Effect of training set size on SVM and Naïve Bayes for Twitter sentiment analysis

Abdelwahab, Omar; Bahgat, Mohamed; Lowrance, Christopher J.; Elmaghraby, Adel

doi:10.1109/isspit.2015.7394379

Cited by 31 publications

(19 citation statements)

References 2 publications

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“…Figure 1 consists of five phases; firstly, the dataset from Twitter will be collected by using Twitter Application Program Interface (API) because the streaming can provide a continuous stream of the information with updates [18]. Secondly, pre-processing the data set using Natural Language Pre-processing (NLP) [1], where NLP processing start with transformations step [25] and end with the extraction process to extract the required features [40]. Thirdly, is the expert labelling technique where in this phase the dataset will be classified into positive, negative and neutral polarity.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers

Alkubaisi¹,

Kamaruddin²,

Husni³

2018

IJET

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Sentiment analysis has gained a lot of importance in last decade especially on the availability of data from Twitter that has created more interest for research in this field. Nevertheless, stock market classification models still suffer less accuracy and this has affected negatively the stock market indicators. In this paper, a new framework related to sentiment analysis from Twitter posts is proposed. The proposed framework represents an improved design of classification model that works to improve the classification accuracy to support decision makers in the domain of stock market exchange. This model starts with data collection part and in second phase filtration is done on data to get only the relevant data. The most important phase is the labelling part in which polarity of data is determined and negative, positive or neutral values are assigned to statements of people. The fourth part is the classification phase in which suitable patterns of stock market will be identified by hybridizing NBCs. The last phase is performance and evaluation. This study proposes to a Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification, hence represents a useful study for investors, companies and researchers and will help them to formulate their policies according to sentiments of people.

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Section: The Proposed Methodsmentioning

confidence: 99%

Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers

Alkubaisi¹,

Kamaruddin²,

Husni³

2018

IJET

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“…Sentiment analysis technique affects the training set before classification, preparation, and detection of the polarity for the dataset helps to improve the classification accuracy (Abdelwahab, Bahgat, Lowrance, & Elmaghraby, 2015).…”

Section: Classifiersmentioning

confidence: 99%

“…1; Figure 1 consists of five phases: firstly, the collection of dataset from Twitter using Twitter Application Program Interface (API) because the streaming can provide a continuous stream of the information with updates (Go, Bhayani, & Huang, 2009). Secondly, pre-processing the data set using NLP (Abdelwahab et al, 2015). NLP processing starts with the extraction process to extract the required features (Yang et al, 2015) and ends with transformations step (Kouloumpis, Wilson, & Moore, 2011).…”

Section: The Proposed Modelmentioning

confidence: 99%

“…The last process in pre-processing method works on extracting all relevant contents and the required features from tweets and dropping the irrelevant contents. This is named filtering step and all steps before it, are called transformations (Abdelwahab et al, 2015). The following explains the main two categories of the pre-processing method:…”

Section: Phase Two: Data Pre-processingmentioning

confidence: 99%

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Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Alkubaisi¹,

Kamaruddin²,

Husni³

2018

CIS

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Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.

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“…In [11] the researchers looked at how varying the training set size on the classification impacts on accuracy and F-score of SVM and Naïve Bayes classifiers. In the research it was concluded that the SVM accuracy largely surpassed that of the Naïve Bayes classifier hence the need to opt for the SVM model for the training the model.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Sentiment Analysis of Twitter Feeds using Machine Learning, Effect of Feature Hash Bit Size

Kwabla¹,

Kwame²,

Katsriku³

2017

CAE

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Sentiment Analysis is a way of considering and grouping of opinions or views expressed in a text. In this age when social media technologies are generating vast amounts of data in the form of tweets, Facebook comments, blog posts, and Instagram comments, sentiment analysis of these usergenerated data provides very useful feedback. Since it is undisputable facts that twitter sentiment analysis has become an effective way in determining public sentiment about a certain topic product or issue. Thus, a lot of research have been ongoing in recent years to build efficient models for sentiment classification accuracy and precision. In this work, we analyse twitter data using support vector machine algorithm to classify tweets into positive, negative and neutral sentiments. This research try to find the relationship between feature hash bit size and the accuracy and precision of the model that is generated. We measure the effect of varying the feature has bit size on the accuracy and precision of the model. The research showed that as the feature hash bit size increases at a certain point the accuracy and precision value started decreasing with increase in the feature hash bit size.

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Effect of training set size on SVM and Naïve Bayes for Twitter sentiment analysis

Cited by 31 publications

References 2 publications

Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers

Conceptual Framework for Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naïve Bayes Classifiers

Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Sentiment Analysis of Twitter Feeds using Machine Learning, Effect of Feature Hash Bit Size

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