Sentiment Classification Performance Analysis Based on Glove Word Embedding

Year 2021, Volume: 25 Issue: 3, 639 - 646, 30.06.2021

Yasin Kırelli , Şebnem Özdemir

https://doi.org/10.16984/saufenbilder.886583

Cited By: 1

Abstract

Representation of words in mathematical expressions is an essential issue in natural language processing. In this study, data sets in different categories are classified as positive or negative according to their content. Using the Glove (Global Vector for Word Representation) method, which is one of the word embedding methods, the effect of the vector set based on the word similarities previously calculated on the classification performance has been analyzed. In this study, the effect of pretrained, embedded and deterministic word embedding classification performance has analyzed by using Long Short Term Memory (LSTM). The porposed LSTM based deep learning model has been tested on three different data sets and the results was evaluated.

Keywords

sentiment classification, word embedding, word weight, glove word embedding

References

• [1] H. Imaduddin, Widyawan and S. Fauziati, "Word Embedding Comparison for Indonesian Language Sentiment Analysis," 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), Yogyakarta, Indonesia, 2019, pp. 426-430, doi: 10.1109/ICAIIT.2019.8834536.
• [2] Eligüzel, N., Çetinkaya, C., & Dereli, T.V(2020). Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study. Advanced Engineering Informatics, 46, 101151. https://doi.org/10.1016/j.aei.2020.101151.
• [3] Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 97, 106754. https://doi.org/10.1016/j.asoc.2020.106754
• [4] Ayata, Deger & Saraclar, Murat & Ozgur, Arzucan. (2017). Turkish tweet sentiment analysis with word embedding and machine learning. 1-4. 10.1109/SIU.2017.7960195.
• [5] Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
• [6] Y. Sharma, G. Agrawal, P. Jain and T. Kumar, "Vector representation of words for sentiment analysis using GloVe," 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, 2017, pp. 279-284, doi: 10.1109/INTELCCT.2017.8324059.
• [7] R. Ni and H. Cao, "Sentiment Analysis based on GloVe and LSTM-GRU," 2020 39th Chinese Control Conference (CCC), Shenyang, China, 2020, pp. 7492-7497, doi: 10.23919/CCC50068.2020.9188578.
• [8] Li, Dan & Qian, Jiang. (2016). Text sentiment analysis based on long short-term memory. 471-475. 10.1109/CCI.2016.7778967.
• [9] Ali, F., Kwak, D., Khan, P., El-Sappagh, S., Ali, A., Ullah, S., Kim, K. H., & Kwak, K.-S. (2019). Transportation sentiment analysis using word embedding and ontology-based topic modeling. Knowledge-Based Systems, 174, 27–42. https://doi.org/10.1016/j.knosys.2019.02.033
• [10] Q. Wang, L. Sun and Z. Chen, "Sentiment Analysis of Reviews Based on Deep Learning Model," 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS), Beijing, China, 2019, pp. 258-261, doi: 10.1109/ICIS46139.2019.8940267.
• [11] M. F. Grawe, C. A. Martins and A. G. Bonfante, "Automated Patent Classification Using Word Embedding," 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Cancun, 2017, pp. 408-411, doi: 10.1109/ICMLA.2017.0-127.
• [12] A. A. A. Rafat, M. Salehin, F. R. Khan, S. A. Hossain and S. Abujar, "Vector Representation of Bengali Word Using Various Word Embedding Model," 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India, 2019, pp. 27-30, doi: 10.1109/SMART46866.2019.9117386.
• [13] A. A. A. Rafat, M. Salehin, F. R. Khan, S. A. Hossain and S. Abujar, "Vector Representation of Bengali Word Using Various Word Embedding Model," 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), Moradabad, India, 2019, pp. 27-30, doi: 10.1109/SMART46866.2019.9117386.
• [14] J. Wang and Z. Cao, "Chinese text sentiment analysis using LSTM network based on L2 and Nadam," 2017 IEEE 17th International Conference on Communication Technology (ICCT), Chengdu, China, 2017, pp. 1891-1895, doi: 10.1109/ICCT.2017.8359958.
• [15] Dimitrios Kotzias, Misha Denil, Nando de Freitas, and Padhraic Smyth. 2015. From Group to Individual Labels Using Deep Features. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). Association for Computing Machinery, New York, NY, USA, 597–606. DOI:https://doi.org/10.1145/2783258.2783380
• [16] Bhoir, Snehal & Ghorpade, Tushar & Mane, Vanita. (2017). Comparative analysis of different word embedding models. 1-4. 10.1109/ICAC3.2017.8318770.
• [17] N. (2020, October 19). An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/
• [18] K. Zhang, W. Song, L. Liu, X. Zhao and C. Du, "Bidirectional Long Short-Term Memory for Sentiment Analysis of Chinese Product Reviews," 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, 2019, pp. 1-4, doi: 10.1109/ICEIEC.2019.8784560.
• [19] Chen, Nan & Wang, Peikang. (2018). Advanced Combined LSTM-CNN Model for Twitter Sentiment Analysis. 684-687. 10.1109/CCIS.2018.8691381.