Journal of Basic and Applied Research International

Enhancing Sentiment Analysis with Word2Vec and LSTM: A Comparative Study

Journal of Basic and Applied Research International, Volume 29, Issue 3, Page 1-10
DOI: 10.56557/jobari/2023/v29i38342

Abstract


Sentiment analysis is an important natural language processing task that helps people understand the emotional information conveyed in texts. This paper aims to propose a sentiment classification model based on the combination of Word2Vec and LSTM (Long Short Term Memory). This paper will introduce two key technologies, Word2Vec and LSTM, combining them to build an effective sentiment analysis model. We conducted a comparative analysis between our model and other state-of-the-art methods including CNN, BiLSTM+CNN, Word2vec+SVM, among others. Through rigorous experimental evaluation, this paper showcases the effectiveness and superior performance of the proposed model in sentiment classification tasks. Our method attains an F1 score of 78.2% on benchmark dataset, indicating its strong performance in the task.

Keywords:
  • Word2Vec
  • LSTM
  • sentiment analysis
  • deep neural network
  • convolutional neural network

How to Cite

Tang , H., Zhang , N., Yu , X., Mao , T., & Wang , L. (2023). Enhancing Sentiment Analysis with Word2Vec and LSTM: A Comparative Study. Journal of Basic and Applied Research International, 29(3), 1–10. https://doi.org/10.56557/jobari/2023/v29i38342

References

Yaling Z, Xiangwei L, Juan W. Analysis and Research of Weibo Public Opinion Based on Text[J]. Journal of Physics: Conference Series. 2021;1769(1).

Haotian H. Research on Public Opinion on Twitter of 2022 Beijing Winter Olympics: Sentiment Analysis based on Support Vector Machine [P]. 2022 7th International Conference on Social Sciences and Economic Development (ICSSED 2022); 2022.

Jun-Jie Z, Jason ZR. The evolution of research in resources, conservation & recycling revealed by Word2vec-enhanced data mining[J]. Resources, Conservation & Recycling. 2023;190.

Xieling C, Lee F W, Gary C, et al. Understanding Learners’ Perception of MOOCs Based on Review Data Analysis Using Deep Learning and Sentiment Analysis [J]. Future Internet. 2022; 14(8).

Wang L, Hu K, Zhang Y, et al. Factor graph model based user profile matching across social networks [J]. IEEE Access. 2019;7:152429-152442.

Wang L, Zhang Y, Hu K. FEUI: F usion E mbedding for U ser I dentification across social networks [J]. Applied Intelligence. 2022;52:8209-8225.

Wang L, Zhang Y, Yuan J, et al. FEBDNN: fusion embedding-based deep neural network for user retweeting behavior prediction on social networks [J]. Neural Computing and Applications. 2022;34(16): 13219-13235.

Wang L, Zhang H, Zhang Y, et al. A Deep Learning-based experiment on forest wildfire detection in machine vision course [J]. IEEE Access. 2023;11:32671-32681.

Summarizing Sentiment-Analyzed Reviews [J]. Journal of Advancements in Robotics. 2018;5(2).

Development of Feature Based Classification of Fruit using Deep Learning [J]. International Journal of Innovative Technology and Exploring Engineering. 2019;8(12).

Anil S, Suresh K. Ontology-based semantic retrieval of documents using Word2vec model[J]. Data & Knowledge Engineering. 2023;144.

Yachun T. Research on Word Vector Training Method Based on Improved Skip-Gram Algorithm [J]. Advances in Multimedia. 2022;2022.

Yaxuan He. Multimodal Sentiment Analysis System based on Combination of LSTM [D].Guangdong University of Technology; 2022. DOI: 10.27029/d.cnki.ggdgu.2022.000930.

Yuan Chai. Research on Sentiment Analysis of Book Review Texts based on LSTM and Word2Vec [J]. Information Technology. 2022;07:59-64+69. DOI: 10.13274/j.cnki.hdzj.2022.07.011

G A,S B,D B, et al. Towards an automated data cleaning with deep learning in CRESST[J]. European Physical Journal Plus. 2023;138(1).

Fenggui Shi. Implementation of chinese text corpus preprocessing module based on jieba Chinese Word Segmentation [J]. Computer Knowledge and Technology. 2020;16(14):248-251+257.

DOI: 10.14004/j.cnki.ckt.2020.1579

Zhang L, Wu Y, Chu Q, et al. SA-Model: Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model [J]. Computer Modeling in Engineering & Sciences. 2023; 137(1).

Huddar GM, Sannakki SS, Rajpurohit SV. Correction to: Attention-based multimodal contextual fusion for sentiment and emotion classification using bidirectional LSTM [J]. Multimedia Tools and Applications. 2021;80(9).

Yaser A, Mohammed A. Thematic Analysis: A Corpus-Based Method for Understanding Themes/Topics of a Corpus through a Classification Process Using Long Short-Term Memory (LSTM)[J]. Applied Sciences. 2023;13(5).

Andreas T, Mark T, Michael B, et al. Confusion Vis: Comparative evaluation and selection of multi-class classifiers based on confusion matrices [J]. Knowledge-Based Systems. 2022; 247.

Wang L, Zhang Y, Xu X. A Novel group detection method for finding related chinese herbs [J]. J. Inf. Sci. Eng. 2015;31(4):1387-1411.

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