RIFT: A Rule Induction Framework for Twitter Sentiment Analysis

Asghar, Muhammad Zubair; Khan, Amin; Khan, Furqan; Kundi, Fazal Masud

doi:10.1007/s13369-017-2770-1

Cited by 42 publications

(23 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The following preprocessing steps on mbti_kaggle dataset are applied before classification, acquired from the [37] work. a) Tokenization: Tokenization is the procedure where words are divided into the small fractions of text.…”

Section: ) Preprocessingmentioning

confidence: 99%

Personality Classification from Online Text using Machine Learning Approach

Khan¹,

Ahmad²,

Asghar³

et al. 2020

IJACSA

Self Cite

View full text Add to dashboard Cite

Personality refer to the distinctive set of characteristics of a person that effect their habits, behaviour's, attitude and pattern of thoughts. Text available on Social Networking sites provide an opportunity to recognize individual's personality traits automatically. In this proposed work, Machine Learning Technique, XGBoost classifier is used to predict four personality traits based on Myers-Briggs Type Indicator (MBTI) model, namely Introversion-Extroversion(I-E), iNtuition-Sensing(N-S), Feeling-Thinking(F-T) and Judging-Perceiving(J-P) from input text. Publically available benchmark dataset from Kaggle is used in experiments. The skewness of the dataset is the main issue associated with the prior work, which is minimized by applying Re-sampling technique namely random over-sampling, resulting in better performance. For more exploration of the personality from text, pre-processing techniques including tokenization, word stemming, stop words elimination and feature selection using TF IDF are also exploited. This work provides the basis for developing a personality identification system which could assist organization for recruiting and selecting appropriate personnel and to improve their business by knowing the personality and preferences of their customers. The results obtained by all classifiers across all personality traits is good enough, however, the performance of XGBoost classifier is outstanding by achieving more than 99% precision and accuracy for different traits.

show abstract

Section: ) Preprocessingmentioning

confidence: 99%

Personality Classification from Online Text using Machine Learning Approach

Khan¹,

Ahmad²,

Asghar³

et al. 2020

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…We applied different preprocessing techniques, such as tokenization, stop word removal, case conversion, and special symbol removal [30]. The tokenization yields a set of unique tokens (356,242), which assist in building a vocabulary from the training set, used for encoding the text.…”

Section: Preprocessingmentioning

confidence: 99%

Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Ahmad

Asghar

Alotaibi

et al. 2019

Hum. Cent. Comput. Inf. Sci.

Self Cite

View full text Add to dashboard Cite

With the tremendous increase in the use of social network sites like Twitter and Facebook, online community is exchanging information in the form of opinions, sentiments, emotions, and intentions, which reflect their affiliations and aptitude towards an entity, event and policy [1-3]. The propagation of extremist content has also been increasing and being considered as a serious issue in the recent era due to the rise of militant groups such as Irish Republican Army, Revolutionary Armed Forces of Colombia (FARC), Al Quaeda, ISIS (Daesh), Al Shabaab, Taliban, Hezbollah and others [4]. These groups have spread their roots not only at the community levels but also their networks are gaining control of social networking sites [5]. These networking sites are vulnerable and approachable platforms for the group strengthening, propaganda, brainwashing, and fundraising due to its massive impact on public sentiments and opinions. Opinions expressed on such sites give an important clue about the activities and behavior of online users. Detection of such extremist content is important to analyze user sentiment towards some extremist group and to discourage such associated unlawful acts. It is also beneficial in terms of classifying user's extremist affiliation by filtering tweets prior to their onward transmission, recommendation or training AI Chatbot from tweets [6].

show abstract

“…The supervised machine learning algorithms`are based on training and testing datasets. The training dataset is used to train the classifier and testing dataset is used to test the prediction capability of the trained classifier [5]. There are different supervised machine learning algorithms, such SVM, NB, KNN, Logistic Regression etc.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…Aforementioned problem can be solved by using a supervised learning-based subjectivity classification approach. Different machine learning classifiers, such as Support Vector Machine Naïve Bayes (N.B), K-Nearest Neighbor (KNN) and others can be used for the efficient classification of text [5]. In this work, a supervised learning-based technique using SVM, is proposed for the efficient classification of text as subjective or objective.…”

Section: Introductionmentioning

confidence: 99%

Performing Subjectivity Classification in Text Using Support Vector Machine

Almagrabi

2018

VAWKUM trans. comput. sci.

View full text Add to dashboard Cite

In this paper, I address the problem of the subjectivity classification in text. The subjective text is opinion bearing, whereas the objective text is text without expressing opinions. The supervised learning technique namely, Support Vector Machine (SVM) is used to classify the text as subjective and objective. A publically available dataset of drug reviews is used to conduct the experiments using WEKA platform. The experimental results show that the proposed SVM classifier performed better than the other classifiers.

show abstract

RIFT: A Rule Induction Framework for Twitter Sentiment Analysis

Cited by 42 publications

References 18 publications

Personality Classification from Online Text using Machine Learning Approach

Personality Classification from Online Text using Machine Learning Approach

Detection and classification of social media-based extremist affiliations using sentiment analysis techniques

Performing Subjectivity Classification in Text Using Support Vector Machine

Contact Info

Product

Resources

About