The Essential of Sentiment Analysis and Opinion Mining in Social Media : Introduction and Survey of the Recent Approaches and Techniques

Soong, Hoong-Cheng; Jalil, Norazira Binti A; Ayyasamy, Ramesh Kumar; Akbar, Rehan

doi:10.1109/iscaie.2019.8743799

Cited by 53 publications

(29 citation statements)

References 44 publications

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“…The focus is on particular geographical areas and hence the same methodology may not be applicable for other geographical areas [5]. For stress detection, researchers also have proposed text sentiment analysis general framework which includes various stages for opinion mining [6]. In this, after data collection and acquisition phase, tokenization is performed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Different algorithms such as Decision Tree, KNN, SVM, Naïve Bayes, Random Forest, ANN are discussed in the context of sentiment analysis. It provides a taxonomy of sentiment analysis tasks and introduces to different levels of sentiment analysis and its techniques [6]. Though the proposed framework is generalized, it is only compatible with machine learning methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, it is not able to provide different frameworks required for other methods. Also, it is found that different approaches and algorithms have direct influences on the overall classification [6]. Analysts have also proposed a simple tree stage framework [7].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

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%

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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“…Sentiment analysis tasks involve the detection of emotions, their classification (binary or multi-class), and the mining of opinion polarity and subjectivity [2]. Argument extraction and opinion summarization can also be applied in order to describe public opinion on a more detailed level.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

Alexandridis

Varlamis

Korovesis³

et al. 2021

Information

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As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community.

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“…Then, we will add those phrases and short sentences into the suspicious word list and test the extension again. We will also employ sentiment analysis, which is the field of study that examines human opinions and sentiments towards different entities [13], to find the neutral, positive, and negative polarities in the sentences. According to those polarities, we can improve the detecting function by weighing up the segments that have negative polarities.…”

Section: 𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑟𝑎𝑡𝑒 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒𝑠 + 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒𝑠mentioning

confidence: 99%

Developing a Google Chrome Extension for Detecting Phishing Emails

Chen¹,

Hossain

EPiC Series in Computing

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Phishing is a fraudulent attempt where attackers trick the victims into disclosing sensitive information under pretenses. This research project aims to develop a Google Chrome extension to detect phishing emails. We firstly collected a number of phishing email samples. Then we used text mining techniques to find out the words that are important in phishing emails. Next, we developed a classifier model that the Chrome extension will use to detect phishing emails using those words. The next step was to test the extension with phishing email samples and standard (non-phishing) email samples. After that, the evaluation metrics were collected. We found our extension was able to identify phishing emails and non-phishing emails with a relatively high degree of accuracy.

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The Essential of Sentiment Analysis and Opinion Mining in Social Media : Introduction and Survey of the Recent Approaches and Techniques

Cited by 53 publications

References 44 publications

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

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

A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

Developing a Google Chrome Extension for Detecting Phishing Emails

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