Sentiment Analysis of Tweets Using Machine Learning Approach

Rathi, Megha; Malik, Aditya; Varshney, Daksh; Sharma, Rachita; Mendiratta, Sarthak

doi:10.1109/ic3.2018.8530517

Cited by 102 publications

(36 citation statements)

References 3 publications

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“…This is because these platforms are the most frequently used platforms. Preprocessing includes classification of a dataset into a training dataset and testing dataset to carry out tokenization further [2]. Next to it, pre-processing of tweets is done which includes removing handles, removing URLs, timings of tweets, #hashtag, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

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Stress Detection Methodology based on Social Media Network: A Proposed Design

Chaware*¹,

Makashir²,

Athavale³

et al. 2020

IJITEE

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Mental disorders can be recognized by how a person behaves, feels, perceives, or thinks over a period of a lifetime. Nowadays, a large number of people are feeling stressed with the rapid pace of life. Stress and depression may lead to mental disorders. Work pressure, working environment, people we interact, schedule of the day, food habits, etc. are some of the major reasons behind building stress among the people. Thus, stress can be detected through some conventional medical symptoms such as headache, rapid heartbeats, feeling low energy, chest pain, frequent colds, infections, etc. The stress also may reflect in normal behavior while carrying out day-to-day activities. Individuals may share their day-to-day activities and interact with friends on social media. Thus, it may be possible to detect stress through social network data. There are many ways to detect stress levels. Some of the instruments are used to detect stress while there is a medical test to know the stress level. Also, there are apps that analyze the behavior of the person to detect stress. Many researchers had tried to use machine learning techniques including the use of various algorithms such as Decision Tree, Naïve Bayes, Random Forest, etc. which gives a lower accuracy of 70% on average. In this paper, we are using a closeness of stress levels with social media data shared by many users. In our proposed system design, Facebook posts are being accessed using a token. Further, we recommend the use of machine learning algorithms such as Conventional Neural Network (CNN) to extract Facebook posts, Transductive Support Vector Machine (TSVM) to classify posts and K-Nearest Neighbors (KNN) to recommend nearby hospitals. With the help of these algorithms, we predict the stress level of the person as positive, negative. Thus, we are expecting more accuracy to detect the stress along with the preventive recommendation. We have proposed a methodology to detect stress because severe stress may lead to self-harming activities and also it may affect the lives of people around us. Thus, stress detection has become extremely important and we are expecting that our proposed model may detect it with more accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

“…Next to it, pre-processing of tweets is done which includes removing handles, removing URLs, timings of tweets, #hashtag, etc. [2]. Support Vector Machine (SVM) and Decision tree algorithms are implemented to obtain positive or negative results.…”

Section: Literature Reviewmentioning

confidence: 99%

Stress Detection Methodology based on Social Media Network: A Proposed Design

Chaware*¹,

Makashir²,

Athavale³

et al. 2020

IJITEE

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“…Machine learning techniques can be utilized to reveal valuable knowledge which is hidden in such noisy social media data created on daily basis [15]. There are numerous techniques of machine learning that support learning, such as K-Nearest Neighbors (KNN) [16], Support Vector Machines (SVM) [17], ID3 decision tree [18], Logistic Regression (LR) [19], Naive Bayesian (NB) [20], or Random FIGURE 1. Basic steps of opinion mining on social network platforms.…”

Section: Introductionmentioning

confidence: 99%

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Es-sabery

Qadir

et al. 2021

IEEE Access

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Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text. With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers. Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process. The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset. In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF. Secondly, we have applied a multiple feature selector to reduce the high feature's dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index. Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3. In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature. Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature. This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS. Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets. We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature. The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49 %), execution time (15.95s), kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).

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“…A classificação de polaridade,é um problema de classificação simples de texto, onde as classes são relativas ao sentimento que está sendo expresso no texto. Entre os classificadores mais utilizados na literatura para essa tarefa, podemos citar o Naive Bayes [Zuo 2018], Máquinas de Vetores de Suporte [Lu andWu 2019, Guan et al 2018], Regressão Logística [Al Omari et al 2019, Ramadhan et al 2017, Arvores de Decisão e Floresta Aleatória [Rathi et al 2018, Rane and Kumar 2018, Hegde and Padma 2017. A classificação de polaridade tambémé um dos temas mais abordados no português, como em [de Aguiar et al 2018, Souza andVieira 2012], onde algoritmos de aprendizagem de máquina foram utilizados para classificar os sentimentos contidos em postagens feitas por usuários de redes sociais, como o Twitter 1 .…”

Section: Introductionunclassified

Uma Abordagem de Classificação de Sentimentos em Revisões de Livros em Português Brasileiro Usando Diferentes Métodos de Extração de Características

Britto¹,

Pacífico²

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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A enorme quantidade de dados textuais disponibilizados todos os dias na internet incentivou a pesquisa em diversas áreas que processam e analisam automaticamente esses textos. Uma das áreas mais populares é Análise de Sentimentos, que apesar de ter sido um tópico amplamente discutido nos últimos anos, ainda enfrenta uma escassez de recursos disponíveis para o idioma português brasileiro. Este trabalho apresenta o processo completo de análise e classificação de sentimentos, desde o desenvolvimento de uma base de dados em português (no domínio de livros) até a classificação de sentimentos utilizando alguns dos principais classificadores da literatura e diferentes métodos de extração de características.

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Sentiment Analysis of Tweets Using Machine Learning Approach

Cited by 102 publications

References 3 publications

Stress Detection Methodology based on Social Media Network: A Proposed Design

Stress Detection Methodology based on Social Media Network: A Proposed Design

A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Uma Abordagem de Classificação de Sentimentos em Revisões de Livros em Português Brasileiro Usando Diferentes Métodos de Extração de Características

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