Sentiment Analysis of Multilingual Twitter Data using Natural Language Processing

Goel, Vinod; Gupta, Amit; Kumar, Narendra

doi:10.1109/csnt.2018.8820254

Cited by 23 publications

(13 citation statements)

References 5 publications

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“…The reported results make evident that the authors agree that sentiments can be extracted, processed and classified, using a wide variety of algorithms and they recognize that the lexicon-based approach, standard machine-based approach, and deep learning-based approach are the three main approaches in SA. In most of the publications summarized in Table 2, a comparison is made between algorithms, methods and models to be applied for SA; highlighting, in this sense, the application of Artificial Neural Networks (ANN) (Batrinca & Treleaven, 2015); Recurrent Neural Network (RNN) (Goel et al, 2018), (Khan & Malviya, 2020); Single-layered Long-Short Term Memory (LSTM) (Moshkin et al, 2019), (Chintalapudi et al, 2021), (Mujahid et al, 2021), (Velampalli et al, 2022); and Neural Network (NN) (Mostafa et al, 2021), (Khasanova & Pasechnik, 2021), (Velampalli et al, 2022). Term presence versus term frequency, N-gram features, parts of speech tagging (Mejova & Srinivasan, 2011).…”

Section: Resultsmentioning

confidence: 99%

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Pérez-Pérez

2023

CIERMMI Women in Science Engineering Sciences and Applications

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Sentiment analysis is a field of study within artificial intelligence aimed at comprehending opinions and emotions expressed in natural language, such as texts published on social networks. Social networks are understood as online technologies, tools, and applications that allow users to generate content, share and exchange information, and create interpersonal and communal relationships through the Internet

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Section: Resultsmentioning

confidence: 99%

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Pérez-Pérez

2023

CIERMMI Women in Science Engineering Sciences and Applications

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“…Extrасt reviews by age and sex which have been commented on by patients, as well as the recognition of sentiment words in each review. After establishing the орiniоn wоrd, the next aim is to decide the оrientаtiоn of the орiniоn wоrd to ascertain if the reviews are pleasant or unpleasant and generate a synopsis [4][5][6].…”

Section: Literature Survey and Limitationsmentioning

confidence: 99%

Extraction of Drug Review Polarity Using Sentimental Analysis

Marappan¹,

Bhaskaran²,

Ashwadh³

et al. 2022

JAMC

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The social networking site and user sites contain a large amount of information with users' feelings and opinions in various fields. For example, pharmaceutical companies provide users with text reviews and drug numeric ratings. Anyhow, these text-oriented reviews may not always be consistent with numerical values. In this project, we use different sentiment analysis models to differentiate drug user rating emotions and compare their accuracy. Various machine learning models including Logistic Regression, XG boost, and Naïve Bayes classifiers are being implemented by pushing it with drug reviews as inputs. In this, the XG boost model performed much better than other models with a total accuracy of 79.7%. This study has shown that these classification models can be used to separate drug reviews and identify the overall polarity or sentiment of the consumers. Since the main focus of this study was to separate the text reviews, the data were segregated according to each polarity. The division of binary classification was chosen instead of multiple classes as the purpose of this project was to identify the best and worst points/polarity. With the help of this polarity, it helps the nutritionist and the doctor to get updated with the in-market polarity of a particular drug and its public response.

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“…The approach consolidates sentiment analysis utilising administered learning and volume-based measures. Finally, an approach to the multilingual sentiment analysis dataset [28] and solution for multilingual sentiment analysis was proposed by executing calculations and getting the outcome. They contrast the exactness factor to locate the best solution for multilingual wistful examination.…”

Section: Literature Reviewmentioning

confidence: 99%

Multilingual Sentiment Mining System to Prognosticate Governance

Zahedi¹,

Alajlan²,

Zahedi³

et al. 2022

Computers, Materials &Amp; Continua

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In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python library that classifies text sentiment. Training data contained tweets in three languages: English: 200k, Urdu: 200k and Roman Urdu: 11k. Five different classification models are applied to determine sentiments, and eventually, the use of ensemble technique to move forward with the acquired results is explored. The Logistic Regression model performed best with an accuracy of 75%, followed by the Linear Support Vector classifier and Stochastic Gradient Descent model, both having 74% accuracy. Lastly, Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73% accuracy.

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Sentiment Analysis of Multilingual Twitter Data using Natural Language Processing

Cited by 23 publications

References 5 publications

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Machine learning for sentiment analysis in social networks data: Advances and perspectives

Extraction of Drug Review Polarity Using Sentimental Analysis

Multilingual Sentiment Mining System to Prognosticate Governance

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