Acta Informatica Pragensia 2023, 12(2), 225-242 | DOI: 10.18267/j.aip.2065031

Emotion-Based Sentiment Analysis Using Conv-BiLSTM with Frog Leap Algorithms

Sandeep Yelisetti ORCID..., Nellore Geethanjali ORCID...
Department of Computer Science Technology, Sri Krishnadevaraya University, Ananthapuram, India

Social media, blogs, review sites and forums can produce large volumes of data in the form of users’ emotions, views, arguments and opinions about various political events, brands, products and social problems. The user's sentiment expressed on the web influences readers, politicians and product vendors. These unstructured social media data are analysed to form structured data, and for this reason sentiment analysis has recently received the most important research attention. Sentiment analysis is a process of classifying the user’s feelings in different manners such as positive, negative or both. The major issue of sentiment analysis is insufficient data processing and outcome prediction. For this, deep learning-based approaches are effective due to their autonomous learning ability. Emotion identification from the text in natural language processing (NLP) provides more benefits in the field of e-commerce and business environments. In this paper, emotion detection-based text classification is used for sentiment analysis. The data collected are pre-processed using tokenization, stop word discarding, stemming and lemmatization. After performing data pre-processing, the features are identified using term frequency and inverse document frequency (TF-IDF). Then the filtered features are turned into word embeddings by documents as a vector (Doc2Vec). Then, for text classification, a deep learning (DL) based model called convolutional bidirectional long short-term memory (CBLSTM) is used to differentiate the sentiments of human expression into positive or good and negative or bad emotions. The neural network hyper-parameters are optimized with a meta-heuristic algorithm called the frog leap approach (FLA). The proposed CBLSTM with FLA uses four review and Twitter datasets. The experimental results of this study are compared with the conventional approaches LSTM-RNN and LSTM-CNN to prove the efficiency of the proposed model. Compared to LSTM-RNN and LSTM-CNN, the proposed model secures an improved average accuracy of 98.1% for review datasets and 97.5% for Twitter datasets.

Keywords: Emotion; Sentiment analysis; Deep learning; Convolution neural network; LSTM; Frog leap algorithm; Meta-heuristic; TF-IDF.

Received: November 5, 2022; Revised: January 6, 2023; Accepted: January 9, 2023; Prepublished online: January 17, 2023; Published: October 10, 2023  Show citation

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Yelisetti, S., & Geethanjali, N. (2023). Emotion-Based Sentiment Analysis Using Conv-BiLSTM with Frog Leap Algorithms. Acta Informatica Pragensia, 12(2), 225-242. doi: 10.18267/j.aip.206
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    References

    1. Al-Deen, H. S. S., Zeng, Z., Al-Sabri, R., & Hekmat, A. (2021). An Improved Model for Analyzing Textual Sentiment Based on a Deep Neural Network Using Multi-Head Attention Mechanism. Applied System Innovation, 4(4), Article no. 85. https://doi.org/10.3390/asi4040085 Go to original source...
    2. Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. (2021). ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis. Future Generation Computer Systems, 115, 279-294. https://doi.org/10.1016/j.future.2020.08.005 Go to original source...
    3. Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge Based Systems, 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134 Go to original source...
    4. Damarta, R., Hidayat, A. M., & Abdullah, A. G. (2021). The application of k-nearest neighbors classifier for sentiment analysis of PT PLN (Persero) twitter account service quality. Journal of Physics, 1722(1), 012002. https://doi.org/10.1088/1742-6596/1722/1/012002 Go to original source...
    5. Dang, N. C., Moreno, M. N., & De La Prieta, F. (2020). Sentiment Analysis Based on Deep Learning: A Comparative Study. Electronics, 9(3), 483. https://doi.org/10.3390/electronics9030483 Go to original source...
    6. Dashtipour, K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2020). A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks. Neurocomputing, 380, 1-10. https://doi.org/10.1016/j.neucom.2019.10.009 Go to original source...
    7. Datta, S., & Chakrabarti, S. (2021). Aspect based sentiment analysis for demonetization tweets by optimized recurrent neural network using fire fly-oriented multi-verse optimizer. Sadhana - Academy Proceedings in Engineering Sciences, 46(2), Article 79. https://doi.org/10.1007/s12046-021-01608-1 Go to original source...
    8. Da'u, A., Salim, N., Rabiu, I., & Osman, A. (2020). Recommendation system exploiting aspect-based opinion mining with deep learning method. Information Sciences, 512, 1279-1292. https://doi.org/10.1016/j.ins.2019.10.038 Go to original source...
    9. De Haro-García, A., García, G. C., & García-Pedrajas, N. (2020). Ensembles of feature selectors for dealing with class-imbalanced datasets: A proposal and comparative study. Information Sciences, 540, 89-116. https://doi.org/10.1016/j.ins.2020.05.077 Go to original source...
    10. Dessì, D., Dragoni, M., Fenu, G., Marras, M., & Recupero, D. R. (2020). Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews. In Algorithms for intelligent systems (pp. 57-83). Springer Nature. https://doi.org/10.1007/978-981-15-1216-2_3 Go to original source...
    11. Eusuff, M., & Lansey, K. (2003). Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management, 129, 210-225. https://doi.org/10.1061/(ASCE)0733-9496(2003)129:3(210) Go to original source...
    12. Hameed, Z., & Garcia-Zapirain, B. (2020). Sentiment Classification Using a Single-Layered BiLSTM Model. IEEE Access, 8, 73992-74001. https://doi.org/10.1109/access.2020.2988550 Go to original source...
    13. Imran, M., Daudpota, S. M., Kastrati, Z., & Batra, R. (2020). Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets. IEEE Access, 8, 181074-181090. https://doi.org/10.1109/access.2020.3027350 Go to original source...
    14. Jadidoleslam, M., Bijami, E., Amiri, N., Ebrahimi, A., & Askari, J. (2012). Application of Shuffled Frog Leaping Algorithm to Long Term Generation Expansion Planning. International Journal of Computer and Electrical Engineering, 4(2), 115-120. https://doi.org/10.7763/ijcee.2012.v4.461 Go to original source...
    15. Jain, D., Kumar, A., & Garg, G. (2020). Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN. Applied Soft Computing, 91, 106198. https://doi.org/10.1016/j.asoc.2020.106198 Go to original source...
    16. Karakoyun, M., & Babalik, A. (2015). Data Clustering with Shuffled Leaping Frog Algorithm (SFLA) for Classification. In International Conference on Intelligent Computing, Electronics Systems and Information Technology. https://doi.org/10.15242/iae.iae0815009 Go to original source...
    17. Kastrati, Z., Dalipi, F., Imran, M., Nuci, K. P., & Wani, M. A. (2021). Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study. Applied Sciences, 11(9), Article 3986. https://doi.org/10.3390/app11093986 Go to original source...
    18. Ligthart, A., Rodriguez, D., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial Intelligence Review, 54(7), 4997-5053. https://doi.org/10.1007/s10462-021-09973-3 Go to original source...
    19. Meng, W., Wei, Y., Liu, P., Zhu, Z., & Yin, H. (2019). Aspect Based Sentiment Analysis With Feature Enhanced Attention CNN-BiLSTM. IEEE Access, 7, 167240-167249. https://doi.org/10.1109/access.2019.2952888 Go to original source...
    20. Pimpalkar, A., & Raj, J. R. (2022). MBiLSTMGloVe: Embedding GloVe knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis. Expert Systems With Applications, 203, 117581. https://doi.org/10.1016/j.eswa.2022.117581 Go to original source...
    21. Ren, F., Feng, L., Xiao, D., Cai, M., & Cheng, S. (2020). DNet: A lightweight and efficient model for aspect based sentiment analysis. Expert Systems With Applications, 151, 113393. https://doi.org/10.1016/j.eswa.2020.113393 Go to original source...
    22. Safa, R., Bayat, P., & Moghtader, L. (2021). Automatic detection of depression symptoms in twitter using multimodal analysis. The Journal of Supercomputing, 78(4), 4709-4744. https://doi.org/10.1007/s11227-021-04040-8 Go to original source...
    23. Shams, M., Khoshavi, N., & Baraani-Dastjerdi, A. (2020). LISA: Language-Independent Method for Aspect-Based Sentiment Analysis. IEEE Access, 8, 31034-31044. https://doi.org/10.1109/access.2020.2973587 Go to original source...
    24. Shilpa, P., Shereen, R., Jacob, S., & Vinod, P. (2021). Sentiment Analysis Using Deep Learning. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE. https://doi.org/10.1109/icicv50876.2021.9388382 Go to original source...
    25. Singh, J., Singh, G., & Singh, R. (2016). A review of sentiment analysis techniques for opinionated web text. CSI Transactions on ICT, 4(2-4), 241-247. https://doi.org/10.1007/s40012-016-0107-y Go to original source...
    26. Sitaula, C., Basnet, A., Mainali, A., & Shahi, T. B. (2021). Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets. Computational Intelligence and Neuroscience, 2021, Article ID 2158184. https://doi.org/10.1155/2021/2158184 Go to original source...
    27. Su, M., Wu, C., Huang, K., & Hong, Q. (2018). LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors. In 2018 First Asian Conference on Affective Computing and Intelligent Interaction. IEEE. https://doi.org/10.1109/aciiasia.2018.8470378 Go to original source...
    28. Wei, H., Sankaranarayanan, J., & Samet, H. (2020). Enhancing local live tweet stream to detect news. Geoinformatica, 24(2), 411-441. https://doi.org/10.1007/s10707-019-00392-9 Go to original source...
    29. Yadav, N., Kudale, O., Gupta, S., Rao, A., & Shitole, A. (2020). Twitter Sentiment Analysis Using Machine Learning For Product Evaluation. In 2020 IEEE International Conference on Inventive Computation Technologies (pp. 181-185). IEEE. https://doi.org/10.1109/icict48043.2020.9112381 Go to original source...

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