Data Mining for Tweet Sentiment Classification: Twitter Sentiment Analysis

 
9783659295171: Data Mining for Tweet Sentiment Classification: Twitter Sentiment Analysis

The goal of this work is to classify short Twitter messages with respect to their sentiment using data mining techniques. Twitter messages, or tweets, are limited to 140 characters. This limitation makes it more difficult for people to express their sentiment and as a consequence, the classification of the sentiment will be more difficult as well. The sentiment can refer to two different types: emotions and opinions. This research is solely focused on the sentiment of opinions. These opinions can be divided into three classes: positive, neutral and negative. The tweets are then classified with an algorithm to one of those three classes. Known supervised learning algorithms as support vector machines and naive Bayes are used to create a prediction model. Before the prediction model can be created, the data has to be pre-processed from text to a fixed-length feature vector. The features consist of sentiment-words and frequently occurring words that are predictive for the sentiment. The learned model is then applied to a test set to validate the model.

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About the Author:

Roy de Groot is a computer scientist who focuses on data analysis during his study and current work at Avanade Netherlands. He graduated for his bachelor and master Computing Science at the Utrecht University. His master thesis was about tweet sentiment classification, in which he tried to classify tweets with respect to their sentiment.

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de Groot, Roy
ISBN 10: 3659295175 ISBN 13: 9783659295171
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Descripción Estado de conservación: New. Publisher/Verlag: LAP Lambert Academic Publishing | Twitter Sentiment Analysis | The goal of this work is to classify short Twitter messages with respect to their sentiment using data mining techniques. Twitter messages, or tweets, are limited to 140 characters. This limitation makes it more difficult for people to express their sentiment and as a consequence, the classification of the sentiment will be more difficult as well. The sentiment can refer to two different types: emotions and opinions. This research is solely focused on the sentiment of opinions. These opinions can be divided into three classes: positive, neutral and negative. The tweets are then classified with an algorithm to one of those three classes. Known supervised learning algorithms as support vector machines and naive Bayes are used to create a prediction model. Before the prediction model can be created, the data has to be pre-processed from text to a fixed-length feature vector. The features consist of sentiment-words and frequently occurring words that are predictive for the sentiment. The learned model is then applied to a test set to validate the model. | Format: Paperback | Language/Sprache: english | 160 gr | 220x150x6 mm | 108 pp. Nº de ref. de la librería K9783659295171

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Descripción LAP Lambert Academic Publishing Nov 2012, 2012. Taschenbuch. Estado de conservación: Neu. Neuware - The goal of this work is to classify short Twitter messages with respect to their sentiment using data mining techniques. Twitter messages, or tweets, are limited to 140 characters. This limitation makes it more difficult for people to express their sentiment and as a consequence, the classification of the sentiment will be more difficult as well. The sentiment can refer to two different types: emotions and opinions. This research is solely focused on the sentiment of opinions. These opinions can be divided into three classes: positive, neutral and negative. The tweets are then classified with an algorithm to one of those three classes. Known supervised learning algorithms as support vector machines and naive Bayes are used to create a prediction model. Before the prediction model can be created, the data has to be pre-processed from text to a fixed-length feature vector. The features consist of sentiment-words and frequently occurring words that are predictive for the sentiment. The learned model is then applied to a test set to validate the model. 108 pp. Englisch. Nº de ref. de la librería 9783659295171

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Descripción LAP Lambert Academic Publishing Nov 2012, 2012. Taschenbuch. Estado de conservación: Neu. Neuware - The goal of this work is to classify short Twitter messages with respect to their sentiment using data mining techniques. Twitter messages, or tweets, are limited to 140 characters. This limitation makes it more difficult for people to express their sentiment and as a consequence, the classification of the sentiment will be more difficult as well. The sentiment can refer to two different types: emotions and opinions. This research is solely focused on the sentiment of opinions. These opinions can be divided into three classes: positive, neutral and negative. The tweets are then classified with an algorithm to one of those three classes. Known supervised learning algorithms as support vector machines and naive Bayes are used to create a prediction model. Before the prediction model can be created, the data has to be pre-processed from text to a fixed-length feature vector. The features consist of sentiment-words and frequently occurring words that are predictive for the sentiment. The learned model is then applied to a test set to validate the model. 108 pp. Englisch. Nº de ref. de la librería 9783659295171

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Descripción LAP LAMBERT Academic Publishing, 2012. Paperback. Estado de conservación: New. book. Nº de ref. de la librería M3659295175

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