Lexicon Ensemble and Lexicon Pooling for Sentiment Polarity Detection

Devaraj, Madhavi; Piryani, Rajesh; Singh, Vivek Kumar

doi:10.1080/02564602.2015.1073572

Cited by 12 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Researchers use this method less commonly to make a prediction. The primary advantage is that it is scalable in comparison to other algorithms [13].…”

Section: Neural Networkmentioning

confidence: 99%

“…For its ability to work in a time-efficient manner and to disregard noise or irrelevant data, and its simplicity to implementation, Naïve Bayes Classifier is a viral algorithm for test classification in the fields like spam or fake news detection, sentiment analysis, etc. This algorithm is based on a theorem known as Bayes Theorem, first invented by a British scientist named Thomas Bayes [26]. The idea of the Bayes Theorem is to calculate the probability of an event based on any previous knowledge or conditions that have an impact on the event.…”

Section: ) Naïve Bayes Classifiermentioning

confidence: 99%

See 1 more Smart Citation

Drug Sentiment Analysis using Machine Learning Classifiers

Uddin¹,

Hafiz²,

Hossain³

et al. 2022

IJACSA

View full text Add to dashboard Cite

In recent times, one of the most emerging subdimensions of natural language processing is sentiment analysis which refers to analyzing opinion on a particular subject from plain text. Drug sentiment analysis has become very significant in present times as classifying medicines based on their effectiveness through analyzing reviews from users can assist potential future consumers in gaining knowledge and making better decisions about a particular drug. The objective of this proposed research is to measure the effectiveness level of a particular drug. Currently most of the text mining researches are based on unsupervised machine learning methods to cluster data. When supervised learning methods are used for text mining, the usual primary concern is to classify the data into two classes. Lack of technical terms in similar datasets make the categorization even more challenging. The proposed research focuses on finding out the keywords through tokenization and lemmatization so that better accuracy can be achieved for categorizing the drugs based on their effectiveness using different algorithms. Such categorization can be instrumental for treating illness as well as improve one's health and well-being. Four machine learning algorithms have been applied for binary classification and one for multiclass classification on the drug review dataset acquired from the UCI machine learning repository. The machine learning algorithms used for binary classification are naive Bayes classifier, random forest, support vector classifier (SVC), and multilayer perceptron; among these machine learning algorithms, linear SVC was used for multiclass classification. Results obtained from these four classifier algorithms have been analyzed to evaluate their performances. The random forest has been proven to have the best performance among these four algorithms. However, multiclass classification was found to have low performance when applied to natural language processing. On the contrary, the applied linear SVC algorithm performed better for class 2 with AUC 0.82 in this research.

show abstract

“…Researchers use this method less commonly to make a prediction. The primary advantage is that it is scalable in comparison to other algorithms [13].…”

Section: Neural Networkmentioning

confidence: 99%

Section: ) Naïve Bayes Classifiermentioning

confidence: 99%

Drug Sentiment Analysis using Machine Learning Classifiers

Uddin¹,

Hafiz²,

Hossain³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Khan et al [14] present an entity-level sentiment analysis method by combining a lexicon and learning-based method to perform sentiment analysis on Twitter data. Devaraj et al [15] detect sentiment polarity of free-form texts by using lexicon ensemble and lexicon pooling. Khan et al [16] build a sentiment dictionary SentiMI by extracting sentiment terms from SentiWordNet and develop a complete framework using feature selection and extracting mutual information from SentiWordNet to improve sentiment polarity detection.…”

Section: Sentiment Polarity Detectionmentioning

confidence: 99%

Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations

Wang

Zhong

Yang

et al. 2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Review rating prediction is an important problem in machine learning and data mining areas and has attracted much attention in recent years. Most existing methods for review rating prediction on Location-Based Social Networks only capture the semantics of texts, but ignore user information (social links, geolocations, etc.), which makes them less personalized and brings down the prediction accuracy. For example, a user's visit to a venue may be influenced by their friends' suggestions or the travel distance to the venue. To address this problem, we develop a review rating prediction framework named TSG by utilizing users' review Text, Social links and the Geolocation information with machine learning techniques. Experimental results demonstrate the effectiveness of the framework.

show abstract

“…Adicionalmente, se han propuesto métodos que involucran recursos léxicos y métodos de aprendizaje supervisado. Por ejemplo, Madhavi D. et al [6] trabajaron con dos enfoques: el primero es la construcción de clasificadores con ensambles que obtienen de la combinación de tres léxicos de cuatro que usan (SentiWordNet, Bing Liu Lexicon, SenticNet, MPQA), haciendo un clasificador por cada léxico, además uno por cada ensamble; el segundo enfoque mencionado consiste en aunar el conocimiento que proporcionan los recursos léxicos a un clasificador. Reportan no haber encontrado una mejora en usar ensambles a usar los léxicos originales, sin embargo, se notó una mejoría al hacer uso del conocimiento proporcionado por los léxicos.…”

Section: Trabajo Relacionadounclassified

“…En este artículo utilizamos un conjunto de recursos léxicos tanto para el idioma Inglés como para el Español que han sido usados previamente en la literatura [6,10]. En la tabla 1 se muestran cinco recursos léxicos con el número de palabras asociada a cada sentimiento.…”

Section: Recursos Léxicos Para El Análisis De Sentimientosunclassified

Un método para el análisis de sentimientos bajo un enfoque supervisado usando recursos léxicos

Ambrocio¹,

Rosa²,

Tello³

2018

RCS

View full text Add to dashboard Cite

Resumen. La tarea de análisis de sentimientos ha sido un tema de interés desde hace algunos años. Conocer si un texto es positivo o negativo es relevante para tener una opinión general sobre diversas entidades como: productos, personajes públicos, y empresas. En este artículo presentamos un método de clasificación supervisada que toma ventaja de recursos léxicos existentes en español e inglés. Los experimentos realizados están orientados a contestar en qué medida la información externa de recursos léxicos, esútil bajo un enfoque de clasificación supervisada. Los resultados obtenidos muestran que el método propuesto es estable para ambos idiomas y diferentes recursos léxicos empleados.

show abstract

Lexicon Ensemble and Lexicon Pooling for Sentiment Polarity Detection

Cited by 12 publications

References 12 publications

Drug Sentiment Analysis using Machine Learning Classifiers

Drug Sentiment Analysis using Machine Learning Classifiers

Review Rating Prediction on Location-Based Social Networks Using Text, Social Links, and Geolocations

Un método para el análisis de sentimientos bajo un enfoque supervisado usando recursos léxicos

Contact Info

Product

Resources

About