A Comparative Sentiment Analysis Of Sentence Embedding Using Machine Learning Techniques

Poornima, A. S.; Kumari, Priya

doi:10.1109/icaccs48705.2020.9074312

Cited by 65 publications

(25 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After having the data processed, text mining is performed, for which SVM and Naive Bayes classification algorithms are implemented. These classifiers are very important, since they gave very encouraging results, leading the SVM that resulted in 98.7% accuracy, as shown in the results section, this percentage is very favourable since in some related works lower results are obtained as can be seen in the following researches [6], [10], and [20], these had results between 80% to 90% accuracy. The results shown in the graphs are made based on SVM and Naive Bayes algorithms, considering which of them obtained the highest percentages.…”

Section: Discussionmentioning

confidence: 79%

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

Rivera-Guamán

Cumbicus-Pineda

López-Lapo

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Nowadays, the use of social networks is part of the daily life of most people, especially young people, to share content and opinions. Twitter is one of the most popular social networks, in which users are the first actors to generate a large amount of information. The analysis of Twitter data requires a systematic process of collection, processing and classification. The main objective of this work is to classify the data tweets in three different classes: Positive, Negative and Neutral Opinions, corresponding to the "International Festival of the Living Arts in Loja (FIAVL)" in the years between 2016 and 2019. The official account of the "FIAVL" produced a total of 18k tweets in Spanish language, which followed the different phases of the Knowledge Discovery in Text (KDT) methodology for its analysis and study. Vector Support Machines (SVM) and Naive Bayes (NB) were used to classify the classes, where an accuracy rate of 98.7% was obtained, with the neutral opinion prevailing over the rest of the classes with 57%, thus it could be concluded that there are no positive or negative opinions about the FIAVL.

show abstract

Section: Discussionmentioning

confidence: 79%

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

Rivera-Guamán

Cumbicus-Pineda

López-Lapo

et al. 2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

show abstract

“…The algorithm is based on the concept of probability and uses a complex cost function called the Sigmoid function [14]. Logistic Regression can be binomial, ordinal or multinomial.…”

Section: Classificationmentioning

confidence: 99%

Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

Rahmanda

Setiawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

View full text Add to dashboard Cite

Customer opinion is an important aspect in determining the success of a company or service provider. By determining the sentiment of the existing opinion, the company can use it as an evaluation material to improve the quality of the service or product provided. Sentiment analysis can be used as a measure of opinion sentiment with input data in the form of a corpus which will be classified into positive or negative classes to obtain the level of customer satisfaction with a product or service. Aspect-based sentiment analysis can be used by companies to analyze more specifically and find out what aspects need to be improved. In this research, an aspect-based sentiment analysis was conducted on Telkomsel users on Twitter. The data used is 16,992 tweets from users who discuss several aspects such as Telkomsel's services and signals in Twitter. In this research Word2Vec was used for feature expansion to minimize vocabulary mismatch caused by limited words in tweets. The results showed that Word2Vec, Synthetic Minority Oversampling Technique (SMOTE), and Boosting algorithm combination with Logistic Regression classifier achieve highest accuracy of 95.10% for signal aspect and using hyperparameters makes the service aspect get the highest accuracy of 93.34%.

show abstract

“…Many sentiment analyses on Twitter data have been conducted but have been limited to traditional natural language processing and machine learning algorithms [36][37][38][39]. Automated sentiment analysis systems on Twitter data were presented by several groups [40][41][42].…”

Section: Related Workmentioning

confidence: 99%

A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports

et al. 2021

View full text Add to dashboard Cite

Annually, over three million people in North America suffer concussions. Every age group is susceptible to concussion, but youth involved in sporting activities are particularly vulnerable, with about 6% of all youth suffering a concussion annually. Youth who suffer concussion have also been shown to have higher rates of suicidal ideation, substance and alcohol use, and violent behaviors. A significant body of research over the last decade has led to changes in policies and laws intended to reduce the incidence and burden of concussions. However, it is also clear that youth engaging in high-risk activities like sport often underreport concussion, while others may embellish reports for specific purposes. For such policies and laws to work, they must operate effectively within a facilitative social context so understanding the culture around concussion becomes essential to reducing concussion and its consequences. We present an automated deep neural network approach to analyze tweets with sport-related concussion context to identify the general public’s sentiment towards concerns in sport-related concussion. A single-layer and multi-layer convolutional neural networks, Long Short-Term Memory (LSTM) networks, and Bidirectional LSTM were trained to classify the sentiments of the tweets. Afterwards, we train an ensemble model to aggregate the predictions of our networks to provide a final decision of the tweet’s sentiment. The system achieves an evaluation F1 score of 62.71% based on Precision and Recall. The trained system is then used to analyze the tweets in the FIFA World Cup 2018 to measure audience reaction to events involving concussion. The neural network system provides an understanding of the culture around concussion through sentiment analysis.

show abstract

A Comparative Sentiment Analysis Of Sentence Embedding Using Machine Learning Techniques

Cited by 65 publications

References 6 publications

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

Word2Vec on Sentiment Analysis with Synthetic Minority Oversampling Technique and Boosting Algorithm

A deep neural network approach for sentiment analysis of medically related texts: an analysis of tweets related to concussions in sports

Contact Info

Product

Resources

About