. A CF is also a matrix of

the same dimensions as the earlier one. A particular adapted CF

convolves on the input text matrix using a sliding window and

produces many real numbers outputs. The resulting sequence of

real numbers called feature map which is corresponds to a

particular CF being used. In this model, let be the

corresponding k-dimensional word vector to the i-th word in the

input sentence. A sentence of length n is represented as a

concatenation of words vectors as illustrated in (1). As we

stated earlier the sentence with length less than n will be padded

where necessary.

X1:n = X1 ⊕ X2 ⊕ …. ⊕ Xn, (1)

Where ⊕ denoting for the concatenation operator.

Generally, assume that is the concatenation of words

, a convolutional layer performs convolution

operation using convolution filter (h is the filter

height or the sliding window size) to each of the windows with

k width. In other words it is a matrix of size h×k, and is

the basic element from the i-th to the (i+j)-th, which represents

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the local feature matrix from the i-th line to the (i+j)-th line of

a sentence word vector. For instance, a feature (i-th feature

value) can be produced from a window of words using

(2).

(2)

Where f is a nonlinear activation function (convolution filter

function) commonly used RELU, hyperbolic tangent and

Sigmoid, etc.[1]. b is a bias term ((b ∊ R) which is a parameter

need to be learned as W during the training task . To generate a

feature map the filter convolves to each window of words in the

sentence matrix { as shown in (3).

C = (3).

Note that C ∊

Pooling mechanism is applied using max-overtime pooling for

features sampling [20] over the feature map and maximum

value of local feature is captured as the feature corresponding

to current filter using (4). The idea is to select the highest value-

feature that is the most important feature on each feature map. ĉ = max {C}. (4)

In this system we used multiple filters with different window

sizes as illustrated on Table. 2 to obtain multiple features, we

have presented the process using one feature which extracted

with one convolution filter. A fully connected layer receives the

selected feature vector as input and then Support Vector

Machine (SVM) classifier is applied to obtain the final

classification result. In this model, for regularization we applied

dropout with a constraint on l2-norms of the weight vectors

during the training [19]. With dropout we can solve significant

issue in machine learning, which is overfitting. Dropout

prevents co-adaptation of hidden units [19]. Dropout performs

setting to zero the output of each hidden neuron with p of 0.5.

The algorithm drops out the neurons which does not contribute

to the forward and back propagation passes. In this model, all

of the neurons are used but their outputs are multiplied by 0.5

after the convolutional layer. For all datasets and experiments in this paper different

parameters are set uniformly as shown in Table. 2. CF weights

W values and softmax layer weights U were assigned uniformly

from [-0:1; 0:1]. 100 feature maps were used for each of these

filter sizes creating a total of 300 feature maps. Learning rate

technique was applied for stochastic gradient descent with a

maximum of 100 training epochs. Batch size was set to 64 with

zero padding as needed.

TABLE. 2. CNN Parameters Settings

Parameter Value

Padding length 64

Word vector dimension 300

Filter region sizes 3,4,5

activation function ReLU (rectified linear unit)

Dropout probability parameter p 0.5

pooling 1-max pooling

L2 regularization lambda 0.0

IV. EXPERIMENTAL RESULTS In this framework, initially, word2vec was used to transform each word into a vector, in this way we can construct the sentences’ vectors. Then these sentences’ vectors are taken as inputs by then CNN for features extraction, in order to classify the sentences to positive/negative sentiment labels we used Support Vector Machine (SVM). We implemented our model an open source framework called TensorFlow running on Python 2.7.12.

A. Evaluation Measures For each experiment, popular evaluation measures such as

(Accuracy, Recall, Specificity, Precision etc.) were used to

evaluate the performance of (DTUIS). Table.3 presents the

calculated values of these measures on movie reviews and

sander datasets for binary classification. The calculated values

of these measures have proved that our approach is accurate and

promising in opinion mining, particularly when considering

users internets prediction. We recorded best Recall (REC),

Specificity, Precision, and F-score on MR dataset while the

measures values were decreased on Sander dataset.

TABLE. 3. Evaluation measures values from the obtained confusion matrices.

Evaluation Metrics Movie

Reviews

Sanders

Accuracy (ACC) 0.97 0.96

Recall ( REC) 1.0 0.96

Specificity (SP) 1.0 0.95

Precision (PRE) 0.99 0.95

False positive rate (FPR) 0.0 0.0

F-score (F1) 0.97 0.96

B. Comparison with other approaches We also evaluated and compared our results of sentimental

analysis with other previous studies on the same datasets. As

shown on Table. 4 for movie reviews dataset, the study by Pang

et al in [21] which is the first work done in this area has obtained

an accuracy of 87.2%. Other work in [22] by Mullen has

obtained an accuracy of 86% , Parabowo in [17] achieved best

accuracy of 87.3% using 10-fold & 5-fold cross validation,

work in [2] done by Sahu has achieved best accuracy of 88.95%

on Random Forest classifier. From this comparison it is found

out that our approach (DTUIS) that used word2vec, CNN, and

SVM outperformed other approaches and reached an accuracy

level of 97.3% which is promising result when it is compared

with other state of-the-art results on a movie review.

We also evaluated and compared our results on sander dataset

with Twitter Sentiment Classification done by Pawar at el in

[18] in which an opinion score of each tweet is calculated using

feature vectors. Then, Neural Network ,QDA, SVM, Naïve,

LDA, Random Forest machine learning classifiers were

evaluated where best classification accuracy reached 88.65%

with SVM followed by 88.62 on Neural Network. As shown on

Table. 5 (DTUIS) outperformed this model with best accuracy

of 96%.

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TABLE 4. Comparisons on Movie reviews dataset

Reference Features

Extraction

Classification Accuracy

Pang et al. [21] unigrams NB, ME, SVM 82.9%

Mullen et al. [22] Part-of Speech SVM 86%

Prabowo et al.[17] Part-of-Speech

taggers

SVM 87.3%

Sahu [2] Bi-grams,

Tri-grams

NB,KNNRF,

DT

88.95%

(DTUIS) CBOW SVM 97.3%

TABLE. 5. Comparisons on Sanders dataset

Reference Features

Extraction

Classification Best accuracy

Pawar at el [18] N-gram,

POS

SVM, Naive

Bayes, Random Forest

88.65% on

SVM

(DTUIS)

CBOW

SVM

96 %

Fig. 4, Fig. 5, illustrate the confusion matrix obtained when

running our system on Movie Reviews and Sanders datasets

respectively. TP = True Positive. FP= False Positive. FN= False

Negative. TN= True Negative. 0,1 for positive and negative

respectively. 2,3 for natural and irrelevant respectively.

Fig. 4. Confusion Matrix of Films Interest Classification on Movie Review

Dataset.

Fig. 5.Confusion Matrix of Sentiment Classification on Sanders Dataset.

V. CONCLUSION

Sentiment mining is an attractive and challenging area. In this

effort, we presented solution to this problem in form of a system

to classify the user interest in a particular topic. We have

described our interesting framework of convolutional neural

network and wor2vec to solve this problem. We have presented

our interesting results on the available public datasets. The

experimental results indicated that CNN with pre-trained

word2vev can outperform and record new state-of-the-art

scores over different classification algorithms. Since there are

more large scale user generated information in an online

environments. Sentiment analyses provide very important tools

for managing this information in predictions. SA represents rich

source of valuable information used in a wide application such

as public opinion, product analysis, etc. We believe that this

approach of sentiment analysis in mining users interests could

give further inspiration to other researchers. Future researches

direction can be in extending this approach to other domains of

opinion mining such as political discussion, newspaper articles,

etc. also it is recommended to extend this work to other

languages. Examine the architecture with other ML classifiers.

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