Sentiments Analysis and Text Summarization of Medicine Reviews Using Deep Sequential Iterative Model with Attention Encoder
DOI:
https://doi.org/10.13052/jmm1550-4646.18415Keywords:
polarity, encoder,decoder, attention model ,summarizationAbstract
In this day and age of data innovation, vast amounts of data are being generated daily, much of it is useless to the general public unless it is correctly handled. This year has seen a huge increase in social networks’ relevance, resulting in vast quantities of data provided by users. This vast amount of information is gathered from a variety of sources, including company websites, customer blogs, and item reviews. The text outline is probably the most significant point of view in daily life. Analysis of text sentiment is a method for assessing, summarising, and drawing conclusions about the most important content. In the field of sentiment analysis, attention methods have been crucial since they make use of sentiment lexicons to gather a huge amount of sentiment polarity information. With an attention mechanism that acts as a link between linguistic information with a strong emotional component and deep learning algorithms, it may be possible to boost text sentiment significantly. In the LSTM model, word sequence addictions can be captured over the long term. For the first time, scientists have developed an attention model that combines LSTM with an incredibly deep RNN model to tackle the problem of sentiment analysis in the real world. The iterative method trains the first set of word embeddings using a Word to Vector technique. With the Word2Vec algorithm, text strings are converted into a numerical value vector and distance between words and comparative words based on meaning are calculated. Using the attention strategy has the added benefit of potentially enhancing machine learning’s ability to learn sentiment representations. The attention model is more scalable and adaptable than previous approaches. The major objective of this work is to assess feelings and develop an abstracted content outline, decide on the semantic summarization of different materials and effectively process the data.
Downloads
References
Wang, Tianshi, et al. “A multi-label text classification method via dynamic semantic representation model and deep neural network.” Applied Intelligence 50.8 (2020): 2339–2351.
Ji, Zhong, et al. “Video summarization with attention-based encoder–decoder networks.” IEEE Transactions on Circuits and Systems for Video Technology 30.6 (2019): 1709–1717.
Abdi, Asad, et al. “Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion.” Information Processing & Management 56.4 (2019): 1245–1259.
Samek, Wojciech, and Klaus-Robert Müller. “Towards explainable artificial intelligence.” Explainable AI: interpreting, explaining and visualizing deep learning. Springer, Cham, 2019. 5–22.
Anbazhagu, U. V., and R. Anandan. “Emotional interpretation using chaotic cuckoo public sentiment variations on textual data from Twitter.” International Journal of Speech Technology (2021): 1–10.
Shiva Prasad, K. M., and T. Hanumantha Reddy. “Bidirectional Encoding Contextual Approach for Identification of Relevant Document in Corpus.” Journal of Information & Knowledge Management (2021): 2150014.
Khattak, Faiza Khan, et al. “A survey of word embeddings for clinical text.” Journal of Biomedical Informatics: X 4 (2019): 100057.
Van Lierde, Hadrien, and Tommy WS Chow. “Query-oriented text summarization based on hypergraph transversals.” Information Processing & Management 56.4 (2019): 1317–1338.
Salminen, Joni, et al. “Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type.” Journal of Business Research 101 (2019): 203–217.
De la Torre-Díez, Isabel, Francisco Javier Díaz-Pernas, and Míriam Antón-Rodríguez. “A content analysis of chronic diseases social groups on Facebook and Twitter.” Telemedicine and e-Medicine 18.6 (2012): 404–408.
Rath, Mamata. “Intelligent Information System for Academic Institutions: Using Big Data Analytic Approach.” Interdisciplinary Approaches to Information Systems and Software Engineering. IGI Global, 2019. 207–232.
Gupta, Garima, and Rahul Katarya. “Research on Understanding the Effect of Deep Learning on User Preferences.” Arabian Journal for Science and Engineering (2020): 1–40.
Patel, Harshita, and B. Manjula Josephine. “Sentimental Analysis in Various Business Applications.” Sentiment Analysis and Knowledge Discovery in Contemporary Business. IGI Global, 2019. 31–43.
Abualigah, Laith, et al. “Text summarization: a brief review.” Recent Advances in NLP: The Case of Arabic Language (2020): 1–15.
Chen, Tao, et al. “Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN.” Expert Systems with Applications 72 (2017): 221–230.
Zhang, Chengwei, et al. “Deep Learning for Design in Concept Clustering.” ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers Digital Collection, 2017.
Gupta, Gitanjali, and Kamlesh Lakhwani. “Big Data Classification Techniques: A Systematic Literature Review.” Journal of Natural Remedies 21.2 (2020): 1–1.
Jin, Mingmin, et al. “Combining deep learning and topic modeling for review understanding in context-aware recommendation.” Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
Zhang, Ji, et al. “Relational intelligence recognition in online social networks—A survey.” Computer Science Review 35 (2020): 100221.
Abid, Fazeel, et al. “Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter.” Future Generation Computer Systems 95 (2019): 292–308.
Rhanoui, Maryem, et al. “A CNN-BiLSTM model for document-level sentiment analysis.” Machine Learning and Knowledge Extraction 1.3 (2019): 832–847.
Krohn, Jon, Grant Beyleveld, and Aglaé Bassens. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence. Addison-Wesley Professional, 2019.
Nadeau, Stephen E. “Neural population dynamics and cognitive function.” Frontiers in human neuroscience 14 (2020): 50.
Yoon, Seunghyun, et al. “Detecting incongruity between news headline and body text via a deep hierarchical encoder.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019.
Baddur, Rajalaxmi P., and Seema Shedole. “A Novel Approach for Sentiment Analysis Using Deep Recurrent Networks and Sequence Modeling.” Recent Patents on Engineering 14.3 (2020): 403–411.
El-Affendi, Mohammed A., Khawla Alrajhi, and Amir Hussain. “A novel deep learning-based multilevel parallel attention neural (MPAN) model for multidomain arabic sentiment analysis.” IEEE Access 9 (2021): 7508–7518.
