Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Backgrounds
Preprocessing Data
Evaluation and Results
Experiment Results
Conclusion
References
All Topics
Computer Science
Artificial Intelligence

Opinion mining using Double Channel CNN for Recommender System

Profile image of Minoo SayyadpourMinoo Sayyadpour

2023, arXiv (Cornell University)

https://doi.org/10.48550/ARXIV.2307.07798
visibility

…

description

6 pages

link

1 file

Abstract

Much unstructured data has been produced with the growth of the Internet and social media. A significant volume of textual data includes users' opinions about products in online stores and social media. By exploring and categorizing them, helpful information can be acquired, including customer satisfaction, user feedback about a particular event, predicting the sale of a specific product, and other similar cases. In this paper, we present an approach for sentiment analysis with a deep learning model and use it to recommend products. A twochannel convolutional neural network model has been used for opinion mining, which has five layers and extracts essential features from the data. We increased the number of comments by applying the SMOTE algorithm to the initial dataset and balanced the data. Then we proceed to cluster the aspects. We also assign a weight to each cluster using tensor decomposition algorithms that improve the recommender system's performance. Our proposed method has reached 91.6% accuracy, significantly improved compared to previous aspectbased approaches.

Related papers

Recommendation system exploiting aspect-based opinion mining with deep learning method
idris rabiu

Information Sciences, 2019

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

View PDFchevron_right
An effective sentiment classification by using machine learning algorithms and deep convolution neural network
V. Karthika, Shri Ramya Perumal

24TH TOPICAL CONFERENCE ON RADIO-FREQUENCY POWER IN PLASMAS, 2022

Abstract. Nowadays social media plays a significant role in all sorts of our activities ranging from analysing the attitude of a person for the job, getting opinions towards buying a product, acting as a forum for exchanging thoughts about the current events of various domains, creating awareness to the public about the natural calamities, educating the public about the fraudulent news spread by the fakers, initiating the young aspirant to protest against any societal issues, etc. Grasping the opinions shared by the experienced people towards a product, film, event, news, or politics like any subject of matter is one among the worth noting applications for a common man. It extends its application to making decisions about our day-to-day activities. The text reviews consist of enormous, sparse, non-uniform distribution of words represented as features. Text mining is the backend process for those applications. It includes techniques such as feature representation, sentiment classification, feature optimization, etc. Analysing the opinions suggested by the experienced people as positive and negative reviews is a challenging process and it is the baseline of our work. This paper contributes to the related processes involved in analysing the sentiments from the text reviews and accurately classifying them based on their polarity. In the proposed work, we particularly focus on feature representation techniques that have a major effect on enhancing the performance of sentiment classification. We explore different feature representation models such as TF-IDF vectorizer, word2vec vectorizer, and glove vectorizer as these word embedding models are interpreting the words and their syntactic and semantic relationships differently from the corpus. Also, we employ machine learning algorithms and a deep convolution neural network to perform comparative studies in classifying the sentiments. The word2vec in combination with Deep Convolution Neural Network provides the accuracy of 85.7%, precision of 84.4%, recall of 87%, and F- measure of 85.7% compared to other models.

View PDFchevron_right
A simplified classification computational model of opinion mining using deep learning
International Journal of Electrical and Computer Engineering (IJECE), Rajeshwari Dembala

International Journal of Electrical and Computer Engineering (IJECE), 2024

Opinion and attempts to develop an automated system to determine people's viewpoints towards various units such as events, topics, products, services, organizations, individuals, and issues. Opinion analysis from the natural text can be regarded as a text and sequence classification problem which poses high feature space due to the involvement of dynamic information that needs to be addressed precisely. This paper introduces effective modelling of human opinion analysis from social media data subjected to complex and dynamic content. Firstly, a customized preprocessing operation based on natural language processing mechanisms as an effective data treatment process towards building quality-aware input data. On the other hand, a suitable deep learning technique, bidirectional long short term-memory (Bi-LSTM), is implemented for the opinion classification, followed by a data modelling process where truncating and padding is performed manually to achieve better data generalization in the training phase. The design and development of the model are carried on the MATLAB tool. The performance analysis has shown that the proposed system offers a significant advantage in terms of classification accuracy and less training time due to a reduction in the feature space by the data treatment operation.

View PDFchevron_right
A Mixed approach of Deep Learning method and Rule-Based method to improve Aspect Level Sentiment Analysis
Paramita Ray

Applied Computing and Informatics, 2019

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag eac...

View PDFchevron_right
A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning
Wilson Ruggiero

IEEE Transactions on Industrial Informatics, 2018

Online social networks (OSN) provide relevant information on users' opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a Knowledge-Based Recommendation System (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a Convolutional Neural Network (CNN) and a Bi-directional Long Short-Term Memory (BLSTM)-Recurrent Neural Networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.

View PDFchevron_right
Application of Deep Learning to Sentiment Analysis for recommender system on cloud
Vankadara Saritha

2017 International Conference on Computer, Information and Telecommunication Systems (CITS), 2017

Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer' reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more timeconsuming. This study is aimed at designing a recommender system based on the explicit and implicit preferences of the customers in order to increase prediction's accuracy. In this study, we have combined sentiment analysis with the Collaborative Filtering (CF) based on deep learning for user groups in order to increase system accuracy. The proposed system uses Natural Language Processing (NLP) and supervised classification approach to analyze sentiments and extract implicit features. In order to design the recommender system, the Singular Value Decomposition (SVD) was used to improve scalability. The results show that our proposed method improves CF performance.

View PDFchevron_right
Mutual Information Approach For Sentiment Analysis Using Deep Machine Learning Convolution Neural Network (CNN) Model
International Journal of Scientific Research in Science, Engineering and Technology IJSRSET

International Journal of Scientific Research in Science, Engineering and Technology, 2020

Sentiment Analysis is a field of Natural Language Processing which addresses the problem of extracting sentiment or, more generally, opinion from text. Obtaining deeper insights on that topic can be very valuable for a range of fields such as finance, marketing, politics and business. Previous research has shown how sentiment and public opinion can affect stock markets, product sales, polls as well as public health. This thesis researches the message sentiment polarity classification problem in Twitter aiming to classify messages based on the polarity of the sentiment towards a specific topic, where the tweets and the topics are always given. The dataset analysed and the evaluation metrics considered are provided conference and the 4th task about "Sentiment Analysis in Twitter". This task includes five subtasks, two of which were eventually engaged in this research according to the implemented approach. First, subtask B is a binary classification task, where the goal is to classify messages into two classes, positive and negative regarding the sentiment towards the topic. Following, subtask C where the target is to classify messages in a five-scale sentiment polarity from highly negative, negative, neutral, positive to highly positive, based on the sentiment towards a given topic. We are implemented and experimented with two deep learning models for both subtasks; a Convolutional Neural Network (CNN) and a state-of-the-art Recurrent Neural Network with context attention CNN LSTM model. We compare both models to the baselines of the challenge and show that the CNN LSTM outperforms the other models, in both subtasks, with all evaluation measures but one.

View PDFchevron_right
SENTIMENT ANALYSIS BASED ON DEEP LEARNING
GJESR Journal

2018

Many emerging social sites, famous forums, review sites, and many bloggers generate huge amount of data in the form of user sentimental reviews, emotions, opinions, arguments, viewpoints etc. about different social events, products, brands, and politics, movies etc. Sentiments expressed by the users has great effect on readers, political images, online vendors. So the data present in scattered and unstructured manner needs to be managed properly and in this context sentiment analysis has got attention at very large level. Sentiment analysis can be defined as organization of the text which is used to understand the mindsets or feelings expressed in the form of different manners such as negative, positive, neutral, not satisfactory etc. This paper explains the implementation and accuracy of sentiment analysis using Tensor flow and python with any kind of text data. It works on embedding, LSTM and Sigmoid layers and finds the accuracy of data in iterative manner for better result.

View PDFchevron_right
Text mining based sentiment analysis using a novel deep learning approach
Alyaa Abdulhussein Al-Joda

International Journal of Nonlinear Analysis and Applications, 2021

Leveraging text mining for sentiment analysis, and integrating text mining and deep learning are the main purposes of this paper. The presented study includes three main steps. At the first step, pre-processing such as tokenization, text cleaning, stop word, stemming, and text normalization has been utilized. Secondly, feature from review and tweets using Bag of Words (BOW) method and Term Frequency $_$Inverse Document Frequency is extracted. Finally, deep learning by dense neural networks is used for classification. This research throws light on understanding the basic concepts of sentiment analysis and then showcases a model which performs deep learning for classification for a movie review and airline$_$ sentiment data set. The performance measure in terms of precision, recall, F1-measure and accuracy were calculated. Based on the results, the proposed method achieved an accuracy of $95.38%$ and $93.84%$ for a movie review and Airline$_$ sentiment, respectively.

View PDFchevron_right
Hai Ha Do, P.W.C. Prasad, Angelika Maag, Abeer Alsadoon, "Deep Learning for Aspect-Based Sentiment Analysis: A comparative Review", Expert Systems with Applications Journal. Volume 118, 15 March 2019, Pages 272-299. 2018. https://doi.org/10.1016/j.eswa.2018.10.003. Q1 Journal
A/Prof Abeer Alsadoon

The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering , as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

View PDFchevron_right

References (46)

  1. B. Rocca, "Introduction to recommender systems," [Online]. Available: https://towardsdatascience.com/introduction-to- recommender-systems-6c66cf15ada. [Accessed 3 June 2019].
  2. D. Aminu, S. Naomie, R. Idris, and O. Akram, "Weighted aspect-based opinion mining using deep learning for recommender system," Expert Systems With Applications, vol. 140, p. 112871, 2020.
  3. Nazarizadeh, A., Banirostam, T., & Sayyadpour, M. (2022). Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets. arXiv. https://doi.org/10.48550/ARXIV.2212.06041.
  4. A. Nazarizadeh, T. Banirostam and M. Sayyadpour, "Using Group Deep Learning and Data Augmentation in Persian Sentiment Analysis," 2022 8th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Behshahr, Iran, Islamic Republic of, 2022, pp. 1-5.
  5. Wu, Y., & Ester, M. (2015). FLAME: A probabilistic model combining aspect-based opinion mining and collaborative filtering. WSDM, 199- 208.
  6. Guang, Q., & Bing, L. (2009). Opinion word expansion and target extraction through double propagation. Computational Linguistics, 37 (1), 1-19.
  7. Jia, Y., Zhang, C., Lu, Q., & Wang, P. (2014). Users' brands preference based on SVD ++ in recommender systems. In Proceedings -2014 IEEE workshop on advanced research and technology in industry applications, WARTIA 2014 (pp. 1175-1178).
  8. Lafferty, J., Mccallum, A., Pereira, F. C. N., & Pereira, F. (2001). Conditional random fields. In Proceedings of the 18th international conference on machine learning 20 01 (ICML 20 01) (pp. 282-289).
  9. Irsoy, O., & Cardie, C. (2014). Opinion mining with deep recurrent neural networks. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 720-728).
  10. Liu, P., Joty, S., & Meng, H. (2015). Fine-grained opinion mining with recurrent neural networks and word embeddings. In Proceedings of the 2015 conference on empirical methods in natural language processing (EMNLP-2015) (pp. 1433-1443).
  11. Poria, S., Cambria, E., & Gelbukh, A. (2016). Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge- Based Systems, 108, 42-49.
  12. Xu, H., Liu, B., Shu, L., & Yu, P. S. (2018). Double embeddings and CNN-based sequence labeling for aspect extraction. In Proceedings of the 56th annual meeting of the association for computational linguistics (pp. 592-601).
  13. Devi, D. V. N. V. N., Kumar, C. K., & Prasad, S. (2016). A feature- based approach for sentiment analysis by using support vector machine. In 2016 IEEE 6th international conference on advanced computing (IACC) (pp. 3-8).
  14. Yoon, J., & Kim, H. (2017). Multi-Channel Lexicon Integrated CNN- BiLSTM Models for Sentiment Analysis. In The 2017 Conference on computational linguistics and speech processing (pp. 244-253).
  15. Titov, I., & McDonald, R. (2008). A joint model of text and aspect ratings for sentiment summarization. In Proceedings of ACL08 HLT: 51 (pp. 308-316).
  16. Wang, F., & Chen, L. (2015). Review mining for estimating users' ratings and weights for product aspects. Web Intelligence, 13 (3), 137- 152.
  17. Dau, Aminu & Salim, Naomie & Idris, Rabiu & Osman, Akram. (2019). Recommendation System Exploiting Aspect-based Opinion Mining with Deep Learning Method. Information Sciences.
  18. Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. J. Artif. Int. Res. 16, 1 (January 2002), 321-357.
  19. Jakob, N., & Ag, S. (2009). Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. TSA09, 57-64.
  20. McAuley, J., & Leskovec, J. (2013). Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on recommender systems (pp. 165-172).
  21. Ling, G., Lyu, M. R., & King, I. (2014). Ratings meet reviews, a combined approach to recommend. In Proceedings of the 8th ACM conference on recommender systems -RecSys '14 (pp. 105-112).
  22. Diao, Q., Qiu, M., Wu, C.-Y., Smola, A. J., Jiang, J., & Wang, C. (2014). Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining - KDD '14 (pp. 193-202).
  23. Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., & Ma, S. (2014). Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In Proceedings of the 37th international ACM SIGIR conference on research & development in information retrieval -SIGIR '14 (pp. 83-92).
  24. Zheng, L., Noroozi, V., & Yu, P. S. (2017). Joint deep modeling of users and items using reviews for recommendation. In WSDM 2017 ACM (pp. 1-10).
  25. Catherine, R., & Cohen, W. (2017). TransNets: Learning to transform for recommendation. In RecSys'17 (pp. 288-296).
  26. Lu, Y., Smyth, B., Dong, R., & Smyth, B. (2018). Coevolutionary recommendation model: mutual learning between ratings and reviews. In Proceedings of the 2018 world wide web conference on world wide web -WWW '18 (pp. 773-782).
  27. Jing, H., & Smola, A. J. (2017). Neural survival Recommender. In WSDM 2017, ACM (pp. 515-524).
  28. Da'u, A., & Salim, N. (2019). Sentiment-aware deep recommender system with neural attention networks. IEEE Access, 7, 45472-45484.
  29. Bai, B., Fan, Y., Tan, W., & Zhang, J. (2017). DLTSR: A deep learning framework for recommendation of long-tail web services. IEEE Transactions on Services Computing, 1374 (c), pp. 1-13.
  30. Yu, M., Quan, T., Peng, Q. et al. A model-based collaborate filtering algorithm based on stacked AutoEncoder. Neural Comput & Applic 34, 2503-2511 (2022).
  31. Yang, Y., Zhu, Y. & Li, Y. Personalized recommendation with knowledge graph via dual-autoencoder. Appl Intell 52, 6196-6207 (2022).
  32. Snyder, B., & Barzilay, R. (2007). Multiple aspect ranking using the good grief algorithm. In Proceedings of NAACL HLT (pp. 300-307).
  33. Diao, Q., Qiu, M., Wu, C.-Y., Smola, A. J., Jiang, J., & Wang, C. (2014). Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining - KDD '14 (pp. 193-202).
  34. Wang, H., Lu, Y., & Zhai, C. (2010). Latent aspect rating analysis on review text data: A rating regression approach. KDD'10, 1-10.
  35. Wang, H., Lu, Y., & Zhai, C. (2011). Latent aspect rating analysis without aspect keyword supervision. In Proceedings of the 17th ACM SIGKDD International conference on knowledge discovery and data mining -KDD '11: 138 (p. 618).
  36. Bauman, K., Liu, B., & Tuzhilin, A. (2017). Aspect-based recommendations: Recommending items with the most valuable aspects based on user reviews. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining -KDD '17 (pp. 717-725).
  37. Xu, H., Liu, B., Shu, L., & Yu, P. S. (2018). Double embeddings and CNN-based sequence labeling for aspect extraction. In Proceedings of the 56th annual meeting of the association for computational linguistics (pp. 592-601).
  38. Safar, S., Jose, B.R., Santhanakrishnan, T. (2022). An Improved Recommendation System with Aspect-Based Sentiment Analysis. In: Mathew, J., Santhosh Kumar, G., P., D., Jose, J.M. (eds) Responsible Data Science. Lecture Notes in Electrical Engineering, vol 940. Springer, Singapore.
  39. Yang, C., Yu, X., Liu, Y., Nie, Y., & Wang, Y. (2016). Collaborative filtering with weighted opinion aspects. Neurocomputing, 210, 185- 196.
  40. Hajba, G.L. (2018). Using Beautiful Soup. In: Website Scraping with Python. Apress, Berkeley, CA.
  41. ML | Handling Imbalanced Data with SMOTE and Near Miss Algorithm in Python, "https://www.geeksforgeeks.org/ml-handling- imbalanced-data-with-smote-and-near-miss-algorithm-in-python", 2022.
  42. Mikolov, T., Yih, W., & Zweig, G. (2013). Linguistic regularities in continuous space word representations. In Proceedings of NAACL- HLT (pp. 746-751).
  43. Jebbara, S., & Cimiano, P. (2016). Aspect-based relational sentiment analysis using a stacked neural network architecture. In On artificial intelligence, 29 August-2 (pp. 1-9).
  44. McAuley, J., & Leskovec, J. (2013). Hidden factors and hidden topics: understanding rating dimensions with review text. In Proceedings of the 7th ACM conference on recommender systems (pp. 165-172).
  45. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42 (8), 30-37.
  46. Tan, Y., Zhang, M., Liu, Y., & Ma, S. (2016). Rating-boosted latent topics: Understanding users and items with ratings and reviews. In IJCAI international joint conference on artificial intelligence, 2016 - January (pp. 2640-2646).

Related papers

Weighted aspect-based opinion mining using deep learning for recommender system
idris rabiu

Expert Systems with Applications, 2019

The main goal of Aspect-Based Opinion Mining is to extract product's aspects and the associated user opinions from the user text review. Although this serves as vital source information for enhancing rating prediction performance, few studies have attempted to fully utilize it for better accuracy of recommendation systems. Most of these studies typically assign equal weights to all aspects in the opinion mining process, however, in practices; users tend to give different priority on different aspects of the product when reaching overall ratings. In addition, most of the existing methods typically rely on handcrafted, rule-based or double propagation methods in the opinion mining process which are known to be time-consuming and often inclined to errors. This could affect the reliability and performance of the recommender systems (RS). Therefore, in this paper, we propose a weighted Aspect-based Opinion mining using Deep learning method for Recommender system (AODR) that can extract product's aspects and the underlying weighted user opinions from the review text using a deep learning method and then fuse them into extended collaborative filtering (CF) technique for improving the RS. The proposed method is basically comprised of two components: (1) Aspect-based opinion mining module which aims to extract the product aspects from the review text to generate aspect rating matrix. (2) Recommendation generation component that uses tensor factorization (TF) technique to compute weighted aspect ratings and finally infer the overall rating prediction. We evaluate the proposed model in terms of both aspect extraction and recommendation performance. Experiment results on different datasets show that our AODR model achieves better results compared to the baselines. .

View PDFchevron_right
A Survey on Deep Learning Based Recommender System Using Sentiment Analysis
persis ivy

2018

Sentiment analysis is a technique for identification of expression, mind-set or feelings of users an classify as negative, positive, favorable, unfavorable, etc from a piece of text in the document through social network sites, blogs, forums etc about different books, movies, music, products, news. Sentiments of users about the various factors have a greater bang on the vendors and personnels. The WWW is growing exponentially in terms of size and complexity. Users require assistance in judgment of items that are related with their interest from the massive data available. A solution to simplify the issue is building recommender system that classifies data and facilitates users decision making ability. Recommender systems are used to recommend an item to a user after analyzing about the interest of the user and characteristics of the item. The sentiment analysis can be made using deep learning techniques because of its effectiveness in automatic learning. The objective of the paper i...

View PDFchevron_right
Sentiment Analysis for Recommendation System and Business Intelligence using Deep Learning
Aishwarya Bhagwat

2021

Sentiment analysis (SA) is the study of analyzing the sentiments, impressions by people about entities, persons, topics and services. Sentimental analysis uses text analysis techniques from big data analytics. Nowadays most of the people are active on social media. They usually show their sentiments through different websites or platforms. These sentiments are most important for business streams. In today’s world, sentimental analysis is becoming important for identifying hidden information in unstructured data formats that arise in the usage of various platforms for business sides. Deep learning plays an important for this business intelligence. The sentiment analysis by deep learning uses popular algorithms. So, in this document we will see how deep learning is important for recommendation system and business intelligence using sentimental

View PDFchevron_right
Customer Reviews’ Sentiments Analysis using Deep Learning
Supriya Panda

International Journal of Computer Applications, 2020

In this era of digitalization, Sentiment Analysis(SA) has become a necessity for progress and prosperity in marketing. Sentiment analysis has become a powerful way of knowing the opinions and thoughts of users. The viewpoint of the consumer, such as knowledge sharing would include a lot of useful experience, while one wrong idea will cost too much for the company.SA has many social media data-related problems, such as natural language interpretation, etc. Issue of theory and technique also affect the accuracy of detecting the polarity. There is a problem of text classification such as analysis of sentiments in document level, sentence level, feature based. Document level analysis is done by two approaches: supervised learning and unsupervised learning. Sentence level contains sentences containing opinions. Aspect based analysis have different attributes is performed in customer reviews. Product opinion is taken for knowing the sentiments. Opinions are compared and are extracted as a feature.SA is very important for business purpose because it gives the way for improving their operations and the products they offer. For improving business strategy it plays an important role. It provides many key benefits like impactful decisions, for finding relevant products, improving business strategy, beating competitions, tackling positive or negative issues affecting the product. To overcome such issues, deep learning processes are applied. The work focuses on two main tasks. Firstly, to extract sentiments present in data of social media of customers' reviews and secondly, to use the deeplearning process for the sentiments' extraction for customer reviews.

View PDFchevron_right
Toward a multitask aspect-based sentiment analysis model using deep learning
Michel RIVEILL

IAES International Journal of Artificial Intelligence (IJ-AI)

Sentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the ...

View PDFchevron_right

Related topics

  • Computer Science
  • Artificial Intelligence
  • Sentiment Analysis
  • Recommender System
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved