A Fused Feature Selection Technique for Enhanced Sentiment Analysis Using Deep Learning

Muthukrishnan, Meenakshi; Andavar, Suruliandi; Raj, Raja Soosaimarian Peter

doi:10.1590/1678-4324-2024240183

Acessibilidade / Reportar erro

Brasil

Brazilian Archives of Biology and Technology

Español English

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Article - Engineering, Technology and Techniques • Braz. arch. biol. technol. 67 • 2024 • https://doi.org/10.1590/1678-4324-2024240183 copy

A Fused Feature Selection Technique for Enhanced Sentiment Analysis Using Deep Learning

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Sentiment analysis holds paramount importance in contemporary business landscapes, particularly in leveraging insights from the extensive pool of social media data. The rise of social media platforms, including opinion polls, weblogs, Twitter, and various other networks, has accentuated the need for effective sentiment analysis tools. Deep learning has emerged as a pivotal technique in natural language processing (NLP), particularly for sentiment analysis tasks, owing to its ability to autonomously learn features. However, the performance of deep learning models can suffer when confronted with a large number of features. To address this limitation, this paper proposes a novel fused feature selection technique, Chi-Vec, aimed at selectively passing relevant features to deep learning models. Chi-Vec is a fusion of Chi-square and Word2Vec. The research encompasses the exploration of three distinct datasets; CBET, ATIS, and AWARE. Leveraging the bi-directional Long Short-Term Memory (Bi-LSTM) architecture in conjunction with Chi-Vec, the approach achieves remarkable accuracy rates of 97.96%, 98.41%, and 94.45% for CBET, ATIS, and AWARE dataset respectively. Chi-Vec not only enhances the efficiency and accuracy of sentiment analysis but also demonstrates promising potential for various NLP applications.

Keywords:
Sentiment Analysis; Deep Learning; Feature Selection; Chi-Vec

HIGHLIGHTS

Proposed a novel fused feature selection technique

The proposed Chi-Vec captures the semantic and statistical features.

Explored the proposed method with three distinct datasets.

S.No.	Field	Data Type	Description
1.	Text	text	Raw text Data
2.	Emotion	text	The emotion of the text

S.No.	Class Name	No. of Instances
1.	Anger	8000
2.	Fear	8000
3.	Joy	8000
4.	Love	8000
5.	Sadness	8000
6.	Surprise	8000
7.	Thankfulness	8000
8.	Disgust	8000
9.	Guilt	8000
	Total Instances	72000

S.No.	Field	Data Type	Description
1.	Intent Id	text	Id of the Intent
2.	Text	text	Airline travel information text

S. No	Class Name	No. of Instances
1.	atis _ abbreviation	147
2.	atis _ aircraft	81
3.	atis _ airfare	423
4.	atis _ airline	157
5.	atis _ flight	3666
6.	atis _ flight _ time	54
7.	atis _ ground _ service	255
	Total Instances	4783

S.No.	Field	Data Type	Description
1.	review	Text	The raw text
2.	sentiment	text	The sentiment behind the review

S. No	Class Name	No. of Instances
1.	P	25000
2.	N	25000
	Total instances	50000

Performance Metric	Formula
Accuracy	$\frac{T P + T N}{T P + T N + F P + F N}$
Recall	$\frac{T P}{T P + F N}$
Precision	$\frac{T P}{T P + F P}$
F1-score	$\frac{2}{\frac{1}{P} + \frac{1}{R}}$ Where P is the precision and R is the Recall

Models	k value for chi-square	Accuracy (%)
Models	k value for chi-square	CBET	ATIS	AWARE
CNN	1000	85.96	86.22	83.48
	500	88.00	89.09	84.82
	200	91.47	90.72	85.73
	100	95.89	93.39	86.20
	50	93.93	96.36	85.10
	40	92.15	95.95	84.82
RNN	1000	89.25	86.73	83.84
RNN	500	91.26	89.91	85.59
	200	94.57	92.14	87.15
	100	96.71	94.59	88.20
	50	95.42	96.46	87.48
	40	94.75	95.68	86.96
LSTM	1000	90.96	88.76	86.86
	500	93.48	91.21	88.53
	200	96.73	93.75	91.38
	100	97.71	95.98	92.20
	50	96.54	97.36	91.39
	40	95.94	96.86	90.85
Bi-LSTM	1000	91.21	89.91	87.46
	500	93.89	90.35	91.99
	200	96.57	92.89	93.58
	100	97.96	96.53	94.45
	50	95.78	98.41	93.01
	40	90.43	97.99	90.97
GRU	1000	88.54	86.49	80.96
	500	91.44	88.83	83.19
	200	94.17	90.86	85.69
	100	96.31	92.47	87.00
	50	95.17	93.35	85.86
	40	94.76	92.59	84.98
BERT	1000	90.95	88.26	84.49
	500	93.00	90.92	86.67
	200	95.11	93.55	88.99
	100	96.92	95.18	90.50
	50	95.62	96.66	89.10
	40	94.99	95.97	88.69

Model	Feature Selection Techniques	Accuracy(%)	Precision(%)	Recall(%)	F-score(%)
CNN	Without Feature Selection	92.09	92.23	92.09	92.84
	Chi-square	95.14	95.58	95.14	96.19
	Mutual Information	93.84	93.98	93.84	94.59
	Word2Vec	94.84	94.98	94.84	95.59
	PCA	93.34	93.48	93.34	94.09
	Proposed Chi-Vec	95.89	96.03	95.89	96.64
RNN	Without Feature Selection	92.91	93.00	92.91	90.73
	Chi-square	96.26	96.35	96.26	94.08
	Mutual Information	94.66	94.75	94.66	92.48
	Word2Vec	95.66	95.75	95.66	93.48
	PCA	94.16	94.25	94.16	91.98
	Proposed Chi-Vec	96.71	96.80	96.71	94.53
LSTM	Without Feature Selection	93.91	92.76	93.91	92.45
	Chi-square	96.26	96.11	96.26	95.80
	Mutual Information	95.66	94.51	95.66	94.20
	Word2Vec	96.00	95.51	96.00	95.20
	PCA	95.16	94.01	95.16	93.7
	Proposed Chi-Vec	97.71	96.56	97.71	96.25
Bi-LSTM	Without Feature Selection	94.96	93.81	94.96	93.5
	Chi-square	96.31	95.16	96.31	94.85
	Mutual Information	94.71	93.56	94.71	93.25
	Word2Vec	95.71	94.56	95.71	94.25
	PCA	94.21	93.06	94.21	92.75
	Proposed Chi-Vec	97.96	97.61	97.96	97.30
GRU	Without Feature Selection	92.51	92.37	92.51	90.14
	Chi-square	95.86	95.72	95.86	93.49
	Mutual Information	94.26	94.12	94.26	91.89
	Word2Vec	95.26	95.12	95.26	92.89
	PCA	93.76	93.62	93.76	91.39
	Proposed Chi-Vec	96.31	96.17	96.31	93.94
BERT	Without Feature Selection	93.12	92.26	93.12	89.84
	Chi-square	95.97	95.61	95.97	93.19
	Mutual Information	94.87	94.01	94.87	91.59
	Word2Vec	95.87	95.01	95.87	92.59
	PCA	94.37	93.51	94.37	91.09
	Proposed Chi-Vec	96.92	96.06	96.92	93.64

Model	Feature Selection Techniques	Accuracy(%)	Precision(%)	Recall(%)	F-score(%)
CNN	Without Feature Selection	91.56	91.63	91.56	90.68
	Chi-square	94.91	94.98	94.91	94.03
	Mutual Information	93.31	93.38	93.31	92.43
	Word2Vec	94.31	94.38	94.31	93.43
	PCA	92.81	92.88	92.81	91.93
	Proposed Chi-Vec	96.36	96.43	96.36	95.48
RNN	Without Feature Selection	91.66	91.64	91.66	90.73
	Chi-square	95.01	94.99	95.01	94.08
	Mutual Information	93.41	93.39	93.41	92.48
	Word2Vec	94.41	94.39	94.41	93.48
	PCA	92.91	92.89	92.91	91.98
	Proposed Chi-Vec	96.46	96.44	96.46	95.53
LSTM	Without Feature Selection	92.56	92.15	92.56	91.62
	Chi-square	95.91	95.5	95.91	94.97
	Mutual Information	94.31	93.9	94.31	93.37
	Word2Vec	95.31	94.9	95.31	94.37
	PCA	93.81	93.4	93.81	92.87
	Proposed Chi-Vec	97.36	96.95	97.36	96.42
Bi-LSTM	Without Feature Selection	93.61	93.2	93.61	92.67
	Chi-square	96.96	96.55	96.96	96.02
	Mutual Information	95.36	94.95	95.36	94.42
	Word2Vec	96.36	95.95	96.36	95.42
	PCA	94.86	94.45	94.86	93.92
	Proposed Chi-Vec	98.41	98.00	98.41	97.47
GRU	Without Feature Selection	88.55	87.12	88.55	85.58
	Chi-square	91.90	90.47	91.90	88.93
	Mutual Information	90.30	88.87	90.30	87.33
	Word2Vec	91.30	89.87	91.30	88.33
	PCA	89.80	88.37	89.80	86.83
	Proposed Chi-Vec	93.35	91.92	93.35	90.38
BERT	Without Feature Selection	91.86	92.47	91.86	90.92
	Chi-square	95.21	95.82	95.21	94.27
	Mutual Information	93.61	94.22	93.61	92.67
	Word2Vec	94.61	95.22	94.61	93.67
	PCA	93.11	93.72	93.11	92.17
	Proposed Chi-Vec	96.66	97.27	96.66	95.72

Model	Feature Selection Techniques	Accuracy(%)	Precision(%)	Recall(%)	F-score(%)
CNN	Without Feature Selection	82.20	83.04	82.20	81.93
	Chi-square	85.55	86.39	85.55	85.28
	Mutual Information	83.95	84.79	83.95	83.68
	Word2Vec	84.95	85.79	84.95	84.68
	PCA	83.45	84.29	83.45	83.18
	Proposed Chi-Vec	86.20	87.04	86.20	85.93
RNN	Without Feature Selection	83.90	84.87	83.90	83.63
	Chi-square	87.25	88.22	87.25	86.98
	Mutual Information	85.65	86.62	85.65	85.38
	Word2Vec	86.65	87.62	86.65	86.38
	PCA	85.15	86.12	85.15	84.88
	Proposed Chi-Vec	88.20	89.17	88.20	87.93
LSTM	Without Feature Selection	87.20	87.48	87.20	87.00
	Chi-square	90.55	90.83	90.55	90.35
	Mutual Information	88.95	89.23	88.95	88.75
	Word2Vec	89.95	90.23	89.95	89.75
	PCA	88.45	88.73	88.45	88.25
	Proposed Chi-Vec	92.20	92.48	92.20	92.00
Bi-LSTM	Without Feature Selection	88.45	88.73	88.45	88.25
	Chi-square	91.80	92.08	91.80	91.60
	Mutual Information	90.20	90.48	90.20	90.00
	Word2Vec	91.20	91.48	91.20	91.00
	PCA	89.70	89.98	89.70	89.50
	Proposed Chi-Vec	94.45	94.73	94.45	94.25
GRU	Without Feature Selection	82.20	83.54	82.20	81.77
	Chi-square	85.55	86.89	85.55	85.12
	Mutual Information	83.95	85.29	83.95	83.52
	Word2Vec	84.95	86.29	84.95	84.52
	PCA	83.45	84.79	83.45	83.02
	Proposed Chi-Vec	87.00	88.34	87.00	86.57
BERT	Without Feature Selection	85.70	86.88	85.70	85.41
	Chi-square	89.05	90.23	89.05	88.76
	Mutual Information	87.45	88.63	87.45	87.16
	Word2Vec	88.45	89.63	88.45	88.16
	PCA	86.95	88.13	86.95	86.66
	Proposed Chi-Vec	90.50	91.68	90.50	90.21

Models	Accuracy (%)
SMO-SVM [⁴4 Sharma S, Jain A. Hybrid ensemble learning with feature selection for sentiment classification in social media. In: Research Anthology on Applying Social Networking Strategies to Classrooms and Libraries. IGI Global; 2022. p. 1183-203.]	88.20
CSO-LSTMNN [⁵5 Alarifi A, Tolba A, Al-Makhadmeh Z, Said W. A big data approach to sentiment analysis using greedy feature selection with cat swarm optimization-based long short-term memory neural networks. J Supercomput. 2020 Jun;76:4414-29.]	96.89
HFV+LSTM [⁷7 Kaur G, Sharma A. A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. J Big Data. 2023 Jan 13;10(1):5.]	95.90
VPCNN-ABiLSTM [⁸8 Daniel DA, Meena MJ. Deep learning-based hybrid sentiment analysis with feature selection using optimization algorithm. Multimed. Tools Appl. 2023 Apr 27:1-24.]	97.10
Proposed Chi-Vec Feature Selection	98.41

Instituto de Tecnologia do Paraná - Tecpar Rua Prof. Algacyr Munhoz Mader, 3775 - CIC, 81350-010 Curitiba PR Brazil, Tel.: +55 41 3316-3052/3054, Fax: +55 41 3346-2872 - Curitiba - PR - Brazil
E-mail: babt@tecpar.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] *Correspondence: minakshiy10@gmail.com; Tel.: +91-9025723183