References
[1]. Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman,
H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014). A review
on applications of ANN and SVM for building electrical
energy consumption forecasting. Renewable and
Sustainable Energy Reviews, 33, 102-109.
[2]. Batrinca, B., & Treleaven, P. C. (2015). Social media
analytics: A survey of techniques, tools and platforms. AI &
Society, 30(1), 89-116.
[3]. Bhojani, S. H., & Bhatt, N. (2016). Data Mining
Techniques and Trends - A Review. Global Journal For
Research Analysis, 5(5), 252-254.
[4]. Chikersal, P., Poria, S., & Cambria, E. (2015, June).
SeNTU: Sentiment Analysis of Tweets by combining a Rulebased
Classifier with Supervised Learning. In SemEval@
NAACL-HLT (pp. 647-651).
[5]. Choubey, D. K., & Paul, S. (2016). Classification
techniques for diagnosis of diabetes: review.
International Journal of Biomedical Engineering and
Technology, 21(1), 15-39.
[6]. Chu, C. H., Wang, C. A., Chang, Y. C., Wu, Y. W., Hsieh,
Y. L., & Hsu, W. L. (2016, November). Sentiment analysis on
Chinese movie review with distributed keyword vector
representation. In Technologies and Applications of
Artificial Intelligence (TAAI), 2016 Conference on (pp. 84-
89). IEEE.
[7]. Giatsoglou, M., Vozalis, M. G., Diamantaras, K.,
Vakali, A., Sarigiannidis, G., & Chatzisavvas, K. C. (2017).
Sentiment analysis leveraging emotions and word
embeddings. Expert Systems with Applications, 69, 214-
224.
[8]. Gupta, P., Sharma, A., & Grover, J. (2016,
September). Rating based mechanism to contrast
abnormal posts on movies reviews using MapReduce
paradigm. In Reliability, Infocom Technologies and
Optimization (Trends and Future Directions) (ICRITO), 2016
5th International Conference on (pp. 262-266). IEEE.
[9]. Hernández-Pereira, E., Bolón-Canedo, V., Sánchez-
Maroño, N., Álvarez-Estévez, D., Moret-Bonillo, V., &
Alonso-Betanzos, A. (2016). A comparison of
performance of K-complex classification methods using
feature selection. Information Sciences, 328, 1-14.
[10]. Kannvdiya, M., Patidar, K., & Kushwaha, R. S.
(2016). A Survey on: Different Techniques and Features of
Data Classification. International Journal of Research in
Computer Applications and Robotics, 4(6), 1-6.
[11]. Kaur, S., & Grewal, A. K. (2016). A Review paper on
Data Mining Classification Techniques for Detection of
Lung Cancer. International Research Journal of
Engineering and Technology (IRJET), 3(11), 1334-1338.
[12]. Khan, F. H., Qamar, U., & Bashir, S. (2016). SentiMI:
Introducing point-wise mutual information with
SentiWordNet to improve sentiment polarity detection.
Applied Soft Computing, 39, 140-153.
[13]. Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data
mining techniques and applications–A decade review
from 2000 to 2011. Expert Systems with Applications,
39(12), 11303-11311.
[14]. Modha, J. S., Pandi, G. S., & Modha, S. J. (2013).
Automatic sentiment analysis for unstructured data.
International Journal of Advanced Research in
Computer Science and Software Engineering, 3(12), 91-
97.
[15]. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S.,
& Coello, C. A. C. (2014). A survey of multiobjective
evolutionary algorithms for data mining: Part I. IEEE
Transactions on Evolutionary Computation, 18(1), 4-19.
[16]. Nehra, N. (2014). A Survey on Sentiment Analysis of
Movie Reviews. International Journal of Innovative
Research In Technology (IJIRT), 1(7), 36-40.
[17]. Raghuvanshi, N., & Patil, J. M. (2016, March). A brief
review on sentiment analysis. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International
Conference on (pp. 2827-2831). IEEE.
[18]. Sahin, H., & Subasi, A. (2015). Classification of the
cardiotocogram data for anticipation of fetal risks using
machine learning techniques. Applied Soft Computing,
33, 231-238.
[19]. Sahu, T. P., & Ahuja, S. (2016, January). Sentiment
analysis of movie reviews: A study on feature selection &
classification algorithms. In Microelectronics, Computing
and Communications (MicroCom), 2016 International
Conference on (pp. 1-6). IEEE.
[20]. Sharma, P., & Mishra, N. (2016, October). Feature
level sentiment analysis on movie reviews. In Next
Generation Computing Technologies (NGCT), 2016 2nd
International Conference on (pp. 306-311). IEEE.
[21]. Singh, V. K., Piryani, R., Uddin, A., & Waila, P. (2013,
March). Sentiment analysis of movie reviews: A new
feature-based heuristic for aspect-level sentiment
classification. In Automation,Computing ,
Communication, Control and Compressed Sensing
(iMac4s), 2013 International Multi-Conference on (pp.
712-717). IEEE.
[22]. Teng, Z., Vo, D. T., & Zhang, Y. (2016). Context-
Sensitive Lexicon Features for Neural Sentiment Analysis. In
EMNLP (pp. 1629-1638).
[23]. Tripathy, A., Agrawal, A., & Rath, S. K. (2016).
Classification of sentiment reviews using n-gram machine
learning approach. Expert Systems with Applications, 57,
117-126.
[24]. Vaghela, V. B., & Jadav, B. M. (2016). Analysis of
Various Sentiment Classification Techniques. Analysis,
140(3), 22-27.
[25]. Yao, D., Yang, J., & Zhan, X. (2013). An improved
random forest algorithm for class-imbalanced data
classification and its application in PAD risk factors
analysis. The Open Electrical & Electronic Engineering
Journal, 7, 62-70.
[26]. You, Y. S., Lee, S., & Kim, J. (2016, October). Design
and development of visualization tool for movie review
and sentiment analysis. In Proceedings of the Sixth
International Conference on Emerging Databases:
Technologies, Applications, and Theory (pp. 117-123).
ACM.
