Ideological and political teaching emotion is an important reflection of students’ learning achievements. At present, the effect of emotion analysis of ideological and political students is poor. This article builds on the artificial intelligence technology and combines machine learning data mining ideas to construct a student emotion analysis model in the ideological and political classroom. Starting from the individual, based on the individual’s own emotions and external stimuli, this article carries out emotion transfer probability statistics on the dialogues with emotions marked, and obtains the individual’s emotion transfer matrix. After the corresponding model is constructed, it can be applied to practice, and the research is conducted from the aspects of systematic emotion analysis effect and teaching promotion effect. In addition, this study designs a controlled experiment to analyze the effects of the model. The research results show that the model constructed in this paper has good performance.
KumariA., TanwarS., TyagiS., KumarN., MaasbergM. and ChooK.K.R., Multimedia big data computing and Internet of Things applications: A taxonomy and process model [J], Journal of Network and Computer Applications124, 169–195.
2.
AhilanA., ManogaranG., RajaC., KadryS., KumarS.N., KumarC.A. and MuruganN.S., Segmentation by fractional order Darwinian particle swarm optimization based multilevel thresholding and improved lossless prediction based compression algorithm for medical images [J], IEEE Access7 (2019), 89570–89580.
3.
Ferna´ndezA.M., EsuliA. and SebastianiF., Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification[J], Journal of Artificial Intelligence Research55 (2016), 131–163.
4.
BasiriM.E., KabiriA., AbdarM., et al., The effect of aggregation methods on sentiment classification in Persian reviews[J], Enterprise Information Systems2019(2), 1–28.
5.
ZhangD., ZhangD., XiongH., HsuC.-H. and VasilakosA.V., BASA: Building mobile Ad-Hoc social networks on top of android [J], IEEE Network28(1), 4–9.
6.
DongL., WeiF., LiuS., et al., A Statistical Parsing Framework for Sentiment Classification [J], Computational Lingus41(2) (2014), 293–336.
7.
FangF., DuttaK. and DattaA., Domain Adaptation for Sentiment Classification in Light of Multiple Sources [J], Informs Journal on Computing26(3) (2014), 586–598.
8.
HarerS. and KadamS., Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment[J], International Journal of Computer Applications100(1) (2014), 30–35.
9.
HuangM., QianQ. and ZhuX., Encoding Syntactic Knowledge in Neural Networks for Sentiment Classification [J], ACM Transactions on Information Systems35(3) (2017), 1–27.
10.
IqbalM., KarimA. and KamiranF., Balancing Prediction Errors for Robust Sentiment Classification [J], Acm Transactions on Knowledge Discovery from Data13(3) (2019), 1–21.
11.
LeeG., JeongJ., SeoS., et al., Sentiment classification with word localization based on weakly supervised learning with a convolutional neural network[J], Knowledge-Based Systems152(JUL.15) (2018), 70–82.
12.
LiY., WangJ., WangS., et al., Local dense mixed region cutting+global rebalancing: a method for imbalanced text sentiment classification[J], International Journal of Machine Learning and Cybernetics10(7) (2019), 1805–1820.
13.
LiuS.M. and ChenJ.H., A multi-label classification based approach for sentiment classification [J], Expert Systems with Application42(3) (2015), 1083–1093.
14.
LiuY., BiJ.W. and FanZ.P., Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms [J], Expert Systems with Application80(SEP.) (2017), 323–339.
15.
Mohamed ShakeelP., El TobelyT.E., Al-FeelH., ManogaranG. and BaskarS., Neural network based brain tumor detection using wireless infrared imaging sensor [J], IEEE Access7 (2017), 5577–5588.
16.
PhuV.N., DatN.D., Ngoc TranV.T., et al., Fuzzy C-means for english sentiment classification in a distributed system[J], Applied Intelligence46(3) (2017), 717–738.
17.
PrathikA., UmaK. and AnuradhaJ., Particulate Matter on Human Health and their Feasibility Study Using Machine Learning Algorithms [J], Journal of Chemical and Pharmaceutical Research8(9) (2016), 260–264.
18.
RaoG., HuangW., FengZ., et al., LSTM with sentence representations for document-level sentiment classification[J], Neurocomputing308(SEP.25) (2018), 49–57.
19.
ShangL., ZhouZ. and LiuX., Particle swarm optimization-based feature selection in sentiment classification [J], Soft Computing20(10) (2016), 3821–3834.
20.
VishwakarmaS. and KumarS., A Lexical Approach for Tweets Sentiment Classification [J], Journal of Applied Physics76(3) (2015), 1980–1982.
21.
WangD., JingB., LuC., et al., Coarse Alignment of Topic and Sentiment: A Unified Model for Cross-Lingual Sentiment Classification[J], IEEE Transactions on Neural Networks and Learning Systems PP(99) (2020), 1–1.
22.
WangG., SunJ., MaJ., et al., Sentiment classification: The contribution of ensemble learning[J], Decision Support Systems57 (2014), 77–93.
23.
WangG., SunJ., MaJ., et al., Sentiment classification: The contribution of ensemble learning[J], Decision Support Systems57(jan.) (2014), 77–93.
24.
ZhangZ., WangZ., GanC., et al., A double auction scheme of resource allocation with social ties and sentiment classification for Device-to-Device communications [J], Computer Networks155(MAY 22) (2019), 62–71.
25.
ZhouS., ChenQ. and WangX., Fuzzy deep belief networks for semi-supervised sentiment classification [J], Neurocomputing131 (2014), 312–322.
