A LIGHTWEIGHT LSTM FRAMEWORK FOR CONTEXTUAL SENTIMENT CLASSIFICATION
DOI:
https://doi.org/10.29121/granthaalayah.v12.i6.2024.6095Keywords:
Lightweight, Lstm Framework, Sentiment Analysis, Vital Tool, Industrial ApplicationsAbstract [English]
With the rapid increase in opinion-rich content shared across the internet, text sentiment analysis has emerged as a vital tool in both academic research and industrial applications. Sentiment analysis typically involves classifying a piece of text as expressing positive, negative, or neutral emotion. Traditional approaches to text classification often require extensive feature engineering and rely heavily on tokenization and embedding techniques, making them resource-intensive and less adaptive to context. To address these limitations, Long Short-Term Memory (LSTM) networks—an advanced form of Recurrent Neural Networks (RNNs)—have been adopted for their ability to capture long-range dependencies in textual data. This study proposes a sentiment classification model based solely on LSTM architecture to analyze short texts and effectively extract context-aware sentiment patterns. Unlike conventional models, LSTM-based frameworks can learn temporal word relationships without explicit syntactic parsing or handcrafted features. By leveraging the memory capabilities of LSTM, the proposed model enhances sentiment categorization accuracy while maintaining a relatively lightweight computational profile. Experimental evaluations demonstrate the effectiveness of LSTM in capturing contextual semantics, making it a suitable choice for real-time sentiment detection tasks in dynamic and user-generated content environments.
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Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New Avenues in Opinion Mining and Sentiment Analysis. IEEE Intelligent Systems, 28(2), 15–21. https://doi.org/10.1109/MIS.2013.30 DOI: https://doi.org/10.1109/MIS.2013.30
Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 56(4), 82–89. https://doi.org/10.1145/2436256.2436274 DOI: https://doi.org/10.1145/2436256.2436274
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735 DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Jurafsky, D., & Martin, J. H. (2021). Speech and Language Processing (3rd ed.). Stanford University.
Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://doi.org/10.3115/v1/D14-1181 DOI: https://doi.org/10.3115/v1/D14-1181
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9 DOI: https://doi.org/10.1007/978-3-031-02145-9_1
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction To Information Retrieval. Cambridge University Press. https://doi.org/10.1017/CBO9780511809071 DOI: https://doi.org/10.1017/CBO9780511809071
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.cfdsfsdzfjsjsjsjkksks
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/9781601981516 DOI: https://doi.org/10.1561/1500000011
Salton, G., & Buckley, C. (1988). Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management, 24(5), 513–523. https://doi.org/10.1016/0306-4573(88)90021-0 DOI: https://doi.org/10.1016/0306-4573(88)90021-0
Zhang, Y., & Wallace, B. C. (2015). A Sensitivity Analysis of (and Practitioners' Guide To) Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1510.03820.
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