Mining opinion from text documents: A survey

Khan, Khairullah; Baharudin, Baharum; Khan, Amin; Fazal-e-Malik,

doi:10.1109/dest.2009.5276756

Cited by 60 publications

(29 citation statements)

References 16 publications

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“…Tweets are limited to 140 characters. They are composed of any of the followings [16] [18][19]: text, links, images, and six seconds video. The mining process is applied to classify these components into positive or negative categories.…”

Section: Data Sourcementioning

confidence: 99%

“…There are typically two techniques to identify sentiment of the text [7] [12-13] [18][19]: knowledge based method and machine learning methods. To make our classification algorithm hybrid, we combine both classification techniques.…”

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

“…They are further classified into supervised and unsupervised methods [12] [14][15][16][17][18][19][20][21][22][23][24]. To carry out sentiment analysis, typically the supervised machine learning methods are used.…”

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

“…Training set is already labelled data whereas test set is used to validate performance of the built classifier. A large number of supervised machine learning algorithms such as Naïve Bayes, Logistic Regression, Support Vector Machine, Maximum Entropy, Decision Tree, Random Forest, and Bayesian Network are used for sentiment analysis [7] [12][13][14][15][16][17][18][19][20][21][22]. Choice of an appropriate algorithm for selected domain is a crucial step.…”

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

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A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks

Desai¹,

Mehta²

2016

IJDKP

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Section: Data Sourcementioning

confidence: 99%

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

Section: Sentiment Classification Techniquesmentioning

confidence: 99%

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A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks

Desai¹,

Mehta²

2016

IJDKP

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show abstract

“…In particular, machine learning (ML) techniques have shown impressive performance in solving real life classification problems in many different areas such as communications (Di, 2007), internet traffic analysis (Nguyen & Armitage, 2008), medical imaging (Wernick, Yang, Brankov, Yourganov, & Strother, 2010), astronomy (Freed & Lee, 2013), document analysis (Khan, Baharudin, Khan, & E-Malik, 2009), biology (Zamani & Kremer, 2011) and time series analysis (Qi & Zhang, 2008). Although complex models such as Neural Networks (NN) and Support Vector Machine (SVM) techniques are studied within the ML field, several other approaches also exist, characterized by a greater degree of simplicity when compared with NN and SVM.…”

Section: Introductionmentioning

confidence: 99%

Evaluating machine learning classification for financial trading: An empirical approach

Gerlein

McGinnity

Belatreche

et al. 2016

Expert Systems with Applications

123

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Technical and quantitative analysis in financial trading use mathematical and statistical tools to help investors decide on the optimum moment to initiate and close orders. While these traditional approaches have served their purpose to some extent, new techniques arising from the field of computational intelligence such as machine learning and data mining have emerged to analyse financial information. While the main financial engineering research has focused on complex computational models such as Neural Networks and Support Vector Machines, there are also simpler models that have demonstrated their usefulness in applications other than financial trading, and are worth considering to determine their advantages and inherent limitations when used as trading analysis tools. This paper analyses the role of simple machine learning models to achieve profitable trading through a series of trading simulations in the FOREX market. It assesses the performance of the models and how particular setups of the models produce systematic and consistent predictions for profitable trading. Due to the inherent complexities of financial time series the role of attribute selection, periodic retraining and training set size are discussed in order to obtain a combination of those parameters not only capable of generating positive cumulative returns for each one of the machine learning models but also to demonstrate how simple algorithms traditionally precluded from financial forecasting for trading applications presents similar performances as their more complex counterparts. The paper discusses how a combination of attributes in addition to technical indicators that has been used as inputs of the machine learning-based predictors such as price related features, seasonality features and lagged values used in classical time series analysis are used to enhance the classification capabilities that impacts directly into the final profitability.

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Study of Socio-Linguistics Online Review System Using Sentiment Scoring Method

Ahamed

Murugan

2019

Advances in Intelligent Systems and Computing

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Mining opinion from text documents: A survey

Cited by 60 publications

References 16 publications

A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks

A Hybrid Classification Algorithm to Classify Engineering Students Problems and Perks

Evaluating machine learning classification for financial trading: An empirical approach

Study of Socio-Linguistics Online Review System Using Sentiment Scoring Method

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