An Aspect-Level Sentiment Analysis Based on LDA Topic Modeling
Subject Areas : Natural Language ProcessingSina Dami 1 * , Ramin Alimardani 2
1 - West Tehran Branch, Islamic Azad University
2 - West Tehran Branch, Islamic Azad University
Keywords: Natural Language Processing, Sentiment Analysis, Aspect-Level, Topic Modeling, LDA,
Abstract :
Sentiment analysis is a process through which the beliefs, sentiments, allusions, behaviors, and tendencies in a written language are analyzed using Natural Language Processing (NLP) techniques. This process essentially comprises of discovering and understanding people's positive or negative sentiments regarding a product or entity in the text. The increased significance of sentiments analysis has coincided with the growth in social media such as surveys, blogs, Twitter, etc. The present study takes advantage of the topic modeling approach based on latent Dirichlet allocation (LDA) to extract and represent the thematic features as well as a support vector machine (SVM) to classify and analyze sentiments at the aspect level. LDA seeks to extract latent topics by observing all the texts, which is accomplished by assigning the probability of each word being attributed to each topic. The important features that represent the thematic aspect of the text are extracted and fed to a support vector machine for classification through this approach. SVM is an extremely powerful classification algorithm that provides the possibility to separate complex data from one another accurately by mapping the data to a space with much larger aspects and creating an optimal hyperplane. Empirical data on real datasets indicate that the proposed model is promising and performs better compared to the baseline methods in terms of precision (with 89.78% on average), recall (with 78.92% on average), and F-measure (with 83.50% on average).
Luo F, Li C, Cao Z. Affective-feature-based sentiment analysis using SVM classifier. In2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) 2016 May 4 (pp. 276-281). IEEE.
Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. InThirty-second AAAI conference on artificial intelligence 2018 Apr 26.
Dami S. News Events Prediction Based on Casual Inference in First-Order Logic (FOL). Journal of Soft Computing and Information Technology. 2016 Dec 21;5(4):11-25.
Dami S, Barforoush AA, Shirazi H. News events prediction using Markov logic networks. Journal of Information Science. 2018 Feb;44(1):91-109.
Rezaei A, Dami S, Daneshjoo P. Multi-document extractive text summarization via deep learning approach. In2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI) 2019 (pp. 680-685). IEEE.
Dami S, Yahaghizadeh M. Efficient event prediction in an IOT environment based on LDA model and support vector machine. In2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) 2018 Feb (pp. 135-138). IEEE.
Emami H, Dami S, Shirazi H. K-Harmonic Means Data Clustering With Imperialist Competitive Algorithm. University Politehnica of Bucharest-Scientific Bulletin, Series C: Electrical Engineering and Computer Science. 2015 Feb;77(7).
García-Pablos A, Cuadros M, Rigau G. W2VLDA: almost unsupervised system for aspect based sentiment analysis. Expert Systems with Applications. 2018 Jan 1;91:127-37.
Goswami S, Nandi S, Chatterjee S. Sentiment analysis based potential customer base identification in social media. InContemporary Advances in Innovative and Applicable Information Technology 2019 (pp. 237-243). Springer, Singapore.
Araque O, Corcuera-Platas I, Sánchez-Rada JF, Iglesias CA. Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Systems with Applications. 2017 Jul 1;77:236-46.
Zhou Q, Xu Z, Yen NY. User sentiment analysis based on social network information and its application in consumer reconstruction intention. Computers in Human Behavior. 2019 Nov 1;100:177-83.
Moraes R, Valiati JF, Neto WP. Document-level sentiment classification: An empirical comparison between SVM and ANN. Expert Systems with Applications. 2013 Feb 1;40(2):621-33.
Shirsat VS, Jagdale RS, Deshmukh SN. Sentence level sentiment identification and calculation from news articles using machine learning techniques. InComputing, Communication and Signal Processing 2019 (pp. 371-376). Springer, Singapore.
Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B. Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of computational science. 2018 Jul 1;27:386-93.
Ozyurt B, Akcayol MA. A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA. Expert Systems with Applications. 2021 Apr 15;168:114231.
Parveen N, Santhi MV, Burra LR, Pellakuri V, Pellakuri H. Women’s e-commerce clothing sentiment analysis by probabilistic model LDA using R-SPARK. Materials Today: Proceedings. 2021 Jan 6.
Xie R, Chu SK, Chiu DK, Wang Y. Exploring public response to COVID-19 on Weibo with LDA topic modeling and sentiment analysis. Data and Information Management. 2021;5(1):86-99.
Ali T, Marc B, Omar B, Soulaimane K, Larbi S. Exploring destination's negative e-reputation using aspect based sentiment analysis approach: Case of Marrakech destination on TripAdvisor. Tourism Management Perspectives. 2021 Oct 1;40:100892.
AlGhamdi N, Khatoon S, Alshamari M. Multi-aspect oriented sentiment classification: Prior knowledge topic modelling and ensemble learning classifier approach. Applied Sciences. 2022 Apr 18;12(8):4066.
Venugopalan M, Gupta D. An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysis. Knowledge-based systems. 2022 Jun 21;246:108668.
Chen P, Sun Z, Bing L, Yang W. Recurrent attention network on memory for aspect sentiment analysis. InProceedings of the 2017 conference on empirical methods in natural language processing 2017 Sep (pp. 452-461).
Dou ZY. Capturing user and product information for document level sentiment analysis with deep memory network. InProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing 2017 Sep (pp. 521-526).
Mahadevaswamy UB, Swathi P. Sentiment analysis using bidirectional LSTM network. Procedia Computer Science. 2023 Jan 1;218:45-56.
Edara DC, Vanukuri LP, Sistla V, Kolli VK. Sentiment analysis and text categorization of cancer medical records with LSTM. Journal of Ambient Intelligence and Humanized Computing. 2023 May;14(5):5309-25.
Iparraguirre-Villanueva O, Alvarez-Risco A, Herrera Salazar JL, Beltozar-Clemente S, Zapata-Paulini J, Yáñez JA, Cabanillas-Carbonell M. The public health contribution of sentiment analysis of Monkeypox tweets to detect polarities using the CNN-LSTM model. Vaccines. 2023 Jan 31;11(2):312.
Mohbey KK, Meena G, Kumar S, Lokesh K. A CNN-LSTM-Based Hybrid Deep Learning Approach for Sentiment Analysis on Monkeypox Tweets. New Generation Computing. 2023 Aug 14:1-9.
Xu J, Chen D, Qiu X, Huang X. Cached long short-term memory neural networks for document-level sentiment classification. arXiv preprint arXiv:1610.04989. 2016 Oct 17.
Wang J, Yu LC, Lai KR, Zhang X. Dimensional sentiment analysis using a regional CNN-LSTM model. InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) 2016 Aug (pp. 225-230).
Wang J, Yu LC, Lai KR, Zhang X. Tree-structured regional CNN-LSTM model for dimensional sentiment analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2019 Dec 11;28:581-91.
Amin J, Sharif M, Raza M, Saba T, Sial R, Shad SA. Brain tumor detection: A long short-term memory (LSTM)-based learning model. Neural Computing and Applications. 2020 Oct;32(20):15965-73.
Dami S, Yahaghizadeh M. Predicting cardiovascular events with deep learning approach in the context of the internet of things. Neural Computing and Applications. 2021 Jan 3:1-8.
Dami S, Esterabi M. Predicting stock returns of Tehran exchange using LSTM neural network and feature engineering technique. Multimedia Tools and Applications. 2021 May;80(13):19947-70.
Dami S. Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic. World Journal of Clinical Cases. 2022 Sep 9;10(26):9207.
Aurangzeb K, Ayub N, Alhussein M. Aspect Based Multi-Labeling Using SVM Based Ensembler. IEEE Access. 2021 Feb 1;9:26026-40.
Yang Y, Jiang J. Adaptive bi-weighting toward automatic initialization and model selection for HMM-based hybrid meta-clustering ensembles. IEEE transactions on cybernetics. 2018 Mar 27;49(5):1657-68.
Liao S, Wang J, Yu R, Sato K, Cheng Z. CNN for situations understanding based on sentiment analysis of twitter data. Procedia computer science. 2017 Jan 1;111:376-81.
Kumar V, Pujari AK, Padmanabhan V, Kagita VR. Group preserving label embedding for multi-label classification. Pattern Recognition. 2019 Jun 1;90:23-34.
Wu G, Zheng R, Tian Y, Liu D. Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification. Neural Networks. 2020 Feb 1;122:24-39.
Ni J, Li J, McAuley J. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. InProceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) 2019 Nov (pp. 188-197).
Wan M, Ni J, Misra R, McAuley J. Addressing marketing bias in product recommendations. InProceedings of the 13th international conference on web search and data mining 2020 Jan 20 (pp. 618-626).
Jahanbakhsh Gudakahriz S, Eftekhari Moghaddam AM, Mahmoudi F. An Experimental Study on Performance of Text Representation Models for Sentiment Analysis. Journal of Information Systems and Telecommunication (JIST). 2020 Jul;1(29):45.
Chandra N, Ahuja L, Khatri SK, Monga H. Utilizing Gated Recurrent Units to Retain Long Term Dependencies with Recurrent Neural Network in Text Classification. Journal of Information Systems and Telecommunication (JIST). 2021 May;2(34):89.
Xiong H, Yan H, Zeng Z, Wang B. Dependency Parsing and Bidirectional LSTM-CRF for Aspect-level Sentiment Analysis of Chinese. InJIST (Workshops & Posters) 2018 (pp. 90-93)