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Predictive Analytics for Stock Prices using Sentiment Analysis

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Salma Elsayed

Salma Elsayed. Predictive Analytics for Stock Prices using Sentiment Analysis. International Journal of Computer Applications 183(48):32-37, January 2022. BibTeX

	author = {Salma Elsayed},
	title = {Predictive Analytics for Stock Prices using Sentiment Analysis},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2022},
	volume = {183},
	number = {48},
	month = {Jan},
	year = {2022},
	issn = {0975-8887},
	pages = {32-37},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2022921888},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Stock prediction is consider one of the most popular tasks in the last decade. Stock prediction can be achieved through the analysis of the numerical values (e.g., open price and close price) or the sentiment analysis of social media text (e.g., tweets). In this paper, we will discuss the several approach of stock prediction using sentiment analysis methods. These methods can be classified into four main categories, namely, machine learning, lexicon, graph, and hybrid based methods. Besides, we discussed the basic tools used to help in the task of sentiment analysis such as Term Frequency Inverse Document Frequency and word2cev algorithms.


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Stock price, stock market, sentiment analysis, stock movement, sentiment classification, machine learning, graph based, lexicon based, hybrid.