Sentiment Analysis on Twitter Data using a Hybrid Approach

Avinash Kumar; Savita Sharma; Dinesh Singh

doi:10.26438/ijcse/v7i5.906911

Sentiment Analysis on Twitter Data using a Hybrid Approach

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i5.906911 ◽

2019 ◽

Vol 7 (5) ◽

pp. 906-911

Author(s):

Avinash Kumar ◽

Savita Sharma ◽

Dinesh Singh

Keyword(s):

Sentiment Analysis ◽

Hybrid Approach ◽

Twitter Data

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A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters

Information Systems Frontiers ◽

10.1007/s10796-021-10107-x ◽

2021 ◽

Cited By ~ 1

Author(s):

Shalak Mendon ◽

Pankaj Dutta ◽

Abhishek Behl ◽

Stefan Lessmann

Keyword(s):

Machine Learning ◽

Natural Disasters ◽

Sentiment Analysis ◽

Hybrid Approach ◽

Twitter Data

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Multi-level aspect based sentiment classification of Twitter data: using hybrid approach in deep learning

PeerJ Computer Science ◽

10.7717/peerj-cs.433 ◽

2021 ◽

Vol 7 ◽

pp. e433

Author(s):

Sadaf Hussain Janjua ◽

Ghazanfar Farooq Siddiqui ◽

Muddassar Azam Sindhu ◽

Umer Rashid

Keyword(s):

Sentiment Analysis ◽

Text Classification ◽

Hybrid Approach ◽

Feature Selection Method ◽

Sentiment Classification ◽

Support Vector ◽

Machine Learning Classification ◽

Research Fields ◽

Twitter Data ◽

Multi Level

Social media is a vital source to produce textual data, further utilized in various research fields. It has been considered an essential foundation for organizations to get valuable data to assess the users’ thoughts and opinions on a specific topic. Text classification is a procedure to assign tags to predefined classes automatically based on their contents. The aspect-based sentiment analysis to classify the text is challenging. Every work related to sentiment analysis approached this issue as the current research usually discusses the document-level and overall sentence-level analysis rather than the particularities of the sentiments. This research aims to use Twitter data to perform a finer-grained sentiment analysis at aspect-level by considering explicit and implicit aspects. This study proposes a new Multi-level Hybrid Aspect-Based Sentiment Classification (MuLeHyABSC) approach by embedding a feature ranking process with an amendment of feature selection method for Twitter and sentiment classification comprising of Artificial Neural Network; Multi-Layer Perceptron (MLP) is used to attain improved results. In this study, different machine learning classification methods were also implemented, including Random Forest (RF), Support Vector Classifier (SVC), and seven more classifiers to compare with the proposed classification method. The implementation of the proposed hybrid method has shown better performance and the efficiency of the proposed system was validated on multiple Twitter datasets to manifest different domains. We achieved better results for all Twitter datasets used for the validation purpose of the proposed method with an accuracy of 78.99%, 84.09%, 80.38%, 82.37%, and 84.72%, respectively, compared to the baseline approaches. The proposed approach revealed that the new hybrid aspect-based text classification functionality is enhanced, and it outperformed the existing baseline methods for sentiment classification.

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