Predicting consumer sentiments from online text

Bai, Xue

doi:10.1016/j.dss.2010.08.024

Cited by 203 publications

(60 citation statements)

References 9 publications

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“…Las opiniones son esenciales para el futuro de un diario, producto o servicio, ya que generan confianza (Wright, 2009). Ello justifica la creación de programas para el análisis de sentimiento, de forma que la información que otrora necesitaba encuestas y grupos focales está disponible gratuitamente en Internet (Bai, 2011;Mostafa, 2013). Las herramientas de análisis de sentimiento permiten identificar el objeto de discusión y clasificar opiniones según puntos de vista diferentes (Jacobson, 2009) y tres categorías posibles de comentario: positivo, negativo y neutral (Wijnhoven y Bloemen, 2013).…”

Section: Análisis De Sentimientounclassified

Algoritmos sobre el impacto de los medios de comunicación en medios sociales: estado de la cuestión

Gascón

2017

icono14

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La relación entre medios de comunicación y los medios sociales está acentuándose en plena Sociedad de la Banda Ancha. Mientras los mass media se plantean la difusión de los contenidos en redes sociales como una obligación, los algoritmos y las métricas de análisis en estas redes se multiplican para objetivizar el rendimiento que la empresa informativa puede obtener de su actividad virtual. En este artículo de innovación teórica se exponen las métricas utilizadas en función el objetivo a conseguir, y se incide en el análisis de sentimiento, que permite conocer si la reacción del lector a las unidades de contenido de los medios de comunicación es positiva, neutra o negativa. Se concluye que la medición de la polaridad de los comentarios del lector ayuda a la empresa informativa a mejorar la experiencia del receptor gracias sobre todo a la interactividad.

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Section: Análisis De Sentimientounclassified

Algoritmos sobre el impacto de los medios de comunicación en medios sociales: estado de la cuestión

Gascón

2017

icono14

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“…This vector is used to calculate weights for each feature that can be combined to determine the most likely label for feature set. [5], [6] Support vector machine is non probabilistic approach which is used to separate data linearly and nonlinearly. It determines the separators in search space which can best separate the classes.…”

Section: ) Supervised Learningmentioning

confidence: 99%

Sentiment Analysis Tools and Techniques: A Comprehensive Survey

Barretto¹

2017

IJRASET

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Data analysis refers to a process of inspecting and transforming data in order to derive some useful information. The objective of this process is to discover interesting patterns that can be used to derive important conclusions in areas like business, science, and social science domains. Semantic analysis plays a very important role in decision making process. Due to increase in web technologies a large amount of data gets generated. In order to extract important data from this, various data mining techniques have been proposed in recent years. Many researchers have focused on finding some interesting patterns out of this data using semantic analysis techniques. These techniques basically deal with determining the contextual polarity with reference to a specific domain. The paper presents a comprehensive survey about various techniques and tools used for sentiment analysis with some newer approaches like sentiment analysis using LDA. We present a comprehensive survey of latest sentiment analysis techniques along with some comparison results.

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“…Due to the strategic importance of sentiment classification, the literature is abundant of many studies that propose various algorithms for sentiment classification to improve its accuracy particularly in business and management research domains (Bai 2011;Duric and Song 2012;Fersini et al 2014;Kontopoulos et al 2013;Sobkowicz et al 2012), computer science (Denecke 2008;Melville et al 2009;Prabowo and Thelwall 2009;Hung and Lin 2013), and computational linguistics (Mullen and Collier 2004;Pang and Lee 2004;Aue and Gamon 2005;Okanohara and Tsujii 2005;Davidov et al 2010;Liu and Yu 2014) among others. With more sophisticated machine learning algorithms or auxiliary resources for word polarity, researchers tried to make an improvement in accuracy.…”

Section: Introductionmentioning

confidence: 99%

Data properties and the performance of sentiment classification for electronic commerce applications

Choi

Lee

2017

Inf Syst Front

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Sentiment classification has played an important role in various research area including e-commerce applications and a number of advanced Computational Intelligence techniques including machine learning and computational linguistics have been proposed in the literature for improved sentiment classification results. While such studies focus on improving performance with new techniques or extending existing algorithms based on previously used dataset, few studies provide practitioners with insight on what techniques are better for their datasets that have different properties. This paper applies four different sentiment classification techniques from machine learning (Naïve Bayes, SVM and Decision Tree) and sentiment orientation approaches to datasets obtained from various sources (IMDB, Twitter, Hotel review, and Amazon review datasets) to learn how different data properties including dataset size, length of target documents, and subjectivity of data affect the performance of those techniques. The results of computational experiments confirm the sensitivity of the techniques on data properties including training data size, the document length and subjectivity of training /test data in the improvement of performances of techniques. The theoretical and practical implications of the findings are discussed.

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Predicting consumer sentiments from online text

Cited by 203 publications

References 9 publications

Algoritmos sobre el impacto de los medios de comunicación en medios sociales: estado de la cuestión

Algoritmos sobre el impacto de los medios de comunicación en medios sociales: estado de la cuestión

Sentiment Analysis Tools and Techniques: A Comprehensive Survey

Data properties and the performance of sentiment classification for electronic commerce applications

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