Abstract

With the extensive availability of social media platforms, Twitter has become a significant tool for the acquisition of peoples’ views, opinions, attitudes, and emotions towards certain entities. Within this frame of reference, sentiment analysis of tweets has become one of the most fascinating research areas in the field of natural language processing. A variety of techniques have been devised for sentiment analysis, but there is still room for improvement where the accuracy and efficacy of the system are concerned. This study proposes a novel approach that exploits the advantages of the lexical dictionary, machine learning, and deep learning classifiers. We classified the tweets based on the sentiments extracted by TextBlob using a stacked ensemble of three long short-term memory (LSTM) as base classifiers and logistic regression (LR) as a meta classifier. The proposed model proved to be effective and time-saving since it does not require feature extraction, as LSTM extracts features without any human intervention. We also compared our proposed approach with conventional machine learning models such as logistic regression, AdaBoost, and random forest. We also included state-of-the-art deep learning models in comparison with the proposed model. Experiments were conducted on the sentiment140 dataset and were evaluated in terms of accuracy, precision, recall, and F1 Score. Empirical results showed that our proposed approach manifested state-of-the-art results by achieving an accuracy score of 99%.

Highlights

  • Advances in internet technology and the continuous development of web 2.0 is resulting in the production of a substantial amount of data daily

  • This shows that TextBlob sentiments are in more correlation with the feature set extracted by term frequency-inverse document frequency (TF-inverse document frequency (IDF))

  • It can be observed that logistic regression (LR) performed well with TextBlob sentiments when trained on features extracted by term frequency (TF)-IDF

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Summary

Introduction

Advances in internet technology and the continuous development of web 2.0 is resulting in the production of a substantial amount of data daily. One of the fast-growing and impactful social media networks is Twitter, on which users can read, post, and update short text messages termed as ‘tweets’ which enable Twitter users to communicate their views, opinions, and sentiments about a particular entity. These sentiment-bearing tweets play a vital role in many areas, for instance, social media marketing [6], academics [7], and election campaign news [6]

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