Traditional English text sentiment analysis models have limitations in handling complex texts and long-term dependency relationships, leading to an impact on the accuracy and deep understanding of sentiment analysis. This article aims to solve the above problems by applying the LSTM (long short-term memory) algorithm. First, the data is cleaned and preprocessed, including regular expressions, word segmentation, stop word filtering, and stem extraction. Next, a sentiment lexicon is utilized to annotat. Word2Vec and TF-IDF are combined for feature vectorization, and the positional encoding is applied. An LSTM network structure is then constructed. After training optimization, the model achieves an accuracy of up to 90% on the test set, outperforming other models. The LSTM algorithm effectively solves the limitations of traditional models and achieves higher accuracy. In summary, the application of LSTM algorithm in English text sentiment analysis models can effectively address existing limitations and make them more accurate.
CenPZhangKZhengD. Sentiment analysis using deep learning approach. J Artif Intell2020; 2(1): 17–27.
2.
HungLPAliasS. Beyond sentiment analysis: a review of recent trends in text based sentiment analysis and emotion detection. J Adv Comput Intell Intell Inf2023; 27(1): 84–95.
3.
MarlinaDTri BasukiKMohdZZ, et al.Sentiment analysis on natural skincare products. J Data Sci2022; 2022(12): 1–17.
4.
RiceDRZornC. Corpus-based dictionaries for sentiment analysis of specialized vocabularies. Political Sci Res Methods2021; 9(1): 20–35.
5.
RazaHFaizanMHamzaA, et al.Scientific text sentiment analysis using machine learning techniques. Int J Adv Comput Sci Appl2019; 10(12): 157–165.
6.
YadavAVishwakarmaDK. Sentiment analysis using deep learning architectures: a review. Artif Intell Rev2020; 53(6): 4335–4385.
7.
BontaVKumareshNJanardhanN. A comprehensive study on lexicon based approaches for sentiment analysis. Asian Journal of Computer Science and Technology2019; 8(S2): 1–6.
8.
D’AnielloGGaetaMLa RoccaI. KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artif Intell Rev2022; 55(7): 5543–5574.
9.
HartmannJHeitmannMSiebertC, et al.More than a feeling: accuracy and application of sentiment analysis. Int J Res Market2023; 40(1): 75–87.
10.
LiJDengDZhaoJ, et al.A novel hybrid short-term load forecasting method of smart grid using MLR and LSTM neural network. IEEE Trans Ind Inf2020; 17(4): 2443–2452.
11.
RuwaliAKumarAJSPrakashKB, et al.Implementation of hybrid deep learning model (LSTM-CNN) for ionospheric TEC forecasting using GPS data. IEEE Geosci Remote Sens Lett2020; 18(6): 1004–1008.
12.
RahimzadMMoghaddam NiaAZolfonoonH, et al.Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting. Water Resour Manag2021; 35(12): 4167–4187.
13.
TripathiM. Sentiment analysis of Nepali covid19 tweets using nb svm and lstm. J Artif Intell2021; 3(03): 151–168.
14.
ParkJLeeH. Prediction of high turbidity in rivers using LSTM algorithm. J Korean Soc Water Wastewater2020; 34(1): 35–43.
15.
KimMHKimJHLeeK, et al.The prediction of COVID-19 using LSTM algorithms. International Journal of Networked and Distributed Computing2021; 9(1): 19–24.
16.
ZhangXChauTKChowYH, et al.A novel sequence to sequence data modelling based CNN-LSTM algorithm for three years ahead monthly peak load forecasting. IEEE Trans Power Syst2023; 39(1): 1932–1947.
17.
PuKLiuXHeH. Wet antenna attenuation model of E-band microwave links based on the LSTM Algorithm. IEEE Antennas Wirel Propag Lett2020; 19(9): 1586–1590.
18.
ShanYouLWangBRLuJQ, et al.Prediction of instrumental intensity for a single station using a LSTM neural network. Chin J Geophys2024; 67(2): 587–599.
19.
KulkarniARMundheSD. An application of porters stemming algorithm for text mining in healthcare. Int J Manag IT Eng (IJMIE)2019; 7(11): 223–228.
20.
JohnsonSJMurtyMRNavakanthI. A detailed review on word embedding techniques with emphasis on word2vec. Multimed Tool Appl2024; 83(13): 37979–38007.
21.
JiSSatishNLiS, et al.Parallelizing word2vec in shared and distributed memory. IEEE Trans Parallel Distr Syst2019; 30(9): 2090–2100.
22.
LeeJHLeeMBKimJW. A study on Korean language processing using TF-IDF. J Inf Syst2019; 28(3): 105–121.
23.
UmadeviM. Document comparison based on tf-idf metric. International Research Journal of Engineering and Technology (IRJET)2020; 7(02): 1546–1550.
24.
YuYSiXHuC, et al.A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput2019; 31(7): 1235–1270.
25.
MaMMaoZ. Deep-convolution-based LSTM network for remaining useful life prediction. IEEE Trans Ind Inf2020; 17(3): 1658–1667.
26.
TurkogluMHanbayDSengurA. Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests. J Ambient Intell Hum Comput2022; 13(7): 3335–3345.
27.
KehmBMLarsenMRSommerselHB. Student dropout from universities in Europe: a review of empirical literature. Hungarian Educational Research Journal2019; 9(2): 147–164.
28.
NicolettiMC. Revisiting the Tinto’s theoretical dropout model. High Educ Stud2019; 9(3): 52–64.
29.
VershyninR. Memory capacity of neural networks with threshold and rectified linear unit activations. SIAM Journal on Mathematics of Data Science2020; 2(4): 1004–1033.
30.
ZhaoMZhongSFuX, et al.Deep residual networks with adaptively parametric rectifier linear units for fault diagnosis. IEEE Trans Ind Electron2020; 68(3): 2587–2597.
31.
CartisCGouldNITointPL. Universal regularization methods: varying the power, the smoothness and the accuracy. SIAM J Optim2019; 29(1): 595–615.
