Determination of Mental States from Texts Using Evolutionary Imperialist Competitive Algorithm and Convolution Neural Networks

Document Type : Persian Original Article

Authors

Computer Dept.; Engineering Faculty, Razi University, Kermanshah, Iran

Abstract

Abstract- The aim of this study is to investigate writings to find out the mood of people in typing texts. In this study, 14640 tweets related to airlines were used to analyze emotions in three categories: positive, negative and neutral. The novel proposed approach has three main steps. In the first step, we perform a pre-processing operation to purify the dataset. In the second step, using the Imperialist Competitive Algorithm (ICA), the main keywords from all the existing texts are extracted. Keywords are the words that have the most impact on categorization. Then, a convolution neural network (CNN) is exploited to extract more features. In the last step, classification, using a multilayer perceptron neural network (MLP), is applied. In the proposed new method, unlike the conventional methods in which words go to the next stage after preprocessing, we use the Imperialist Competitive Algorithm to extract the main words from all these words, which in turn causes There is a significant reduction in the volume of input words.using this new proposed approach, we achieved precision, accuracy and recall of 0.990, 0.983 and 0.875, respectively. The experimental results indicated the superiority of the proposed method the comparison with other well-known approaches.

Keywords

References

[1]   www.statista.com, https://www.statista.com/statistics/346167/facebook-global-dau, (2020).
[2] Binali, Haji, Chen Wu, and Vidyasagar Potdar. "Computational approaches for emotion detection in text." 4th IEEE International Conference on Digital Ecosystems and Technologies, IEEE, (2010).
[3] Neethu, M. S., and R. Rajasree. "Sentiment analysis in twitter using machine learning techniques." Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE,) 2013(.
[4] Liu, Gang, and Jiabao Guo. "Bidirectional LSTM with attention mechanism and convolutional layer for text classification." Neurocomputing, )2019(.
[5] Bandhakavi, Anil, et al. "Lexicon generation for emotion detection from text." IEEE intelligent systems 32.1, (2017).
]6[ سارا منتظریان، سید محمدحسین معطر. "تشخیص احساس متن فارسی با استفاده از روش مبتنی بر شباهت معنایی." کنگره بین‌المللی فن‌آوری، ارتباطات و دانش، )1394(.
[7] Zobeidi, Shima, Marjan Naderan, and Seyyed Enayatallah Alavi. "Opinion mining in Persian language using a hybrid feature extraction approach based on convolutional neural network." Multimedia Tools and Applications 78.22 ,(2019).
[8] Zheng, Zhaohui, and Rohini Srihari. "Optimally combining positive and negative features for text categorization." ICML 2003 Workshop, (2003).
[9] Lucini, Filipe R., et al. "Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews." Journal of Air Transport Management 83, (2020).
[10] tan, Pang-Ning. Steinbach, Michael. Kumar, Introduction to data mining. Pearson Education, (2014).
[11] Medhat, Walaa, Ahmed Hassan, and Hoda Korashy. "Sentiment analysis algorithms and applications: A survey." Ain Shams engineering journal 5.4, (2014).
[12] Ravi, Kumar, and Vadlamani Ravi. "A survey on opinion mining and sentiment analysis: tasks, approaches and applications." Knowledge-Based Systems 89, (2015).
[13] Mohammad, Saif M., and Peter D. Turney. "Crowdsourcing a word–emotion association lexicon." Computational Intelligence 29.3, (2013).
[14] Strapparava, Carlo, and Alessandro Valitutti. "Wordnet affect: an affective extension of wordnet." Lrec. Vol. 4. No, )2004(.
[15] Ahmad, Munir, et al. "Tools and techniques for lexicon driven sentiment analysis: a review." Int. J. Multidiscip. Sci. Eng 8.1 , (2017).
[16] LeCun, Yann. "LeNet-5, convolutional neural networks". Retrieved 16 November, (2013).
[17]  Zhang, Wei. "Shift-invariant pattern recognition neural network and its optical architecture". Proceedings of annual conference of the Japan Society of Applied Physics, (1988).                                                                              
[18] Zhang, Wei. "Parallel distributed processing model with local space-invariant interconnections and its optical architecture". Applied Optics, (1990) .
 
]19 [ فاطمه عباسی، بابک سهرابی، امیر مانیان، آمنه خدیور. "ارائه مدلی جهت دسته‌بندی احساسات خریداران کتاب با استفاده از رویکردی ترکیبی." فصلنامه مطالعات مدیریت کسب‌وکار هوشمند سال ششم، شماره 21 ، ( 1396).
[20] E. Atashpaz-Gargari and C. Lucas, "Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition," 2007 IEEE Congress on Evolutionary Computation, Singapore, )2007).
[21] Qiancheng Fang, Hoang Nguyen, et al. " Prediction of Blast-Induced Ground Vibration in Open-pit Mines Using a New Technique Based on Imperialist Competitive Algorithm and M5Rules." Natural Resources Research, (2019).
 [22] Vijayarani, S., and R. Janani. "Text mining: open source tokenization tools-an analysis." Advanced Computational Intelligence: An International Journal (ACII) 3.1, (2016).
]23[ نیکنام فرزاد و نیک نفس علی اکبر. "بهبود روش‌های متن‌کاوی در کاربرد پیش‌بینی بازار با استفاده از الگوریتم‌های انتخاب نمونه اولیه." مدیریت فناوری اطلاعات، دوره 8، شماره2, (1395).           
[24] Rustam, Furqan, et al. "Tweets Classification on the Base of Sentiments for US Airline Companies." Entropy 21.11 (2019).
[25] Rane, Ankita, and Anand Kumar. "Sentiment classification system of twitter data for US airline service analysis." 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). Vol. 1. IEEE, )2018(.
[26] Acosta, Joshua, et al. "Sentiment analysis of twitter messages using word2vec." Proceedings of Student-Faculty Research Day, CSIS, Pace University 7, (2017).
[27] Wazery, Yaser Maher, Hager Saleh Mohammed, and Essam Halim Houssein. "Twitter Sentiment Analysis using Deep Neural Network." 2018 14th International Computer Engineering Conference (ICENCO).IEEE, ( 2018).
Journal of Soft Computing and Information Technology
Volume 10, Issue 1 - Serial Number 31
Spring
April 2021
Pages 13-23

Files

Share

How to cite

Statistics