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(Liu, 2012)
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Text mining
2
Sentiment analysis
3
Deep learning
4
Machine learning
5
Natural language processing
6
Opinion mining
""
""
""""(Liu, 2015)
(Rojas-Barahona & Maria, 2016)
7
User-generated content
8
Natural Language Processing (NLP)
Pang
)2002Pang, et al., (
NBSVM
(Wang & Manning, 2012)
PersianCluesLDASA
(Shams, et al., 2012)
(Basiri, et al., 2014)
)2015LeCun, et al., (
one-hot
(Maas, et al., 2011)
)2018Dashtipour, et al., (
Naive Bayes (NB)
11
Maximum Entropy
Support Vector Machine (SVM)
Wan
Mang
Bigram
Latent Dirichlet allocation (LDA)
17
Logistic regression
Machine Translation
Convolutional Neural Network (CNN)
20
Autoencoder
21
Multilayer perceptron (MLP)
SentiPers)2018Hosseini, et al., (
SentiPers
22
Dataset
https://www.digikala.com
24
Interface
25
https://github.com/JoyeBright/Sentiment-Analysis
)2017Xie, et al., (
(Fadaee, et al., 2017)
Data noising
27
Google Translate
GoPro HD Hero
However, as we said, the GoPro HD Hero Sport Camera is an ideal device
for recording the most exciting and memorable moments of athletes.
GoPro HD Hero Sport Camera
28
Multinomial classification
idf-tf
)2003Ramos, (
N(Sugathadasa, et al., 2018)N=2
tf-idf
tf-idf
29
Binary classification
30
Term frequency–inverse document frequency
31
Embedding
32
https://github.com/sobhe/hazm
)1997Thorsten, (
(Prasetijo, et al., 2017)(Li & Li, 2013)
SVM
SGD
NB
–
–
–
SVM
SGD
NB
–
–
–
(Day, 2016)
(Vateekul & Koomsubha, 2016)
Stochastic Gradient Descend (SGD)
G.E. Hilton
35
Recursive Neural Networks (RNN)
(Collobert, et al., 2011)
(Rojas-Barahona & Maria, 2016)
(Collobert, et al., 2011)
)2010Turian, et al., (
36
Deep Belief Networks (DBN)
37
Long short-term memory (LSTM)
38
Semantic
39
Grammatical
40
Word Feature
Keras
FastText
""""""
""
""""""
FastText
FastText
41
Pre-trained Word Embedding
42
https://fasttext.cc/docs/en/crawl-vectors
Keras
(Srivastava, et al., 2014)
BI- LSTM
FastText
CNN
Keras
Dropout
Overfitting
Bidirectional Long Short-Term Memory (BI-LSTM)
(Kim, 2014)
CNN
FastText
CNN Keras
LSTM FastText
LSTM Keras
–
–
–
CNN
FastText
CNN Keras
LSTM FastText
LSTM Keras
–
–
–
Kim
–
–
SVM
FastTextBI-LSTMCNN
CNNKerasBI-LSTMFastText
Basiri, M. E., Nilchi, A. R. N. & Ghassem-aghaee, N., 2014. A Framework for Sentiment
Analysis in Persian.
Collobert, R. et al., 2011. Natural Language Processing (Almost) from Scratch. The Journal
of Machine Learning Research, Volume 12, pp. 2493-2537.
Dashtipour, K. et al., 2018. Exploiting Deep Learning for Persian Sentiment Analysis. s.l., s.n.
Day, M., 2016. Deep Learning for Financial Sentiment Analysis on Finance News Providers.
Fadaee, M., Bisazza, A. & Monz, C., 2017. Data Augmentation for Low-Resource Neural
Machine Translation. arXiv.
Hosseini, P. et al., 2018. SentiPers: A Sentiment Analysis Corpus for Persian. arXiv.
Kim, Y., 2014. Convolutional Neural Networks for Sentence Classification. Doha, Qatar, s.n.
LeCun, Y., Bengio, Y. & Hinton, G., 2015. Deep learning. Nature, Volume 521, pp. 436-444.
Liu, B., 2012. Sentiment Analysis and Opinion Mining. Synthesis lectures on human
language technologies, pp. 1-167.
Liu, B., 2015. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions.
s.l.:Cambridge University Press.
Li, Y.-M. & Li, T.-Y., 2013. Deriving market intelligence from microblogs. Decision Support
Systems, 55(1), pp. 206-217.
Maas, A. L. et al., 2011. Learning Word Vectors for Sentiment Analysis. Proceedings of the
49th Annual Meeting of the Association for Computational Linguistics: Human Language
Technologies, pp. 142-150.
Pang, B., Lee, L. & Vaithyanathan, S., 2002. Thumbs up?: sentiment classification using
machine learning techniques. Proceeding, pp. 79-86.
Prasetijo, A. B. et al., 2017. Hoax detection system on Indonesian news sites based on text
classification using SVM and SGD. s.l., s.n., pp. 45-49.
Ramos, J., 2003. Using TF-IDF to Determine Word Relevance in Document Queries. Arxiv.
Rojas-Barahona & Maria, L., 2016. Deep learning for sentiment analysis, Language and
Linguistics Compass. Language and Linguistics Compass.
Shams, M., Shakery, A. & Faili, H., 2012. A non-parametric LDA-based induction method for
sentiment analysis. Shiraz, Iran, s.n.
Srivastava, N. et al., 2014. Dropout: a simple way to prevent neural networks from
overfitting. Journal of Machine Learning Research, Volume 15, pp. 1929-1958.
Sugathadasa, K., Ayesha, B., de Silva, N. & Perera, A., 2018. Legal Document Retrieval
using Document Vector Embeddings and Deep Learning. ArXiv.
Thorsten, J., 1997. A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text
Categorization. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., pp. 143--151.
Turian, J., Ratinov, L. & Bengi, Y., 2010. Word representations: a simple and general
method for semi-supervised learning. Stroudsburg, s.n.
Vateekul, P. & Koomsubha, T., 2016. A Study of Sentiment Analysis Using Deep Learning
Techniques on Thai Twitter Data. Khon Kaen, s.n.
Wang, S. & Manning, C. D., 2012. Baselines and Bigrams : Simple, Good Sentiment and
Topic Classification. Proceedings of the 50th Annual Meeting of the Association for
Computational Linguistics: Short Papers, Volume 2, pp. 90-94.
Xie, Z. et al., 2017. Data Noising as Smoothing in Neural Network Language Models. ICLR.
