Analyzing and Preprocessing the Twitter Data for Opinion Mining

Anand, Neetu; Goyal, Dhruvi; Kumar, T. Ganesh

doi:10.1007/978-981-10-8198-9_22

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…The common thing in many of the recent studies regarding sentiment analysis is the use of Twitter as the primary source of data [23] [24] [25]. It is owing to the fact that Twitter is a universal microblogging website and it allows the users to express their thoughts in limited characters which makes the preprocessing part easy for the researchers [26]. However, as discussed in Section I, machine learning-based sentiment classification for tweets encounters three problems namely, high sparsity, high-dimensional feature vectors and highly skewed classes.…”

Section: Related Workmentioning

confidence: 99%

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

et al. 2021

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Sentiment Analysis is a technique that is being used abundantly nowadays for customer reviews analysis, popularity analysis of electoral candidates, hate speech detection and similar applications. Sentiment analysis on tweets encounters challenges such as highly skewed classes, high dimensional feature vectors and highly sparse data. In this study, we have analyzed the improvement achieved by successively addressing these problems in order to determine their severity for sentiment analysis of tweets. Firstly, we prepared a comprehensive data set consisting of Urdu Tweets for sentiment analysis-based hate speech detection. To improve the performance of the sentiment classifier, we employed dynamic stop words filtering, Variable Global Feature Selection Scheme (VGFSS) and Synthetic Minority Optimization Technique (SMOTE) to handle the sparsity, dimensionality and class imbalance problems respectively. We used two machine learning algorithms i.e., Support Vector Machines (SVM) and Multinomial Naïve Bayes' (MNB) for investigating performance in our experiments. Our results show that addressing class skew along with alleviating the high dimensionality problem brings about the maximum improvement in the overall performance of the sentiment analysis-based hate speech detection.

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Section: Related Workmentioning

confidence: 99%

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

et al. 2021

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“…Some manually annotated tweets are shown below in. Preprocessing and Feature Extraction: The data collected from social media contains a lot of noise (25)(26)(27)(28). The data needs to be filtered to remove the noise and prepare it for machine classification before the machine processes the data.…”

Section: Tweet Length and Distributionmentioning

confidence: 99%

“…Thus, out of 4797, a total of 531 tweets were removed, leaving 4266 tweets for classification. The text is pre-processed by using various promising standard techniques of text mining (27,31). After removing the URLs, non-ASCII characters, the text was tokenized and stemmed using Tokenizer and Stemmer available in Weka Tool.…”

Section: Tweet Length and Distributionmentioning

confidence: 99%

Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches

2020

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Suicidal ideation is one of the most severe mental health issues faced by people all over the world. There are various risk factors involved that can lead to suicide. The most common & critical risk factors among them are depression, anxiety, social isolation and hopelessness. Early detection of these risk factors can help in preventing or reducing the number of suicides. Online social networking platforms like Twitter, Redditt and Facebook are becoming a new way for the people to express themselves freely without worrying about social stigma. This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim of this unfortunate mental disorder. The data is collected from Twitter, one of the popular Social Networking Sites (SNS). The Tweets are then pre-processed and annotated manually. Finally, various machine learning and ensemble methods are used to automatically distinguish Suicidal and Non-Suicidal tweets. This experimental study will help the researchers to know and understand how SNS are used by the people to express their distress related feelings and emotions. The study further confirmed that it is possible to analyse and differentiate these tweets using human coding and then replicate the accuracy by machine classification. However, the power of prediction for detecting genuine suicidality is not confirmed yet, and this study does not directly communicate and intervene the people having suicidal behaviour.

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Segregation and User Interactive Visualization of Covid-19 Tweets Using Text Mining Techniques

Chaudhary

Kshirsagar

2021

Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (IC

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Analyzing and Preprocessing the Twitter Data for Opinion Mining

Cited by 6 publications

References 8 publications

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

Improving Hate Speech Detection of Urdu Tweets Using Sentiment Analysis

Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches

Segregation and User Interactive Visualization of Covid-19 Tweets Using Text Mining Techniques

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