Abstract
Sentiment analysis examines the emotional content of a statement, such as views, assessments, feelings, or attitudes about a topic, human, or object. Emotions can be categorized as either unbiased, good, or bad. It determines how people feel about the company online through social media. Based on the sentiments, the problem of solving the stock price prediction model is advantageous as it involves the sentiment score evaluated from the text information. This work introduces a new stock price prediction considering sentiment scores from text info in this concern. For that, we have considered news data and stock data. Moreover, this work falls under bigdata perspective by increasing the data size. The proposed model includes two major steps: feature extraction and prediction. Feature extraction takes place under two scenarios: features from news data and features from stock data. Features like Bag of words, n-Gram, TFIDF, and Improved cosine similarity are extracted from the news data, and features like improved exponential moving average and other existing technical indicator-based features such as ATR, TR are extracted from stock data. Both the feature sets are fused to determine the final prediction results. Particularly, this final observation involves the sentiments from the given news data. For this, optimized LSTM model is used, where the optimal training process will be carried out by a new Harris Hawks Induced Sparrow Search Optimization via tuning the optimal weights. The proposed model is the combination of Harris Hawks Optimization Algorithm and Sparrow Search Algorithm, respectively. Finally, the performance of proposed work will be evaluated over the other conventional models with respect to different measures.
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Data availability
The datasets (datasets 1–6) were collected. "The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear and TCS. In addition, each company includes 3 datasets (a) in daily option, set start day 1–1-2019 and end day 1-12-2020, (b) in monthly option, set start jan2000 and end dec2020, and (c) in yearly option, set year 2000."
Abbreviations
- SA:
-
Sentiment analysis
- NLP:
-
Natural language processing
- Multi-AFM:
-
Multi-attention fusion modeling
- CNN:
-
Convolutional neural network
- SLCABG:
-
Sentiment Lexicon Convolutional Neural Network and Bi-GRU
- TBoC:
-
Tagged bag-of-concepts
- Bi-GRU:
-
Bidirectional gated recurrent unit
- ATR:
-
Average true range
- TR:
-
True range
- IR:
-
Information retrieval
- ML:
-
Machine learning
- TFIDF:
-
Term frequency inverse document frequency
- BOW:
-
Bag of words
- LSTM:
-
Long short-term memory
- SSA:
-
Sparrow search algorithm
- HHO:
-
Harris Hawks Optimization
- ANN:
-
Artificial neural network
References
Abboud R, Tekli J (2020) Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition. Soft Comput 24:9875–9925. https://doi.org/10.1007/s00500-019-04503-4
Abdul Aziz A, Starkey A (2020) Predicting supervise machine learning performances for sentiment analysis using contextual-based approaches. IEEE Access 8:17722–17733. https://doi.org/10.1109/ACCESS.2019.2958702
Ahmed M, Chen Q, Li Z (2020) Constructing domain-dependent sentiment dictionary for sentiment analysis. Neural Comput Appli 32:14719–14732. https://doi.org/10.1007/s00521-020-04824-8
Aydin CR, Güngör T (2020) Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations. IEEE Access 8:77820–77832. https://doi.org/10.1109/ACCESS.2020.2990306
Consoli S, Barbaglia L, Manzan S (2022) Fine-grained, aspect-based sentiment analysis on economic and financial lexicon. Knowl Based Syst 247:108781
Dashtipour K, Gogate M, Gelbukh A et al (2022) Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis. Soc Netw Anal Min 12:9. https://doi.org/10.1007/s13278-021-00840-1
de Oliveira Carosia AE, Coelho GP, da Silva AE (2021d) Investment strategies applied to the Brazilian stock market: a methodology based on Sentiment Analysis with deep learning. Expert Syst Appl 184:115470
Fu Y, Liu Y, Peng SL (2020) An integrated word embedding-based dual-task learning method for sentiment analysis. Arab J Sci Eng 45:2571–2586. https://doi.org/10.1007/s13369-019-04241-7
Ito T, Tsubouchi K, Sakaji H et al (2020) Contextual sentiment neural network for document sentiment analysis. Data Sci Eng 5:180–192. https://doi.org/10.1007/s41019-020-00122-4
Jing N, Wu Z, Wang H (2021b) A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Syst Appl 178:115019
Koratamaddi P, Wadhwani K, Gupta M, Sanjeevi SG (2021c) Market sentiment-aware deep reinforcement learning approach for stock portfolio allocation. Eng Sci Technol Int J. 24(4):848–859
Li Z, Li R, Jin G (2020) Sentiment analysis of danmaku videos based on naïve bayes and sentiment dictionary. IEEE Access 8:75073–75084. https://doi.org/10.1109/ACCESS.2020.2986582
Mehanna YS, Mahmuddin MB (2021) A semantic conceptualization using tagged bag-of-concepts for sentiment analysis. IEEE Access 9:118736–118756. https://doi.org/10.1109/ACCESS.2021.3107237
Naresh Kumar KE, Uma V (2021) Intelligent sentinet-based lexicon for context-aware sentiment analysis: optimized neural network for sentiment classification on social media. J Supercomput 77:12801–12825. https://doi.org/10.1007/s11227-021-03709-4
Phan HT, Tran VC, Nguyen NT, Hwang D (2020) Improving the performance of sentiment analysis of tweets containing fuzzy sentiment using the feature ensemble model. IEEE Access 8:14630–14641. https://doi.org/10.1109/ACCESS.2019.2963702
Wan Q, Xu X, Zhuang J, Pan B (2021a) A sentiment analysis-based expert weight determination method for large-scale group decision-making driven by social media data. Expert Syst Appl 185:115629
Wang L, Niu J, Yu S (2020) Sentidiff: combining textual information and sentiment diffusion patterns for twitter sentiment analysis. IEEE Trans Knowl Data Eng 32(10):2026–2039. https://doi.org/10.1109/TKDE.2019.2913641
Wang Y, Huang G, Li J, Li H, Zhou Y, Jiang H (2021) Refined global word embeddings based on sentiment concept for sentiment analysis. IEEE Access 9:37075–37085. https://doi.org/10.1109/ACCESS.2021.3062654
Yang L, Li Y, Wang J, Sherratt RS (2020) Sentiment analysis for E-commerce product reviews in chinese based on sentiment lexicon and deep learning. IEEE Access 8:23522–23530. https://doi.org/10.1109/ACCESS.2020.2969854
Yuan J, Wu Y, Lu X et al (2020) Recent advances in deep learning based sentiment analysis. Sci China Technol Sci 63:1947–1970. https://doi.org/10.1007/s11431-020-1634-3
Zhai G, Yang Y, Wang H, Du S (2020) Multi-attention fusion modeling for sentiment analysis of educational big data. Big Data Min Anal 3(4):311–319. https://doi.org/10.26599/BDMA.2020.9020024
Zhao X, Liu Y, Jin Z (2021) Multiplex network embedding for implicit sentiment analysis. Complex Intell Syst 7:3179–3193. https://doi.org/10.1007/s40747-021-00504-9
Zhou J, Jin S, Huang X (2020) ADeCNN: an improved model for aspect-level sentiment analysis based on deformable CNN and attention. IEEE Access 8:132970–132979. https://doi.org/10.1109/ACCESS.2020.3010802
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YA conceived the presented idea and designed the analysis. Also, he carried out the experiment and wrote the manuscript with support from SKAP. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript.
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Ayyappa, Y., Siva Kumar, A.P. Optimized long short-term memory-based stock price prediction with sentiment score. Soc. Netw. Anal. Min. 13, 13 (2023). https://doi.org/10.1007/s13278-022-01004-5
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DOI: https://doi.org/10.1007/s13278-022-01004-5